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Top 30 Python Libraries for Data Science in 2024

Updated on 25 October, 2024

13.52K+ views
37 min read

You might have seen different statistics on Python as one of the best learning languages. We are going to differ in our opinion here. Python is the best language to learn. The reason is that Python is closer to a human-interpretable language than machine-level languages like C++ or Java. It is an intuitive language that can be used across a wide range of applications. 

Now, what does the term packages or library even mean? Python is a plus and play language. The idea is that if you are looking to implement a simple or even a complex logic, it is likely that someone has already done it before. This logic is then put in a form that makes it reusable this is known as a package or a library (the terms are used interchangeably). So, why this blog? 

What are Python Libraries? 

The term "library" is used to collectively describe a reusable chunk of code. A python library consists of code that we can reuse while writing code for a given application. However, just to go a little bit in detail, a collection of modules is called a package, and a collection of packages is called a module. Now, a fundamental question comes to mind, when people are writing all this code, why would they build libraries for everyone to use? Let's understand a python module example. 

This is one of the reasons Python has grown to be one of the most widely used languages in the world. Besides its ease of use and wide applications, there is an extremely supportive community around Python with millions of possible solutions for any issues you face. Python can be used for applications such as backend, frontend, middleware, data science, machine learning, artificial intelligence, deep learning, and even something as simple as mathematics!

In the next section, let's understand why we should leverage the libraries that are available in Python. 

List of Python Libraries for Data Science in 2024

Now, we will go through different categories for the python modules list, ranging from Mathematics, data exploration and visualization, machine learning, data mining & data scraping, and natural language processing, and if you stick around till the end, we will also have bonus Python packages.  

Now, remember, through this python libraries list exploration, our aim is to explore python libraries for data science that can help you in the field of Data Science and Data Analytics. And data science starts with one main thing – math! Using the python library example, let's dive into the Python libraries for mathematics. 

A) Python Libraries for Math 

In this section, we will go over the python packages list we use for mathematics. 

1. NumPy 

Just like how we see the world in terms of visuals, smell, taste, and touch, machines see the world in terms of multi-dimensional arrays. As human beings, we can see and feel just 3 dimensions (X-Axis, Y-Axis, and Z-Axis). Machines can process and comprehend multiple dimensions, and this is represented by multi-dimensional arrays. 

a. Features: 

  • NumPy is an abbreviation of numerical Python and is a package that is used to work with multi-dimensional arrays. It is a fundamental package for scientific computing with Python. It is one of the #1 packages used by almost everyone in the Data Science community. 
  • NumPy has functions in the domain of matrices, Fourier transformation, and of course, linear algebra 
  • NumPy is 50 times faster than traditional Python lists! This is because NumPy stores all of its arrays in one continuous memory, plus it is optimized to work with the newer CPU architectures 
  • NumPy is primarily written in C and C++ to enable super-fast computation, as C & C++ is a machine-level languages.  

b. Pros 

  • Highly optimized: NumPy is a highly optimized package to perform scientific computation by working with numeric arrays, which makes it a fantastic tool for data scientists 
  • Efficient for use in popular packages; NumPy arrays are used as the input for many popular packages such as sci-kit-learn and TensorFlow 
  • The use of ndarray object: The array object, ndarray, provides a lot of supporting functions that make ndarray very efficient to use, such as elementwise addition and multiplication, the computation of Kronecker product, etc., which is not supported by Python lists 

c. Cons 

  • The use of NaN: NumPy supports the use of Nan, which stands for "Not a Number," which is supported by NumPy, but not by as many packages. This makes it difficult to interpret and work with the user. 
  • Requires a continuous allocation of memory: When there is continuous memory allocated contiguously, the allocation and de-allocation of memory via insertion and deletion of memory becomes costly as it requires shifting.  

d. Applications 

  • NumPy is leveraged to maintain minimal memory 
  • It is used as an alternative to arrays and lists in Python while working well for multi-dimensional arrays 
  • NumPy is used in cases where there is a requirement for faster runtime behavior 

2. SciPy   

SciPy is an open-source package used for scientific and technical computing. It has modules for integration, optimization, interpolation, linear algebra, eigenvalue, statistics, multi-dimensional image processing, etc. Fun fact – SciPy uses NumPy underneath.  

SciPy has utility functions for signal processing, stats, and optimization. 

a. Features 

  • Used in scientific computing and mathematics 
  • SciPy comes under the umbrella of the NumPy stack, which includes packages such as matplotlib and pandas. 
  • SciPy has a full set of functions for linear algebra, while NumPy has comparatively fewer functions for linear algebra 
  • SciPy has featured in the domain of: 
  • Integration 
  • Optimization 
  • Interpolation 
  • Fourier Transformation 
  • Signal Processing 
  • Linear Algebra 
  • Eigenvalues 
  • Multi-dimensional Image processing 

b. Pros 

  • SciPy has classes for efficient visualization and data manipulation 
  • There is better cross-functionality with other Python libraries 
  • SciPy has the option for parallel programming for certain database and web routines 
  • SciPy is quick and simple to pick up 

c. Cons 

  • The use of NaN: SciPy supports the use of Nan, which stands for "Not a Number," which is supported by NumPy, but not by as many packages. This makes it difficult to interpret and work with the user. 
  • It can be complex for someone with no mathematics background: SciPy is meant to be a tool that can aid scientific and mathematical exploration. However, if you do not have a fundamental knowledge of what you are looking to do, it may not be the best tool. 

d. Applications 

  • Mathematics! SciPy is used to perform tasks for research and scientific computation related to mathematical functions such as linear algebra, calculus, solving differential equations, and signal processing. 

3. Theano  

Theano is a python package built on top of NumPy to manipulate and evaluate mathematical expressions, specifically matrix-valued ones. 

a. Features 

  • Integration with NumPy: NumPy's ndarray objects are used by the Teano library as well 
  • Can calculate derivatives: Theano's class of libraries helps it to compute derivatives for one or more functions 
  • Dynamically generate C code: Theano can dynamically generate code in the programming language C to be able to evaluate expressions faster 

b. Pros 

  • Efficient GPU use: Theano can perform operations that are data-intensive up to 140 times faster than on a CPU by leveraging a GPU 
  • Reliable and fast: Theano has been known to be stable and efficient while calculating expressions for large values of x 
  • Self-tests: Theano has tools to enable self-verification and unit testing, which can help catch potential problems early on in the analysis lifecycle.  

c. Cons 

  • Newer, better versions now: Theano is considered to be the Godfather of machine learning libraries, specifically in the deep learning arena.  
  • Development stopped: The development of Theano stopped in late 2017. In fact, Google created Tensorflow to replace Theano 

d. Applications 

  • Computer Vision: Theano is used in computer vision, such as recognizing handwriting and sparse coding 
  • Deep Learning: Considered the Godfather of Python packages, Theano was one of the first packages to leverage GPU optimization

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B) Python Libraries for Data Exploration and Visualization 

Let's review some of the python libraries for data analysis, also taught in the Data Science Bootcamp

1. Pandas  

Arguably the most used package by Data Scientists all over the world. Pandas is a software library that works with data structures and provides functions for data manipulation and analysis. 

a. Features 

  • Pandas library is used  able to work with a large selection of IO tools such as CSV, JSON, SQL, BigQuery, and Excel files 
  • It has methods to perform functions such as object creation, viewing data, selection of data, analyzing missing data, operations such as merge, grouping, reshaping, time series, categorical values, and plotting 
  • Pandas have two main objects that it works with: Pandas Series and Dataframes 

b. Pros 

  • Simple representation of data: Python has the ability to take multiple types of data and condense the information into a simple data frame. This facilitates us to visualize and understand the data more efficiently. 
  • Powerful features: Any command that is needed to manipulate data can be found within the Pandas library. From filtering to grouping to segmenting, Pandas can do it all! 
  • Handles large datasets: One of the main reason pandas was built was to handle large data frames efficiently 

c. Cons 

  • Steep Learning Curve: Pandas have a steep learning curve, and users that are starting out with Pandas might take some time to get accustomed to the way that the Pandas library works 
  • Imperfect Documentation: Documentation is not the strong suit of pandas. This can perhaps be due to the sheer amount of capability of Pandas. However, if you know the application you are looking for, there are multiple use cases to refer to. 
  • Incompatibility with 3D matrices: One of the biggest drawbacks is Pandas' poor compatibility in handling 3D matrices. For applications that need to process multi-dimensional arrays, it is preferred to use packages such as NumPy. 

d. Applications 

  • Recommendation Systems: Websites like Netflix and Spotify leverage Pandas in the background for efficient processing of large volumes of data 
  • Advertising: Personalization via advertising has taken a huge leap, with software conglomerates streamlining the process of lead generation. Pandas help a lot of smaller companies streamline their efforts 
  • Natural Language Processing: With the help of packages such as Pandas and Scikit Learn, it has become simpler to create NLP models that can help with a plethora of applications. 

2. Matplotlib   

Matplotlib is a Python package example that aids in visualizing and plotting data to make static, animated, and interactive visualizations. 

a. Features 

  • Enables a wide variety of visualizations such as line plots, subplots, images, histograms, paths, bar charts, pie charts, tables, scatter plots, filled curves, log plots, data handling, and stream plots 
  • It can be embedded in various IDEs as well as Jupyter Lab, and Graphical User Interfaces 
  • Images and visualizations can be exported to multiple file formats 

b. Pros 

  • Based on NumPy, matplotlib is fairly simple for beginners to start off with 
  • Intuitive for folks who have worked with graph plotting tools such as Matlab 
  • High level of customization through code 

c. Cons 

  • Not all visualizations from Matplotlib are interactive 
  • It is difficult to adjust the visuals from Matplotlib to look great as it is a low-level interface 
  • Plotting non-basic plots in matplotlib can get complex, as it can get code-heavy 

d. Applications 

  • Used to make a lot of preliminary plots for large datasets, matplotlib is helpful in visualizing data  
  • Given that it uses NumPy in the backend, matplotlib is used extensively with multiple third-party extensions to get the fastest results 

3. Plotly  

Perhaps the best plotting and graphing software in Python. Plotly enables the user to build low-code applications to build, scale and deploy data apps in Python. 

a. Features 

  • We are able to build full-fledged enterprise-grade applications with plotly or dash in the backend 
  • Plotly has all of the features of Matplotlib and more 

b. Pros 

  • Interactive Plots: One of the biggest advantages of plotly over most other graphing and visualization tool is that the plots that we make in plotly are interactive 
  • Saves time: The interactivity helps the user save time and makes it easy to export and modify the plot 
  • Arguably the best plotting library: With customization and flexibility like none other, plotly is perhaps the best plotting library that exists 
  • Aesthetics: The ability to plot all of the charts from matplotlib and seaborn in a more aesthetically pleasing well, plotly has the best of all worlds 

c. Cons 

  • Initial Setup: There is a teething period with plotly with an online account, and plotly is code-heavy for a lot of instances 
  • Extremely vast: When there is so much to keep up with, like Chart Studio, Express, etc., it is hard to keep everything up to date, so the documentation is out of date at times 

d. Applications 

  • There are numerous use cases of plotly being used to build an enterprise-grade dashboard with the dash in the background 

4. Seaborn  

We discussed that matplotlib has a low-level interface. Seaborn is built on top of matplotlib with a high-level interface to provide informative statistical graphs and draw attractive visualizations. 

a. Features 

  • Has plots such as relational plots, categorical plots, distribution plots, regression plots, matrix plots, multi-plot grids 
  • There are themes to style matplotlib visualizations 
  • Seaborn is able to plot linear regression models and statistical time series and works well with NumPy as well as Pandas data structures 
  • It is also fast at visualizing univariate and bivariate data 

b. Pros 

  • Seaborn is simply faster as a visualization tool – we can pass the entire data, and seaborn does a lot of the work 
  • Seaborn has an interactive and informative representation that lets us visualize the data in a quick fashion 

c. Cons 

  • Visualizations are not exactly interactive 
  • We are limited to the styles that seaborn has in terms of customization 

d. Applications 

  • Seaborn is used to visualize the data in an aesthetically pleasing fashion, and it is used in multiple IDEs 

5. Ggplot  

Ggplot stands for the grammar of graphics. Ggplot is a package that was built with R in mind. It can be used in Python using the package plotnine 

a. Features 

  • Follows a format of data, x, y, and then the rest of the aesthetics 
  • It can be used to create complex plots from data present in a data frame 
  • It can provide a programmatic interface to work on the visualizations, the variables to represent, how to display them, and their corresponding visual properties 
  • Has components such as statistical transformations, scales, facets, coordinated systems, and themes 

b. Pros 

  • The consistent underlying theme of the grammar of graphics means that you can do more visualization with lesser code 
  • The plots have a high level of abstraction ad are flexible 
  • The refinement has to lead to a mature and complete graphics system 

c. Cons 

  • ggplot of slower as compared to more fundamental graphics solutions 
  • Even though the visuals that ggplot has to look nicer than the other libraries, it is difficult to change the default colors 
  • Gglot might require modifications to the structure of the data for certain plots 

d. Applications 

  • A great package to use to make quick visuals, irrespective of how layered the base data is 

6. Altair  

Altair is a declarative statistical visualization package that s based on Vega (which is a visualization grammar. 

a. Features 

  • Can provide aesthetic and effective visualization with a minimal amount of code 

b. Pros 

  • The base code remains the same, and the user needs to only change the "mark" attribute to get various plots 
  • The code is short and simple as compared to other libraries. There is a higher focus on the relationship between the data columns than on the plot details 
  • It is easier to implement interactivity and faceting 

c. Cons 

  • There is a limited amount of customization possible 
  • Plotting complex machine-learning models becomes difficult 
  • There is no 3D visualization with the Altair library for Python 

d. Applications 

  • Altair is used to automatically visualize in a number of ways graphs for data frames that preferably have less than 5,000 rows (Source

7. Autoviz  

Autoviz can make automatic visualizations of a dataset.  

a. Features 

  • Autoviz is able to analyze the dataset and make recommendations on how to clean your variables 
  • It is able to detect missing values, mixed data types, and rare categories and can help speed up data-cleaning activities 
  • Can be a part of MLOps pipelines and form word clouds 

b. Pros 

  • Everything is done automatically! This is a huge boon if you are not sure what exactly you are analyzing in the dataset 
  • Autiviz is considerably fast in creating visualizations 
  • There is no bias in the visualizations, wherein a subject matter expert may even have a bias in the charts they select 

c. Cons 

  • No cons as such; it is fast and effective. It would depend on the codebase maintenance team of AutoViz to keep innovating for AutoViz to be widely used 

d. Applications 

  • AutoViz can be used across a wide range of domains to understand data better and faster 

8. Pydot  

Graphviz is an open-source visualization tool. It used an object called DOT, which is written in Python. Pydot is an interface to Graphviz. 

a. Features 

  • Used to manipulate dot files from Graphviz 
  • From an existing DOT string, a graph can be parsed  
  • NetworkX graphs can be converted to a Pydot graph 
  • Can add further nodes and edges along with being able to edit the attributes of graphs, nodes, and edges 

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C) Python Libraries for Machine Learning 

Let's go over some of the python packages for data science and machine learning. 

1. Keras  

Keras provides an interface for Artificial Neural Networks (ANNs) and acts as an interface to the Tensorflow library. Keras is optimized to reduce cognitive load to help perform Deep Learning in a manner that requires a minimum number of user actions. 

a. Features 

  • Keras is simple, flexible, and powerful, and Keras is able to run experiments quickly and efficiently 
  • Keras is built on top of Tensorflow 2 and can scale to large settings for production quality outputs 
  • Keras can be deployed anywhere, such as websites, phones with Android, iOS, embedded devices, and even as a web API 

b. Pros 

  • Since Keras is tightly integrated with TensorFlow 2, Keras is able to cover end-to-end machine learning solutions 
  • It is easy to use and is one of the best ways to get into deep learning 
  • Keras has pre-trained models and has multiple GPUs as well as TPU support 

c. Cons 

  • Low-level API problems: Sometimes, while working with Keras, it is possible to get low-level backend problems, especially when we would like to perform operations that Keras was not designed for 
  • There are certain features, such as data pre-processing, basic machine learning algorithms, dynamic chart creation, etc., that Keras can improve on 
  • Thanks to Keras's user-friendliness, some applications sacrifice speed for their user-friendliness 

d. Applications 

  • Pre-trained models are especially helpful in applications such as image recognition, where we use models such as Xception, ResNet, MobileNet, ImageNet, etc. 

2. SciKit-Learn  

Arguably the most popular machine learning library for modeling, Scikit learn is a machine learning library used for predictive data analysis. Scikit-learn has been built on open-source tools such as NumPy, SciPy, and Matplotlib.  

a. Features 

  • Supports predictive data analytics applications such as classification, regression, clustering, dimensionality reduction, model selection, pre-processing, etc. 
  • Scikit-learn supports algorithms such as logistic regression, decision trees, bagging, boosting, random forest, XGBoost, and Support Vector Machine (SVM), along with a whole host of classification metrics as well 

b. Pros 

  • The package comes under a license that makes it free to use with minimum licensing and legal restrictions 
  • Scikit-learn is one of the most used packages for machine learning packages and is a great toolkit to work with modeling 

c. Cons 

  • Scikit-learn is a great fundamental package; however, it is not the library of choice for in-depth machine learning 
  • Does not easily scale to large datasets 
  • One thing that becomes a barrier for data scientists is the confusion while working with NumPy as well as Pandas data frames. Scikit-learn does not work as well with Pandas 

d. Applications 

  • There is a wide range of applications, such as spam detection, image recognition, drug response, stock prices, customer segmentation, grouping experimentation, etc. 

3. PyTorch  

PyTorch is a machine learning framework that is based on the torch library by Meta that accelerates the path from prototyping & researching to production & deployment. 

a. Features 

  • PyTorch is built from the ground up to be production ready. There are easy tools to deploy the models in a way that is cloud agnostic. 
  • Supports features such as metrics, logging, multi-model serving, and the creation of RESTful endpoints 
  • Training for the model can be done in a distributed fashion & has a robust ecosystem 

b. Pros 

  • Even though PyTorch has a C++ frontend, it also has a Pythonic frontend to extend PyTorch functionalities when desired 
  • PyTorch is easy to learn, has a strong community, and is easy to debug 
  • It has support for CPU as well as GPU and can scale very well 

c. Cons 

  • PyTorch is fairly new and is not as widely known in the community – it was released in 2016 
  • There is no monitoring and dashboard tool like Tensorflow has a tensor board 
  • The developer community s relatively smaller as compared to the other frameworks 

d. Applications 

  • There are applications for deep learning – PyTorch in computer vision, natural language processing, and even reinforcement learning 

4. Pycaret  

PyCaret is a low-code machine-learning library that is used to automate machine learning workflows. 

a. Features 

  • The three primary features that PyCare pushes are:  
    Fast + Scalable + Explainable 
  • PyCaret recommends you spend less time coding and more time on analysis due to its automation. 
  • It has automated workflows in the field of exploratory data analysis, data pre-processing, model training, model explainability, and mlops 
  • PyCaret does end-to-end machine learning – all the way from eda to deployment, where it has advanced features such as  
  • Experiment Tracking 
  • Creating ML Applications 
  • Building Docker Images 
  • Creating REST APIs 
  • GPU Support 

b. Pros 

  • Models can be created with just a line of code – this can make modeling really approachable 
  • PyCaret automatically tunes the model, removing all of the labor that goes into hyperparameter tuning 
  • Evaluation is also a line of code 

c. Cons 

  • PyCaret is on its way to democratizing data. But it definitely won't be happening that soon as it is not mature and there are a lot of bugs to iron out 
  • AutoML means we are not able to see what is happening at the backend. So, it would not be advisable for beginners who are looking to learn 

d. Applications 

  • AutoML libraries are great places to start our experimentation because they can do a lot more in much less time, and this can give us solid direction 
  • PyCaret leads to increased productivity, is easy to use, and is business ready 

5. TensorFlow  

Tensorflow is a world-renowned package with a focus on the training and inference of deep neural networks. It is an opens source package for machine learning and Data Science. 

a. Features 

  • Tensorflow is used to prepare data, build ML models, deploy models and implement MLOps 
  • Tensorflow enables ease of use via pre-trained models, research with state-of-the-art models, and helps build your own models 
  • Tensorflow can be deployed on the web, on mobile and edge, and on servers 

b. Pros 

  • Models are easy to build with Tensorflow using the high-level Keras API 
  • Tensorflow enables Robust ML Production  
  • Tensorflow is scalable, enables easy debugging, has extensive scalable architectural support, and has fantastic library management support 

c. Cons 

  • Tensorflow is not that exceptional on Windows  
  • As compared to other frameworks, Tensorflow is relatively slow and inconsistent 
  • Tensorflow has architectural limitations, where it only allows the execution of models and does not allow its training 

d. Applications 

  • Airbnb leverages Tensorflow to classify images and detect objects 
  • Airbus leverages Tensorflow to extract information from satellite images to deliver insights to clients 
  • GE leverages Tensorflow to identify the anatomy on MRIs of brains 

6. Requests  

Requests is an HTTP library that allows the user to send and receive HTTP requests easily. It has over 30 million downloads per week. 

a. Features 

  • Requests have features like keep-alive, connection pooling, international domains, sessions with cookie persistence, browser-style SSL verification, content decoding, authentication, automatic decompression, HTTP(s) proxy support, multipart file uploads, streaming downloads, connection timeouts, etc. 
  • The requests module allows us to send HTTP requests using Python and returns a response object with the response data such as content, encoding, and data 

b. Pros 

  • Easy to use, this library can also be used for web scraping 
  • With the requests module, it is possible to get, post, delete and update the data in a particular link 
  • Handling cookies and sessions are easy. The security is also taken care of by the authentications module 

c. Applications 

  • The requests package is used to make requests and test out various URLs for performance, security, etc. 

D) Python Libraries for Data Mining and Data Scrapping 

1. Scrapy  

Python scrapy is a framework that is open source and collaborative for obtaining the information you require from websites. Quickly, easily, and extensively. It can be applied to a variety of purposes, including data mining, monitoring, and automated testing. 

a. Features 

  • Scrapy is capable of exporting feeds in formats such as JSON, CSV, and XML  
  • Scrapy consists of robust encoding support and auto-detection, which enables to deal with non-standard, broken, and foreign encoding declarations  
  • With helper methods to extract utilizing regular expressions, expanded CSS selectors and XPath expressions are supported for choosing and extracting data from HTML/XML sources. 

b. Pros 

  • It is a cross-platform application framework that can be used across Windows, Linux, and Mac OS  
  • The requests on scrapy are processed in an asynchronous manner, meaning it can load several pages in parallel  
  • Large volumes of data can be scraped using scrapy while consuming little memory and CPU space 

c. Cons 

  • Python version of 2.7 or greater is required for using scrapy 
  • Different operating systems have different installation processes for scrapy  
  • Scrapy cannot handle Javascript 

d. Applications 

  • Web Scraping 
  • Data Extraction using APIs  
  • Web crawler for different websites 

2.  BeautifulSoup   

Python's Beautiful Soup package is used to extract data from HTML and XML files for web scraping purposes. The source code of the website generates a parse tree that can be utilized to extract information and data in a hierarchical and more comprehensible way. 

a. Features 

  • Consists of different parsing tools, which are Html.parser, lxml, and HTML5lib enabling different parsing methods 
  • BeautifulSoup permits the processing of parallel requests  

b. Pros 

  • BeautifulSoup is easy to use  
  • BeautifulSoup only requires a few lines of code, making it widely popular among many developers 
  • Reliable online community for BeautifulSoup, which for resolution of questions at a quick turnaround 

c. Cons 

  • It is not easy to set BeautifulSoup  
  • It lags in speed and performance in comparison to Scrapy 
  • It is limited to smaller web scraping tasks with less amount of data 

d. Applications 

  • It is used for parsing HTML and XML packages for web scraping  

3. SQLAlchemy  

SQLAlchemy is a python SQL toolkit and an object relationship mapper that enables a user the full flexibility and power to use SQL in Python. It is widely popular for its object-relational mapper (ORM), which provides a data mapper pattern where classes are mapped to the database in multiple, open-ended ways that allow the object model and the database schema to develop in a cleanly decouples method 

a. Features 

  • It is compatible with Python 2.5x - 3. x versions and also supports both Jython (Python implementation in Java)  and Pypy 
  • The Core is a fully featured SQL abstraction toolkit consisting of DBAPI implementations and SQL expression language 
  • The ORM method ensures clean decoupled development between the object model and database schema from the inception 

b. Pros 

  • Supports a wide range of databases such as SQLite, PostgreSQL, MySQL, Oracle, etc.  
  • It is open source and hence can be used by just installing the package  
  • It allows users to write Python code to map from the DB schema to the python object, meaning SQL knowledge is not necessarily required  

c. Cons 

  • Not always efficient due to the layer of abstractions  

d. Applications 

  • SQLalchemy facilitates the communication between Python and databases 
  • A user can create python code to interact with the databases using the SQLalchemy package 

E) Python Libraries For Natural Language Processing 

1. NLTK  

A collection of libraries and applications for statistical language processing can be found in the NLTK (Natural Language Toolkit) library. One of the most potent NLP libraries, it includes tools that allow computers to comprehend human language and respond when it is used. This quite popular package in Python finds its application quite often in education and research 

a. Features 

  • Tokenization: If we break down a text paragraph into smaller chunks, a single chunk is called a token 
  • NLTK offers sentiment analysis through its built-in classifier, which enables tagging or determining the ratio of positive to negative engagements about a specific topic 
  • Stop words and names can be removed in a recommendation system with NLTK 

b. Pros 

  • It supports the largest number of languages than any other similar packages  

c. Cons 

  • The difficulty level of using this package is on the higher side 
  • NLTK package is comparatively slower than similar packages due to which it doesn't match the demands of real-world production usage 

d. Applications 

  • NLTK can be used for performing sentiment analysis on online reviews 
  • Chatbots can be built using the nltk.chat module 

2. SpaCy  

An advanced NLP library called SpaCy is accessible in Python and Cython. It is designed to operate alongside deep learning frameworks like TensorFlow or PyTorch and is performance-oriented. Convolutional neural network models for tagging, parsing, and named entity recognition are included, along with tokenization for more than 50 languages. 

a. Features 

  • Similar to NLTK, the spacy library consists of tokenization too 
  • Part of speech (POS) tagging can be performed using SpaCy, where a word's POS is determined for nouns, verbs, adjectives, etc. 
  • SpaCy also enables Named Entity Recognition (NER) which helps in identifying and classifying named entities 

b. Pros 

  • It is faster than NLTK 
  • It is a library that is easy to learn and use 
  • It uses Neural networks for training models 

c. Cons 

  • SpaCy is not very customizable if the task doesn't match one of SpaCy's prebuilt models, which makes it less flexible than NLTK 

d. Applications 

  • It is used for analyzing online reviews as well as sentiment analysis 
  • Automated summarization of resumes with Named Entity Recognition 
  • Search autocomplete, and autocorrect can be done with SpaCy 

3. Gensim  

Gensim, which is short for Generate Similar, is an open-source, popular NLP language library used for topic modeling. Genism uses modern statistical ML for performing complex tasks, including corpora, topic identification, etc., 

a. Features 

  • Genism easily processes large and web-scale corpora through its online training algorithms  
  • It is highly scalable, meaning the input corpus doesn't have to reside in RAM at any given time. Algorithms part of Genism are memory-independent  
  • Genism is platform agnostic (Windows, Linux and Mac OS) 
  • Genism enables effective and efficient multicore implementations for high-speed processing and retrieval  

b. Pros 

  • Enables us to handle large text files even without loading them to the memory 
  • Due to its use of unsupervised models, it doesn't require annotations or hand tagging documents  

c. Cons 

  • Genism is limited to unsupervised text modeling only  
  • It doesn't possess the capacity to implement an NLP pipeline fully  

d. Applications 

  • Has been used and cited in thousands of academic, and commercial  applications and research papers, as well 
  • Includes streamlined implementations for fastText (Word embedding text classification), Word2vec (for reconstructing linguistic context) 
  • For applications such as Latent Semantic, Latent Dirichlet Allocation, term frequency-inverse document frequency (TF-IDF)

F) Bonus Python Libraries! 

Now that we have gone over python libraries used for data science let's go over some bonus Python libraries. 

1. OpenCV  

This is a library that is dedicated to applications of computer vision, machine learning, and image processing. For the usage of various machine learning and computer vision skills like object identification and facial recognition, OpenCV provides access to over 2,500 methods.  

a. Features 

  • Supports a wide variety of programming languages (Python, C++, Java, etc.) 
  • It can identify objects, faces, or handwriting by processing images and videos  
  • It is an open-source, quick, and often easy to implement and integrate  
  • The library's code is customizable, which can be performed to meet business needs  

b. Pros 

  • More than 2500 modern and classic algorithms can be accessed for performing various tasks  
  • OpenCV is extensively used across the industry, which makes the community very accessible for assistance for all users  
  • It takes advantage of hardware acceleration and multicore systems to deploy, which provides algorithmic efficiency  

c. Cons 

  • Within the facial recognition system, there are many limitations for OpenCV, such as being highly sensitive to pose variations, and occlusion interference.

d. Applications 

  • OpenCV can be used to remove watermarks on images  
  • Backgrounds from images  can be removed/cleaned using OpenCV 
  • OpenCV can be used for facial detection and recognition and similarly  for objects as well 

2. Mahotas  

Mahotas is a Python module for computer vision and image processing. Many of the algorithms are speed-oriented C++ implementations that use NumPy arrays and a fairly clear Python interface. Currently, Mahotas has over 100 image processing and computer vision functions. 

a. Features 

  • It has numerous operations for image processing which include cropping images, finding eccentricity and roundness, finding tonal distribution using histograms, etc. 
  • It has functions for wavelet decompositions and local feature computations  
  • Mahotas has a comprehensive automated unit test suite that verifies all functionality and contains a number of regression tests for the Quality Control of module  

b. Pros 

  • It is faster in processing than libraries such as pymorph, scikits-image  
  • It is available on different operating systems such as Linux, Mac OS, and windows  

c. Cons 

  • For more complex methods such as watershed, the pure python approach is considered to be very inefficient  
  • It is dependent on NumPy to be present and installed  

d. Applications 

  • It is widely used for image processing which utilizes the above-mentioned features  

3. SimpleITK  

SimpleITK is a comprehensive toolkit for image analysis that supports a variety of filtering operations as well as picture segmentation and registration. 

a. Features 

  • Up to 20 image file types, including JPG, PNG, and DICOM, are supported and convertible through its image file I/O. 
  • Otsu, level sets, and watersheds are just a few of the image segmentation workflow filters that are offered. 
  • It understands images as spatial objects rather than an array of pixels. 

b. Pros 

  • It is available in most programming languages such as Python, R, Java, C#, etc 
  • The documentation for SimpleITK is good and extensive for high-level guides and instructions to build toolkits and examples for SimpleIKT applications  

c. Cons 

  • Main ITK features such as spatial objects framework, point sets, and the mesh framework are missing in SimplITK 

d. Applications 

  • SimpleITK excels in basic image classification and ITKv4 registration framework 

4. Pillow  

The standard image processing package for the Python language is the Python Imaging Library (extension of PIL). It includes simple image processing capabilities that help with image creation, editing, and saving. PIL will be replaced going forward by Pillow, it was stated. BMP, PNG, JPEG, and TIFF are just a few of the several image file types that Pillow supports. 

a. Features 

  • Provides features for image processing such as obtaining information for color mode, size, and format of the image, rotating images, etc., 
  • It supports different types of formats of images such as jpeg, png, gif, tiff, etc. 
  • It allows getting general statistics of the images, which can be used for statistical analysis and automatic contrast enhancement  

b. Pros 

  • It has a wide variety of actions that can be performed on images  
  • It works on devices such as Raspberry Pi zero, where modules such as OpenCV don't  

c. Cons 

  • It lacks optimization of codes even though it is simple and easy to pick up 
  • Feature extraction from images is a limitation of Pillow 

d. Applications 

  • It is used for various operations such as creating thumbnails, merging images, cropping, blurring, and resizing images 
  • It can be used for creating watermarks for images   

5. Selenium 

Python web browser interaction can be automated with the help of the selenium module. For many testers around the world, Selenium is the first choice for any tasks relating to executing automated tests. It allows the user to automatically define and detect the tests on a pre-decided browser. 

a. Features 

  • Selenium is capable of interacting with different browsers, such as Chrome and Safari. IE, Opera, Edge, etc.  
  • Selenium can support all the programming languages like Python, JavaScript, Ruby, etc. 
  • Due to its WebDriver component within Selenium, it is able to execute the test cases with high performance and speed 

b. Pros 

  • Selenium is an open-source website that can be easily downloaded from its website 
  • It can work across different Operating Systems such as Linux, Windows, Mac OS, etc. 
  • Server installation is not necessary as Selenium interacts directly with the browser 

c. Cons 

  • Incomplete solution: Third-party frameworks are required to automate the testing of web applications completely  
  • Code modification is hard: Since the scripts are written in Mandarin, it makes it non-user friendly and hard to modify 

d. Applications 

  • Automated Testing: Selenium enables automated testing, which saves a lot of time and effort for web testers 
  • Users can create automation scripts to test and view the results from the automation test results  

6. PyTest 

PyTest is a plugin-based, feature-rich ecosystem for testing your Python code. Common activities can be completed with PyTest with less code, and more complex jobs can be completed using a range of time-saving commands and plug-ins. 

a. Features 

  • PyTest provides multiple options for running tests from the command line  
  • It is easy to start with and uses simple syntax, making it easier for one to pick up 
  • The community of PyTest has huge test plug-ins for extending its functionality  

b. Pros 

  • PyTest is open source and does not associate with any licensing cost  
  • It is easy and quick to learn due to its simple syntax 
  • Can execute multiple test cases simultaneously, which reduces the duration of execution  

c. Cons 

  • PyTest doesn't guarantee to uncover every bug 
  • More time investment is required at times when one has to write multiple lines of code to test one line of code 
  • Errors in integration are not identified as PyTest only tests sets of data and its functionality  

d. Applications 

  • Simple and scalable for writing tests for databases  

7. PyUnit  

Python unit testing is used to find defects early in the application development process when fixing them will be easier and less expensive. For the automated testing of the code, PyUnit includes fixtures, test cases, test suites, and a test runner. You can group test cases into suites in PyUnit that share the same fixtures. 

a. Features 

  • Test cases can be organized as suites using the same fixtures  
  • It includes test cases, test suites, and a test runner, which enables automatic running for the testing of the code  

b. Pros 

  • By using PyUnit to create tests, we can identify bugs early in the development cycle 

c. Cons 

  • It is not suitable for high-level testing, which is also called large test suites  
  • Group testing is not available in PyUnit  
  • HTML reports cant be created using PyUnit 

d. Applications 

  • To perform unit tests which can be utilized to create automated test cases for testing databases, line of codes 

Why Use Python Libraries for Data Science

Let's take a simple, fundamental example of a function and extrapolate the use of python libraries from there. Let's say we are trying to add two numbers, and we need to use this in ten places in our code. 

Method A 

a = 1, b = 6 

We will mention c = a + b in ten places in our code.  

Method B 

We can define a function,  

Def calculation_function(a, b): 
     c = a + b      
     return c 

Now, we will mention this function is ten places in our code, instead of the code directly like in Method A. 

Use-Case 

Now, let's say that the business changes the logic, and we need to make it multiplication (*)instead of addition (+).  

In Method A, you will have to go to ten places and change the code manually. This is error-prone and inefficient.  

In Method B, you will have to change one character in the function. This will apply in 10 places in a consistent and efficient manner. 

Now, let's scale this up to thousands, maybe even millions of lines of code. Every time you try to implement a new logic, would you rather re-write so many lines of code that are error-prone, or would you rather use near-perfect, well-documented, versioned code compliant with world-class global coding standards? Unless you are doing something extraordinarily unique, the best route for 99% of people is to use packages in Python. 

  1. Ease of learning  
  2. Less Code  
  3. Prebuilt Libraries  
  4. Platform Independent  
  5. Massive Community Support  

Conclusion

In this detailed blog, we were able to understand a wide variety of packages. We first went over the fundaments of Python Packages and why we use them. From there, we explored packages for mathematics, data exploration, visualization, machine learning, data mining, natural language processing, and even some bonus Python packages. 

Packages are the backbone of why Python is the best language in the world today, and having this knowledge in your toolkit will make you stand out as an accomplished data scientist.  

So why not start your journey with the Python Programming Bootcamp from upGrad? You can learn Python, SQL, and other programming tools like NumPy, Pandas, and more with live online classes over eight weeks. 

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Frequently Asked Questions (FAQs)

1. Is Pandas as fast as NumPy?

In terms of speed, NumPy and Pandas difference is that numerous C or Cython-optimized functions that are available in Pandas may be quicker than their NumPy equivalents. Pandas DataFrames are typically going to be slower than a NumPy array if you want to perform mathematical operations like computing the mean, the dot product, and other similar tasks. 

2. What should I learn first, Pandas or NumPy?

The ndarrays in NumPy are used in Pandas DataFrames and learning operations like indexing, slicing, etc. in ndarrays can prove to be useful while exploring Pandas. 

3. Can Pandas work without NumPy?

No, NumPy is required for Pandas to work since Pandas is built on top of NumPy and other libraries. 

4. Which library is faster than Pandas?

Pandas make use of a single core of CPU to perform operations. Libraries such as Dask, PySpark, PyPolars, cuDF, Modin, etc. take advantage of multi-cores of CPU and therefore, are faster than Pandas.

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Rohit Sharma

Rohit Sharma is the Program Director for the UpGrad-IIIT Bangalore, PG Diploma Data Analytics Program.

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Note: The article was originally written for LinkedIn Pulse by Sameer Dhanrajani, Business Leader at Cognizant Technology Solutions. Data Scientist is one of the fastest-growing and highest paid jobs in technology industry. Dr. Tara Sinclair, Indeed.com’s chief economist, said the number of job postings for “data scientist” grew 57% year-over-year in Q1:2015. Yet, in spite of the incredibly high demand, it’s not entirely clear what education someone needs to land one of these coveted roles. Do you get a degree in data science? Attend a bootcamp? Take a few Udemy courses and jump in? Learn data science to gain edge over your competitors It depends on what practice you end up it. Data Sciences has become a widely implemented phenomenon and multiple companies are grappling to build a decent DS practice in-house. Usually online courses, MOOCs and free courseware usually provides the necessary direction for starters to get a clear understanding, quickly for execution. But Data Science practice, which involves advanced analytics implementation, with a more deep-level exploratory approach to implementing Data Analytics, Machine Learning, NLP, Artificial Intelligence, Deep Learning, Prescriptive Analytics areas would require a more establishment-centric, dedicated and extensive curriculum approach. A data scientist differs from a business analyst ;data scientist requires dwelling deep into data and gathering insights, intelligence and recommendations that could very well provide the necessary impetus and direction that a company would have to take, on a foundational level. And the best place to train such deep-seeded skill would be a university-led degree course on Data Sciences. It’s a well-known fact that there is a huge gap between the demand and supply of data scientist talent across the world. Though it has taken some time, but educationalists all across have recognized this fact and have created unique blends of analytics courses. Every month, we hear a new course starting at a globally recognized university. Data growth is headed in one direction, so it’s clear that the skills gap is a long-term problem. But many businesses just can’t wait the three to five years it might take today’s undergrads to become business-savvy professionals. Hence this aptly briefs an alarming need of analytics education and why universities around the world are scrambling to get started on the route towards being analytics education leaders. Obviously, the first mover advantage would define the best courses in years to come i.e. institutes that take up the data science journey sooner would have a much mature footing in next few years and they would find it easier to attract and place students. Strategic Benefits to implementing Data Science Degrees Data science involves multiple disciplines The reason why data scientists are so highly sought after, is because the job is really a mashup of different skill sets and competencies rarely found together. Data scientists have tended to come from two different disciplines, computer science and statistics, but the best data science involves both disciplines. One of the dangers is statisticians not picking up on some of the new ideas that are coming out of machine learning, or computer scientists just not knowing enough classical statistics to know the pitfalls. Even though not everything can be taught in a Degree course, universities should clearly understand the fact that training a data science graduate would involve including multiple, heterogeneous skills as curriculum and not one consistent courseware. They might involve computer science, mathematics, statistics, business understanding, insight interpretation, even soft skills on data story telling articulation. Beware of programs that are only repackaging material from other courses Because data science involves a mixture of skills — skills that many universities already teach individually — there’s a tendency toward just repackaging existing courses into a coveted “data science” degree. There are mixed feelings about such university programs. It seems to me that they’re more designed to capitalize on the fact that the demand is out there than they are in producing good data scientists. Often, they’re doing it by creating programs that emulate what they think people need to learn. And if you think about the early people who were doing this, they had a weird combination of math and programming and business problems. They all came from different areas. They grew themselves. The universities didn’t grow them. Much of a program’s value comes from who is creating and choosing its courses. There have been some decent course guides in the past from some universities, it’s all about who designs the program and whether they put deep and dense content and coverage into it, or whether they just think of data science as exactly the same as the old sort of data mining. The Theories on Theory A recurring theme throughout my conversations was the role of theory and its extension to practical approaches, case studies and live projects. A good recommendation to aspiring data scientists would be to find a university that offers a bachelor’s degree in data science. Learn it at the bachelor’s level and avoid getting mired in only deep theory at the PostGrad level. You’d think the master’s degree dealing with mostly theory would be better, but I don’t think so. By the time you get to the MS you’re working with the professors and they want to teach you a lot of theory. You’re going to learn things from a very academic point of view, which will help you, but only if you want to publish theoretical papers. Hence, universities, especially those framing a PostGrad degree in Data Science should make sure not to fall into orchestrating a curriculum with a long drawn theory-centric approach. Also, like many of the MOOCs out there, a minimum of a capstone project would be a must to give the students a more pragmatic view of data and working on it. It’s important to learn theory of course. I know too many ‘data scientists’ even at places like Google who wouldn’t be able to tell you what Bayes’ Theorem or conditional independence is, and I think data science unfortunately suffers from a lack of rigor at many companies. But the target implementation of the students, which would mostly be in corporate houses, dealing with real consumer or organizational data, should be finessed using either simulated practical approach or with collaboration with Data Science companies to give an opportunity to students to deal with real life projects dealing with data analysis and drawing out actual business insights. Our learners also read: Free Python Course with Certification upGrad’s Exclusive Data Science Webinar for you – ODE Thought Leadership Presentation document.createElement('video'); https://cdn.upgrad.com/blog/ppt-by-ode-infinity.mp4 Explore our Popular Data Science Online Certifications Executive Post Graduate Programme in Data Science from IIITB Professional Certificate Program in Data Science for Business Decision Making Master of Science in Data Science from University of Arizona Advanced Certificate Programme in Data Science from IIITB Professional Certificate Program in Data Science and Business Analytics from University of Maryland Data Science Online Certifications Don’t Forget About the Soft Skills In an article titled The Hard and Soft Skills of a Data Scientist, Todd Nevins provides a list of soft skills becoming more common in data scientist job requirements, including: Manage teams and projects across multiple departments on and offshore. Consult with clients and assist in business development. Take abstract business issues and derive an analytical solution. Top Data Science Skills You Should Learn SL. No Top Data Science Skills to Learn 1 Data Analysis Online Certification Inferential Statistics Online Certification 2 Hypothesis Testing Online Certification Logistic Regression Online Certification 3 Linear Regression Certification Linear Algebra for Analysis Online Certification The article also emphasizes the importance of these skills, and criticizes university programs for often leaving these skills out altogether: “There’s no real training about how to talk to clients, how to organize teams, or how to lead an analytics group.” Data science is still a rapidly evolving field and until the norms are more established, it’s unlikely every data scientist will be following the same path. A degree in data science will definitely act as the clay to make your career. But the part that really separates people who are successful from that are not is just a core curiosity and desire to answer questions that people have — to solve problems. Don’t do it because you think you can make a lot of money, chances are by the time you’re trained, you either don’t know the right stuff or there’s a hundred other people competing for the same position, so the only thing that’s going to stand out is whether you really like what you’re doing. Read our popular Data Science Articles Data Science Career Path: A Comprehensive Career Guide Data Science Career Growth: The Future of Work is here Why is Data Science Important? 8 Ways Data Science Brings Value to the Business Relevance of Data Science for Managers The Ultimate Data Science Cheat Sheet Every Data Scientists Should Have Top 6 Reasons Why You Should Become a Data Scientist A Day in the Life of Data Scientist: What do they do? Myth Busted: Data Science doesn’t need Coding Business Intelligence vs Data Science: What are the differences?
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by Ashish Korukonda

03 May'16
Computer Center turns Data Center; Computer Science turns Data Science

5.13K+

Computer Center turns Data Center; Computer Science turns Data Science

(This article, written by Prof. S. Sadagopan, was originally published in Analytics India Magazine) There is an old “theory” that talks of “power shift” from “carrier” to “content” and to “control” as industry matures. Here are some examples In the early days of Railways, “action” was in “building railroads”; the “tycoons” who made billions were those “railroad builders”. Once enough railroads were built, there was more action in building “engines and coaches” – General Electric and Bombardier emerged; “power” shifted from “carrier” to “content”; still later, action shifted to “passenger trains” and “freight trains” – AmTrak and Delhi Metro, for example, that used the rail infrastructure and available engines and coaches / wagons to offer a viable passenger / goods transportation service; power shifted from “content” to “control”. The story is no different in the case of automobiles; “carrier” road-building industry had the limelight for some years, then the car and truck manufacturers – “content” – GM, Daimler Chrysler, Tata, Ashok Leyland and Maruti emerged – and finally, the “control”, transport operators – KSRTC in Bangalore in the Bus segment to Uber and Ola in the Car segment. In fact, even in the airline industry, airports become the “carrier”, airplanes are the “content” and airlines represent the “control” Learn data science courses from the World’s top Universities. Earn Executive PG Programs, Advanced Certificate Programs, or Masters Programs to fast-track your career. It is a continuum; all three continue to be active – carrier, content and control – it is just the emphasis in terms of market and brand value of leading companies in that segment, profitability, employment generation and societal importance that shifts. We are witnessing a similar “power shift” in the computer industry. For nearly six decades the “action” has been on the “carrier”, namely, computers; processors, once proprietary from the likes of IBM and Control Data, then to microprocessors, then to full blown systems built around such processors – mainframes, mini computers, micro computers, personal computers and in recent times smartphones and Tablet computers. Intel and AMD in processors and IBM, DEC, HP and Sun dominated the scene in these decades. A quiet shift happened with the arrival of “independent” software companies – Microsoft and Adobe, for example and software services companies like TCS and Infosys. Along with such software products and software services companies came the Internet / e-Commerce companies – Yahoo, Google, Amazon and Flipkart; shifting the power from “carrier” to “content”. Explore our Popular Data Science Courses Executive Post Graduate Programme in Data Science from IIITB Professional Certificate Program in Data Science for Business Decision Making Master of Science in Data Science from University of Arizona Advanced Certificate Programme in Data Science from IIITB Professional Certificate Program in Data Science and Business Analytics from University of Maryland Data Science Courses This shift was once again captured by the use of “data center” starting with the arrival of Internet companies and the dot-com bubble in late nineties. In recent times, the term “cloud data center” is gaining currency after the arrival of “cloud computing”. Though interest in computers started in early fifties, Computer Science took shape only in seventies; IITs in India created the first undergraduate program in Computer Science and a formal academic entity in seventies. In the next four decades Computer Science has become a dominant academic discipline attracting the best of the talent, more so in countries like India. With its success in software services (with $ 160 Billion annual revenue, about 5 million direct jobs created in the past 20 years and nearly 7% of India’s GDP), Computer Science has become an aspiration for hundreds of millions of Indians. With the shift in “power” from “computers” to “data” – “carrier” to “content” – it is but natural, that emphasis shifts from “computer science” to “data science” – a term that is in wide circulation only in the past couple of years, more in corporate circles than in academic institutions. In many places including IIIT Bangalore, the erstwhile Database and Information Systems groups are getting re-christened as “Data Science” groups; of course, for many acdemics, “Data Science” is just a buzzword, that will go “out of fashion” soon. Only time will tell! As far as we are concerned, the arrival of data science represents the natural progression of “analytics”, that will use the “data” to create value, the same way Metro is creating value out of railroad and train coaches or Uber is creating value out of investments in road and cars or Singapore Airlines creating value out of airport infrastructure and Boeing / Airbus planes. More important, the shift from “carrier” to “content” to “control” also presents economic opportunities that are much larger in size. We do expect the same from Analytics as the emphasis shifts from Computer Science to Data Science to Analytics. Computers originally created to “compute” mathematical tables could be applied to a wide range of problems across every industry – mining and machinery, transportation, hospitality, manufacturing, retail, banking & financial services, education, healthcare and Government; in the same vein, Analytics that is currently used to summarize, visualize and predict would be used in many ways that we cannot even dream of today, the same way the designers of computer systems in 60’s and 70’s could not have predicted the varied applications of computers in the subsequent decades. We are indeed in exciting times and you the budding Analytics professional could not have been more lucky. Announcing PG Diploma in Data Analytics with IIT Bangalore – To Know more about the Program Visit – PG Diploma in Data Analytics. Top Data Science Skills to Learn to upskill SL. No Top Data Science Skills to Learn 1 Data Analysis Online Courses Inferential Statistics Online Courses 2 Hypothesis Testing Online Courses Logistic Regression Online Courses 3 Linear Regression Courses Linear Algebra for Analysis Online Courses upGrad’s Exclusive Data Science Webinar for you – ODE Thought Leadership Presentation document.createElement('video'); https://cdn.upgrad.com/blog/ppt-by-ode-infinity.mp4 Read our popular Data Science Articles Data Science Career Path: A Comprehensive Career Guide Data Science Career Growth: The Future of Work is here Why is Data Science Important? 8 Ways Data Science Brings Value to the Business Relevance of Data Science for Managers The Ultimate Data Science Cheat Sheet Every Data Scientists Should Have Top 6 Reasons Why You Should Become a Data Scientist A Day in the Life of Data Scientist: What do they do? Myth Busted: Data Science doesn’t need Coding Business Intelligence vs Data Science: What are the differences? Our learners also read: Free Online Python Course for Beginners About Prof. S. Sadagopan Professor Sadagopan, currently the Director (President) of IIIT-Bangalore (a PhD granting University), has over 25 years of experience in Operations Research, Decision Theory, Multi-criteria optimization, Simulation, Enterprise computing etc. His research work has appeared in several international journals including IEEE Transactions, European J of Operational Research, J of Optimization Theory & Applications, Naval Research Logistics, Simulation and Decision Support Systems. He is a referee for several journals and serves on the editorial boards of many journals.
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by Prof. S. Sadagopan

11 May'16
Enlarge the analytics & data science talent pool

5.19K+

Enlarge the analytics & data science talent pool

Note: The articlewas originally written by Sameer Dhanrajani, Business Leader at Cognizant Technology Solutions. A Better Talent acquisition Framework Although many articles have been written lamenting the current talent shortage in analytics and data science, I still find that the majority of companies could improve their success by simply revamping their current talent acquisition processes. Learn data science courses online from the World’s top Universities. Earn Executive PG Programs, Advanced Certificate Programs, or Masters Programs to fast-track your career. We’re all well aware that strong quantitative professionals are few and far between, so it’s in a company’s best interest to be doing everything in their power to land qualified candidates as soon as they find them. It’s a candidate’s market, with strong candidates going on and off the market lightning fast, yet many organizational processes are still slow and outdated. These sluggish procedures are not equipped to handle many candidates who are fielding multiple offers from other companies who are just as hungry (if not more so) for quantitative talent. Here are the key areas I would change to make hiring processes more competitive: Fix your salary bands – It (almost) goes without saying that if your salary offerings are outdated or aren’t competitive to the field, it will be difficult for you to get the attention of qualified candidates; stay topical with relevant compensation grids. Consider one-time bonuses – Want to make your offer compelling but can’t change the salary? Sign-on bonuses and relocation packages are also frequently used, especially near the end of the year, when a candidate is potentially walking away from an earned bonus; a sign-on bonus can help seal the deal. Be open to other forms of compensation – There are plenty of non-monetary ways to entice Quants to your company, like having the latest tools, solving challenging problems, organization-wide buy-in for analytics and more. Other things to consider could be flexible work arrangements, remote options or other unique perks. Pick up the pace – Talented analytics professionals are rare, and the chances that qualified candidates will be interviewing with multiple companies are very high. Don’t hesitate to make an offer if you find what you’re looking for at a swift pace – your competitors won’t. Court the candidate – Just as you want a candidate who stands out from the pack, a candidate wants a company that makes an effort to stand apart also. I read somewhere, a client from Chicago sent an interviewing candidate and his family pizzas from a particularly tasty restaurant in the city. I can’t say for sure that the pizza was what persuaded him to take the company’s offer, but a little old-fashioned wooing never hurts. Button up the process – Just as it helps to have an expedited process, it also works to your benefit is the process is as smooth and trouble-free as you can make it. This means hassle-free travel arrangements, on-time interviews, and quick feedback. Network – make sure that you know the best of the talent available in the market at all levels and keep in touch with them thru porfessional social sites on subtle basis as this will come handy in picking the right candidate on selective basis Redesigned Interview Process In the old days one would screen resumes and then schedule lots of 1:1’s. Typically people would ask questions aimed at assessing a candidate’s proficiency with stats, technicality, and ability to solve problems. But there were three problems with this – the interviews weren’t coordinated well enough to get a holistic view of the candidate, we were never really sure if their answers would translate to effective performance on the job, and from the perspective of the candidate it was a pretty lengthy interrogation. So, a new interview process need to be designed that is much more effective and transparent – we want to give the candidate a sense for what a day in the life of a member on the team is like, and get a read on what it would be like to work with a company. In total it takes about two days to make a decision, and there be no false positives (possibly some false negatives though), and the feedback from both the candidates and the team members has been positive. There are four steps to the process: Resume/phone screens – look for people who have experience using data to drive decisions, and some knowledge of what your company is all about. On both counts you’ll get a much deeper read later in the process; you just want to make sure that moving forward is a good use of either of both of your time. Basic data challenge – The goal here is to validate the candidate’s ability to work with data, as described in their resume. So send a few data sets to them and ask a basic question; the exercise should be easy for anyone who has experience. In-house data challenge – This is should be the meat of the interview process. Try to be as transparent about it as possible – they’ll get to see what it’s like working with you and vice versa. So have the candidate sit with the team, give them access to your data, and a broad question. They then have the day to attack the problem however they’re inclined, with the support of the people around them. Do encourage questions, have lunch with them to ease the tension, and check-in periodically to make sure they aren’t stuck on something trivial. At the end of the day, we gather a small team together and have them present their methodology and findings to you. Here, look for things like an eye for detail (did they investigate the data they’re relying upon for analysis), rigor (did they build a model and if so, are the results sound), action-oriented (what would we do with what you found), and communication skills. Read between the resume lines Intellectual curiosity is what you should discover from the project plans. It’s what gives the candidate the ability to find loopholes or outliers in data that helps crack the code to find the answers to issues like how a fraudster taps into your system or what consumer shopping behaviors should be considered when creating a new product marketing strategy. Data scientists find the opportunities that you didn’t even know were in the realm of existence for your company. They also find the needle in the haystack that is causing a kink in your business – but on an entirely monumental scale. In many instances, these are very complex algorithms and very technical findings. However, a data scientist is only as good as the person he must relay his findings to. Others within the business need to be able to understand this information and apply these insights appropriately. Explore our Popular Data Science Courses Executive Post Graduate Programme in Data Science from IIITB Professional Certificate Program in Data Science for Business Decision Making Master of Science in Data Science from University of Arizona Advanced Certificate Programme in Data Science from IIITB Professional Certificate Program in Data Science and Business Analytics from University of Maryland Data Science Courses Good data scientists can make analogies and metaphors to explain the data but not every concept can be boiled down in layman’s terms. A space rocket is not an automobile and, in the brave new world, everyone must make this paradigm shift. Top Data Science Skills You Should Learn SL. No Top Data Science Skills to Learn 1 Data Analysis Online Certification Inferential Statistics Online Certification 2 Hypothesis Testing Online Certification Logistic Regression Online Certification 3 Linear Regression Certification Linear Algebra for Analysis Online Certification upGrad’s Exclusive Data Science Webinar for you – Watch our Webinar on The Future of Consumer Data in an Open Data Economy document.createElement('video'); https://cdn.upgrad.com/blog/sashi-edupuganti.mp4 Read our popular Data Science Articles Data Science Career Path: A Comprehensive Career Guide Data Science Career Growth: The Future of Work is here Why is Data Science Important? 8 Ways Data Science Brings Value to the Business Relevance of Data Science for Managers The Ultimate Data Science Cheat Sheet Every Data Scientists Should Have Top 6 Reasons Why You Should Become a Data Scientist A Day in the Life of Data Scientist: What do they do? Myth Busted: Data Science doesn’t need Coding Business Intelligence vs Data Science: What are the differences? Our learners also read: Free Python Course with Certification And lastly, the data scientist you’re looking for needs to have strong business acumen. Do they know your business? Do they know what problems you’re trying to solve? And do they find opportunities that you never would have guessed or spotted?
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by upGrad

14 May'16
UpGrad partners with Analytics Vidhya

5.69K+

UpGrad partners with Analytics Vidhya

We are happy to announce our partnership with Analytics Vidhya, a pioneer in the Data Science community. Analytics Vidhya is well known for its impressive knowledge base, be it the hackathons they organize or tools and frameworks that they help demystify. In their own words, “Analytics Vidhya is a passionate community for Analytics/Data Science professionals, and aims at bringing together influencers and learners to augment knowledge”. Explore our Popular Data Science Degrees Executive Post Graduate Programme in Data Science from IIITB Professional Certificate Program in Data Science for Business Decision Making Master of Science in Data Science from University of Arizona Advanced Certificate Programme in Data Science from IIITB Professional Certificate Program in Data Science and Business Analytics from University of Maryland Data Science Degrees We are joining hands to provide candidates of our PG Diploma in Data Analytics, an added exposure to UpGrad Industry Projects. While the program already covers multiple case studies and projects in the core curriculum, these projects with Analytics Vidhya will be optional for students to help them further hone their skills on data-driven problem-solving techniques. To further facilitate the learning, Analytics Vidhya will also be providing mentoring sessions to help our students with the approach to these projects. Our learners also read: Free Online Python Course for Beginners Top Essential Data Science Skills to Learn SL. No Top Data Science Skills to Learn 1 Data Analysis Certifications Inferential Statistics Certifications 2 Hypothesis Testing Certifications Logistic Regression Certifications 3 Linear Regression Certifications Linear Algebra for Analysis Certifications This collaboration brings great value to the program by allowing our students to add another dimension to their resume which goes beyond the capstone projects and case studies that are already a part of the program. Read our popular Data Science Articles Data Science Career Path: A Comprehensive Career Guide Data Science Career Growth: The Future of Work is here Why is Data Science Important? 8 Ways Data Science Brings Value to the Business Relevance of Data Science for Managers The Ultimate Data Science Cheat Sheet Every Data Scientists Should Have Top 6 Reasons Why You Should Become a Data Scientist A Day in the Life of Data Scientist: What do they do? Myth Busted: Data Science doesn’t need Coding Business Intelligence vs Data Science: What are the differences? Through this, we hope our students would be equipped to showcase their ability to dissect any problem statement and interpret what the model results mean for business decision making. This also helps us to differentiate UpGrad-IIITB students in the eyes of the recruiters. upGrad’s Exclusive Data Science Webinar for you – Transformation & Opportunities in Analytics & Insights document.createElement('video'); https://cdn.upgrad.com/blog/jai-kapoor.mp4 Check out our data science training to upskill yourself
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by Omkar Pradhan

09 Oct'16
Data Analytics Student Speak: Story of Thulasiram

5.69K+

Data Analytics Student Speak: Story of Thulasiram

When Thulasiram enrolled in the UpGrad Data Analytics program, in its first cohort, he was not very different for us, from the rest of our students in this. While we still do not and should not treat learners differently, being in the business of education – we definitely see this particular student in a different light. His sheer resilience and passion for learning shaped his success story at UpGrad. Humble beginnings Born in the small town of Chittoor, Andhra Pradesh, Thulasiram does not remember much of his childhood given that he enlisted in the Navy at a very young age of about 15 years. Right out of 10th standard, he trained for four years, acquiring a diploma in mechanical engineering. Thulasiram came from humble means. His father was the manager of a small general store and his mother a housewife. It’s difficult to dream big when leading a sheltered life with not many avenues for exposure to unconventional and exciting opportunities. But you can’t take learning out of the learner. “One thing I remember about school is our Math teacher,” reminisces Thulasiram, “He used to give us lot of puzzles to solve. I still remember one puzzle. If you take a chessboard and assume that all pawns are queens; you have to arrange them in such a way that none of the eight pawns should die. Every queen, should not affect another queen. It was a challenging task, but ultimately we did it, we solved it.” Navy & MBA At 35 years of age, Thulasiram has been in the navy for 19 years. Presently, he is an instructor at the Naval Institute of Aeronautical Technology. “I am from the navy and a lot of people don’t know that there is an aviation wing too. So, it’s like a dream; when you are a small child, you never dream of touching an aircraft, let alone maintaining it. I am very proud of doing this,” says Thulasiram on taking the initiative to upskill himself and becoming a naval-aeronautics instructor. When the system doesn’t push you, you have to take the initiative yourself. Thulasiram imbibed this attitude. He went on to enroll in an MBA program and believes that the program drastically helped improve his communication skills and plan his work better. How Can You Transition to Data Analytics? Data Analytics Like most of us, Thulasiram began hearing about the hugely popular and rapidly growing domain of data analytics all around him. Already equipped with the DNA of an avid learner and keen to pick up yet another skill, Thulasiram began researching the subject. He soon realised that this was going to be a task more rigorous and challenging than any he had faced so far. It seemed you had to be a computer God, equipped with analytical, mathematical, statistical and programming skills as prerequisites – a list that could deter even the most motivated individuals. This is where Thulsiram’s determination set him apart from most others. Despite his friends, colleagues and others that he ran the idea by, expressing apprehension and deterring him from undertaking such a program purely with his interests in mind – time was taken, difficulty level, etc. – Thulasiram, true to the spirit, decided to pursue it anyway. Referring to the crucial moment when he made the decision, he says, If it is easy, everybody will do it. So, there is no fun in doing something which everybody can do. I thought, let’s go for it. Let me push myself — challenge myself. Maybe, it will be a good challenge. Let’s go ahead and see whether I will be able to do it or not. UpGrad Having made up his mind, Thulasiram got straight down to work. After some online research, he decided that UpGrad’s Data Analytics program, offered in collaboration with IIIT-Bangalore that awarded a PG Diploma on successful completion, was the way to go. The experience, he says, has been nothing short of phenomenal. It is thrilling to pick up complex concepts like machine learning, programming, or statistics within a matter of three to four months – a feat he deems nearly impossible had the source or provider been one other than UpGrad. Our learners also read: Top Python Free Courses Favorite Elements Ask him what are the top two attractions for him in this program and, surprising us, he says deadlines! Deadlines and assignments. He feels that deadlines add the right amount of pressure he needs to push himself forward and manage time well. As far as assignments are concerned, Thulasiram’s views resonate with our own – that real-life case studies and application-based learning goes a long way. Working on such cases and seeing results is far superior to only theoretical learning. He adds, “flexibility is required because mostly only working professionals will be opting for this course. You can’t say that today you are free, because tomorrow some project may be landing in your hands. So, if there is no flexibility, it will be very difficult. With flexibility, we can plan things and maybe accordingly adjust work and family and studies,” giving the UpGrad mode of learning, yet another thumbs-up. Amongst many other great things he had to say, Thulasiram was surprised at the number of live sessions conducted with industry professionals/mentors every week. Along with the rest of his class, he particularly liked the one conducted by Mr. Anand from Gramener. Top Data Science Skills to Learn to upskill SL. No Top Data Science Skills to Learn 1 Data Analysis Online Courses Inferential Statistics Online Courses 2 Hypothesis Testing Online Courses Logistic Regression Online Courses 3 Linear Regression Courses Linear Algebra for Analysis Online Courses What Kind of Salaries do Data Scientists and Analysts Demand? Get data science certification from the World’s top Universities. Learn Executive PG Programs, Advanced Certificate Programs, or Masters Programs to fast-track your career. Read our popular Data Science Articles Data Science Career Path: A Comprehensive Career Guide Data Science Career Growth: The Future of Work is here Why is Data Science Important? 8 Ways Data Science Brings Value to the Business Relevance of Data Science for Managers The Ultimate Data Science Cheat Sheet Every Data Scientists Should Have Top 6 Reasons Why You Should Become a Data Scientist A Day in the Life of Data Scientist: What do they do? Myth Busted: Data Science doesn’t need Coding Business Intelligence vs Data Science: What are the differences? upGrad’s Exclusive Data Science Webinar for you – ODE Thought Leadership Presentation document.createElement('video'); https://cdn.upgrad.com/blog/ppt-by-ode-infinity.mp4 Explore our Popular Data Science Courses Executive Post Graduate Programme in Data Science from IIITB Professional Certificate Program in Data Science for Business Decision Making Master of Science in Data Science from University of Arizona Advanced Certificate Programme in Data Science from IIITB Professional Certificate Program in Data Science and Business Analytics from University of Maryland Data Science Courses “Have learned most here, only want to learn..” Interested only in learning, Thulasiram made this observation about the program – compared to his MBA or any other stage of life. He signs off calling it a game-changer and giving a strong recommendation to UpGrad’s Data Analytics program. We are truly grateful to Thulasiram and our entire student community who give us the zeal to move forward every day, with testimonials like these, and make the learning experience more authentic, engaging, and truly rewarding for each one of them. If you are curious to learn about data analytics, data science, check out IIIT-B & upGrad’s PG Diploma in Data Science which is created for working professionals and offers 10+ case studies & projects, practical hands-on workshops, mentorship with industry experts, 1-on-1 with industry mentors, 400+ hours of learning and job assistance with top firms.
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by Apoorva Shankar

07 Dec'16
Decoding Easy vs. Not-So-Easy Data Analytics

5.12K+

Decoding Easy vs. Not-So-Easy Data Analytics

Authored by Professor S. Sadagopan, Director – IIIT Bangalore. Prof. Sadagopan is one of the most experienced academicians on the expert panel of UpGrad & IIIT-B PG Diploma Program in Data Analytics. As a budding analytics professional confounded by jargon, hype and overwhelming marketing messages that talk of millions of upcoming jobs that are paid in millions of Rupees, you ought to get clarity about the “real” value of a data analytics education. Here are some tidbits – that should hopefully help in reducing your confusion. Some smart people can use “analytical thinking” to come up with “amazing numbers”; they are very useful but being “intuitive”, they cannot be “taught.” For example: Easy Analytics Pre-configuring ATMs with Data Insights  “We have the fastest ATM on this planet” Claimed a respected Bank. Did they get a new ATM made especially for them? No way. Some smart employee with an analytical mindset found that 90% of the time that users go to an ATM to withdraw cash, they use a fixed amount, say Rs 5,000. So, the Bank re-configured the standard screen options – Balance Inquiry, Withdrawal, Print Statement etc. – to include another option. Withdraw XYZ amount, based on individual customer’s past actions. This ended up saving one step of ATM operation. Instead of selecting the withdrawal option and then entering the amount to be withdrawn, you could now save some time – making the process more convenient and intuitive. A smart move indeed, however, this is something known as “Easy Analytics” that others can also copy. In fact, others DID copy, within three months! A Start-Up’s Guide to Data Analytics Hidden Data in the Weather In the sample data-sets that used to accompany a spreadsheet product in the 90’s, there used to be data on the area and population of every State in the United States. There was also an exercise to teach the formula part of the spreadsheet to compute the population density (population per sq. km). New Jersey, with a population of 467 per sq. km, is the State with the highest density. While teaching a class of MBA students in New Jersey, I met an Indian student who figured out that in terms of population density, New Jersey is more crowded than India with 446 people per sq. km!  An interesting observation, although comparing a State with a Country is a bit misleading. Once again, an Easy Analytics exercise leading to a “nice” observation! Some simple data analytics exercises can be routinely done, and are made relatively easier, thanks to amazing tools: B-School Buying Behavior Decoded In a B-School in India that has a store on campus, (campus is located far from the city center) some smart students put several years of sales data of their campus store. They were excited by the phenomenal computer power and near, idiot-proof analytics software. The real surprise, however, was that eight items accounted for 85% of their annual sales. More importantly, these eight items were consumed in just six days of the year! Everyone knew that a handful of items were the only fast-moving items, but they did not know the extent (85%) or the intensity (consumption in just six days) of this. It turns out that in the first 3 days of the semester the students would stock the items for the full semester! The B-School found it sensible to request a nearby store to prop up a temporary stall for just two weeks at the beginning of the semesters and close down the Campus Store. This saved useful space and costs without causing major inconvenience to the students. A good example of Easy Analytics done with the help of a powerful tool. Top 4 Data Analytics Skills You Need to Become an Expert! The “Not So Easy” Analytics needs deep analytical understanding, tools, an ‘analytical mindset’ and some hard work. Here are two examples, one taken from way back in the 70’s and the other occurring very recently: Not-So-Easy Analytics To Fly or Not to Fly, That is the Question Long ago, the American Airlines perfected planned overbooking of airline seats, thanks to SABRE Airline Reservation system that managed every airline seat. Armed with detailed past data of ‘empty seats’ and ‘no show’ in every segment of every flight for every day through the year, and modeling airline seats as perishable commodities, the American Airlines was able to improve yield, i.e., utilization of airplane capacity. They did this through planned overbooking – selling more tickets than the number of seats, based on projected cancellations. Explore our Popular Data Science Online Certifications Executive Post Graduate Programme in Data Science from IIITB Professional Certificate Program in Data Science for Business Decision Making Master of Science in Data Science from University of Arizona Advanced Certificate Programme in Data Science from IIITB Professional Certificate Program in Data Science and Business Analytics from University of Maryland Data Science Online Certifications If indeed more passengers showed up than the actual number of seats, American Airlines would request anyone volunteering to forego travel in the specific flight, with the offer to fly them by the next flight (often free) and taking care of hotel accommodation if needed. Sometimes, they would even offer cash incentives to the volunteer to opt-out. Using sophisticated Statistical and Operational Research modeling, American Airlines would ensure that the flights went full and the actual incidents of more passengers than the full capacity, was near zero. In fact, many students would look forward to such incidents so that they could get incentives, (in fact, I would have to include myself in this list) but rarely were they rewarded!) upGrad’s Exclusive Data Science Webinar for you – Transformation & Opportunities in Analytics & Insights document.createElement('video'); https://cdn.upgrad.com/blog/jai-kapoor.mp4 What American Airlines started as an experiment has become the standard industry practice over the years. Until recently, a team of well-trained (often Ph.D. degree holders) analysts armed with access to enormous computing power, was needed for such an analytics exercise to be sustained. Now, new generation software such as the R Programming language and powerful desktop computers with significant visualization/graphics power is changing the world of data analytics really fast. Anyone who is well-trained (not necessarily requiring a Ph.D. anymore) can become a first-rate analytics professional. Top Data Science Skills You Should Learn SL. No Top Data Science Skills to Learn 1 Data Analysis Online Certification Inferential Statistics Online Certification 2 Hypothesis Testing Online Certification Logistic Regression Online Certification 3 Linear Regression Certification Linear Algebra for Analysis Online Certification Unleashing the Power of Data Analytics Our learners also read: Free Python Course with Certification Read our popular Data Science Articles Data Science Career Path: A Comprehensive Career Guide Data Science Career Growth: The Future of Work is here Why is Data Science Important? 8 Ways Data Science Brings Value to the Business Relevance of Data Science for Managers The Ultimate Data Science Cheat Sheet Every Data Scientists Should Have Top 6 Reasons Why You Should Become a Data Scientist A Day in the Life of Data Scientist: What do they do? Myth Busted: Data Science doesn’t need Coding Business Intelligence vs Data Science: What are the differences?   Cab Out of the Bag Uber is yet another example displaying how the power of data analytics can disrupt a well-established industry. Taxi-for-sure in Bangalore and Ola Cabs are similar to Uber. Together, these Taxi-App companies (using a Mobile App to hail a taxi, the status monitor the taxi, use and pay for the taxi) are trying to convince the world to move from car ownership to on-demand car usage. A simple but deep analytics exercise in the year 2008 gave such confidence to Uber that it began talking of reducing car sales by 25% by the year 2025! After building the Uber App for iPhone, the Uber founder enrolled few hundreds of taxi customers in San Francisco and few hundreds of taxi drivers in that area as well. All that the enrolled drivers had to do was to touch the Uber App whenever they were ready for a customer. Similarly, the enrolled taxi customers were requested to touch the Uber App whenever they were looking for a taxi. Thanks to the internet-connected phone (connectivity), Mobile App (user interface), GPS (taxi and end-user location) and GIS (location details), Uber could try connecting the taxi drivers and the taxi users. The real insight was that nearly 90% of the time, taxi drivers found a customer, less than 100 meters away! In the same way, nearly 90% of the time, taxi users were connected with their potential drivers in no time, not too far away. Unfortunately, till the Uber App came into existence, riders and taxi drivers had no way of knowing this information. More importantly, they both had no way of reaching each other! Once they had this information and access, a new way of taxi-hailing could be established. With back-end software to schedule taxis, payment gateway and a mobile payment mechanism, a far more superior taxi service could be established. Of course, near home, we had even better options like Taxi-for-sure trying to extend this experience even to auto rickshaws. The rest, as they say, is “history in the making!” Deep dive courses in data analytics will help prepare you for such high impact applications. It is not easy, but do remember former US President Kennedy’s words “we chose to go to the Moon not because it is easy, but because it is hard!” Get data science certification from the World’s top Universities. Learn Executive PG Programs, Advanced Certificate Programs, or Masters Programs to fast-track your career.  
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by Prof. S. Sadagopan

14 Dec'16
Launching UpGrad’s Data Analytics Roadshow – Are You Game?

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Launching UpGrad’s Data Analytics Roadshow – Are You Game?

We, at UpGrad, are excited to announce a brand new partnership with various thought leaders in the Data Analytics industry – IIIT Bangalore, Genpact, Analytics Vidhya and Gramener – to bring to you a one-of-a-kind Analytics Roadshow! As part of this roadshow, we will be conducting several back-to-back events that focus on different aspects of analytics, creating interaction points across India, to do our bit for a future ready and analytical, young workforce.  Also Read: Analytics Vidhya article on the UpGrad Data Analytics Roadshow Here is the line-up for the roadshow, to give you a better sense of what to expect: 9 webinars – These webinars (remote) will be conducted by industry experts and are aimed at increasing analytics awareness, providing a way for aspirants to interact with industry practitioners and getting their tough questions answered. 11 workshops – The workshops will be in-person events to take these interactions to the next level. These would be spread across 6 cities – Delhi, Bengaluru, Hyderabad, Chennai, Mumbai and Pune. So, if you are in any of these cities, we are looking forward to interact with you. Featured Data Science program for you: Master of Science in Data Science from from IIIT-B 2 Conclaves – These conclaves are larger events with a pre-defined agendas and time for networking. The first conclave is happening on the 17th of December in Bengaluru.  Explore our Popular Data Science Online Certifications Executive Post Graduate Programme in Data Science from IIITB Professional Certificate Program in Data Science for Business Decision Making Master of Science in Data Science from University of Arizona Advanced Certificate Programme in Data Science from IIITB Professional Certificate Program in Data Science and Business Analytics from University of Maryland Data Science Online Certifications Hackathon – Time to pull up your sleeves and showcase your nifty skills. We will be announcing the format of the event shortly. “We find that the IT in­dustry is ab­sorb­ing al­most half of all of the ana­lyt­ics jobs. Banking is the second largest, but trails at al­most one fourth of IT’s re­cruit­ing volume. It is in­ter­est­ing that data rich in­dus­tries like Retail, Energy and Insurance are trail­ing near the bot­tom, lower than even con­struc­tion or me­dia, who handle less data. Perhaps these are ripe for dis­rup­tion through ana­lyt­ics?” Our learners also read: Learn Python Online for Free Mr. S. Anand, CEO of Gramener, wonders aloud. Read our popular Data Science Articles Data Science Career Path: A Comprehensive Career Guide Data Science Career Growth: The Future of Work is here Why is Data Science Important? 8 Ways Data Science Brings Value to the Business Relevance of Data Science for Managers The Ultimate Data Science Cheat Sheet Every Data Scientists Should Have Top 6 Reasons Why You Should Become a Data Scientist A Day in the Life of Data Scientist: What do they do? Myth Busted: Data Science doesn’t need Coding Business Intelligence vs Data Science: What are the differences? upGrad’s Exclusive Data Science Webinar for you – Watch our Webinar on The Future of Consumer Data in an Open Data Economy document.createElement('video'); https://cdn.upgrad.com/blog/sashi-edupuganti.mp4   Top Data Science Skills You Should Learn SL. No Top Data Science Skills to Learn 1 Data Analysis Online Certification Inferential Statistics Online Certification 2 Hypothesis Testing Online Certification Logistic Regression Online Certification 3 Linear Regression Certification Linear Algebra for Analysis Online Certification Get data science certification from the World’s top Universities. Learn Executive PG Programs, Advanced Certificate Programs, or Masters Programs to fast-track your career.
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by Apoorva Shankar

15 Dec'16
What’s Cooking in Data Analytics? Team Data at UpGrad Speaks Up!

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What’s Cooking in Data Analytics? Team Data at UpGrad Speaks Up!

Team Data Analytics is creating the most immersive learning experience for working professionals at UpGrad. Data Insider recently checked in to me to get my insights on the data analytics industry; including trends to watch out for and must-have skill sets for today’s developers. Here’s how it went: How competitive is the data analytics industry today? What is the demand for these types of professionals? Let’s talk some numbers, a widely-quoted McKinsey report states that the United States will face an acute shortage of around 1.5 million data professionals by 2018. In India, which is emerging as the global analytics hub, the shortage of such professionals could go up to as high as 200,000. In India alone, the number of analytics jobs saw a 120 percent rise from June 2015 to June 2016. So, we clearly have a challenge set out for us. Naturally, because of acute talent shortage, talented professionals are high in demand. Decoding Easy vs. Not-So-Easy Analytics What trends are you following in the data analytics industry today? Why are you interested in them? There are three key trends that we should watch out for: Personalization I think the usage of data to create personalized systems is a key trend being adopted extremely fast, across the board. Most of the internet services are removing the anonymity of online users and moving towards differentiated treatment. For example, words recommendations when you are typing your messages or destinations recommendations when you are using Uber. Our learners also read: Learn Python Online for Free End of Moore’s Law Another interesting trend to watch out for is how companies are getting more and more creative as we reach the end of Moore’s Law. Moore’s Law essentially states that every two years we will be able to fit double the number of transistors that could be fit on a chip, two years ago. Because of this law, we have unleashed the power of storing and processing huge amounts of data, responsible for the entire data revolution. But what will happen next? IoT Another trend to watch out for, for the sheer possibilities it brings. It’s the emergence of smart systems which is made possible by the coming together of cloud, big data, and IoT (internet of things). Explore our Popular Data Science Courses Executive Post Graduate Programme in Data Science from IIITB Professional Certificate Program in Data Science for Business Decision Making Master of Science in Data Science from University of Arizona Advanced Certificate Programme in Data Science from IIITB Professional Certificate Program in Data Science and Business Analytics from University of Maryland Data Science Courses What skill sets are critical for data engineers today? What do they need to know to stay competitive? A good data scientist sits at a rare overlap of three areas: Domain Knowledge This helps understand and appreciate the nuances of a business problem. For e.g, an e-commerce company would want to recommend complementary products to its buyers. Statistical Knowledge Statistical and mathematical knowledge help to inform data-driven decision making. For instance, one can use market basket analysis to come up with complementary products for a particular buy. Technical Knowledge This helps perform complex analysis at scale; such as creating a recommendation system that shows that a buyer might prefer to also buy a pen while buying a notebook. How Can You Transition to Data Analytics? Outside of their technical expertise, what other skills should those in data analytics and business intelligence be sure to develop? Ultimately, data scientists are problem solvers. And every problem has a specific context, content and story behind it. This is where it becomes extremely important to tie all these factors together – into a common narrative. Essentially all data professionals need to be great storytellers. In this respect, one of the key skills for analysts to sharpen would be, breaking down the complexities of analytics for others working with them. They can appreciate the actual insights derived – and work toward a common business goal. In addition, what is as crucial is getting into a habit of constantly learning. Even if it means waking up every morning and reading what’s relevant and current in your domain. Top Essential Data Science Skills to Learn SL. No Top Data Science Skills to Learn 1 Data Analysis Certifications Inferential Statistics Certifications 2 Hypothesis Testing Certifications Logistic Regression Certifications 3 Linear Regression Certifications Linear Algebra for Analysis Certifications What should these professionals be doing to stay ahead of trends and innovations in the field? Professionals these days need to continuously upskill themselves and be willing to unlearn and relearn. The world of work and the industrial landscape of technology-heavy fields such as data analytics is changing every year. The only way to stay ahead, or even at par with these trends, is to invest in learning, taking up exciting industry-relevant projects, participating in competitions like Kaggle, etc. How important is mentorship in the data industry? Who can professionals look toward to help further their careers and their skills? Extremely important. Considering how fast this domain has emerged, academia and universities, in general, have not had the chance to keep up equally fast. Hence, the only way to stay industry-relevant with respect to this domain is to have industry-specific learning. This can only be done in two ways – through real-life case studies and mentors who are working/senior professionals and hail from the data analytics industry. In fact, at UpGrad, there is a lot of stress on industry mentorship for aspiring data specialists. This is in addition to a whole host of case studies and industry-relevant projects. Get data science certification from the World’s top Universities. Learn Executive PG Programs, Advanced Certificate Programs, or Masters Programs to fast-track your career. Read our popular Data Science Articles Data Science Career Path: A Comprehensive Career Guide Data Science Career Growth: The Future of Work is here Why is Data Science Important? 8 Ways Data Science Brings Value to the Business Relevance of Data Science for Managers The Ultimate Data Science Cheat Sheet Every Data Scientists Should Have Top 6 Reasons Why You Should Become a Data Scientist A Day in the Life of Data Scientist: What do they do? Myth Busted: Data Science doesn’t need Coding Business Intelligence vs Data Science: What are the differences?   Where are the best places for data professionals to find mentors? upGrad’s Exclusive Data Science Webinar for you – Transformation & Opportunities in Analytics & Insights document.createElement('video'); https://cdn.upgrad.com/blog/jai-kapoor.mp4 While it’s important for budding or aspiring data professionals to tap into their networks to find the right mentors, it is admittedly tough to do so. There are two main reasons that can be blamed for this. First, due to the nascent stage, the industry is at, it is extremely difficult to find someone with the requisite skill sets to be a mentor. Even if you find someone with considerable experience in the field, not everybody has the time and inclination to be an effective mentor. Hence most people don’t know where to go to be mentored. That’s where platforms like UpGrad come in, which provide you with a rich, industry-relevant learning experience. Nowhere else are you likely to chance upon such a wide range of industry tie-ups or associations for mentorship from very senior and reputed professionals. How Can You Transition to Data Analytics? What resources should those in the data analytics industry be using to ensure they’re educated and up-to-date on developments, trends, and skills? There are many. For starters, here are some good and pretty interesting blogs and resources that would serve aspiring/current data analysts well to keep up with Podcasts like Data Skeptic, Freakonomics, Talking Machines, and much more.   This interview was originally published on Data Insider.  
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by Rohit Sharma

23 Dec'16