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16 Best Data Science Project Ideas & Topics for Beginners [2024]

Updated on 25 October, 2024

965.24K+ views
32 min read

Summary:

In this Article, you will learn about 16 exciting data science project ideas & topics for beginners.

1. Beginner Level | Data Science Project Ideas

  • Fake News Detection
  • Human Action Recognition
  • Forest Fire Prediction
  • Road Lane Line Detection

2. Data Science Projects Ideas | Intermediate Level

  • Recognition of Speech Emotion
  • Gender and Age Detection with Data Science
  • Driver Drowsiness Detection in Python
  • Chatbots
  • Handwritten Digit & Character Recognition Project

3. Advance Level Data Science Projects Ideas

  • Credit Card Fraud Detection Project
  • Customer Segmentations
  • Traffic Signs Recognition

4. Top Data Analytics Projects

  • Web Scraping
  • Data Cleaning
  • Exploratory Data Analysis
  • Sentiment Analysis

Read more to know each in detail.

Best Data Science Project Ideas

We have segmented all the Data Science Project Ideas with source code as per the learner’s level. Therefore, you will get a list of a few amazing project briefs for beginner, intermediate & advanced Data Science project ideas.

Our learners also read: Free excel courses!

1. Beginner Level | Data Science Project Ideas

This list of data science project ideas for college students is suited for beginners, and those just starting out with Python or Data Science in general. These data science project ideas will get you going with all the practicalities you need to succeed in your career as a data science developer.

Must read: Data structures and algorithms free course!

Further, if you’re looking for data science project ideas for final year, this list should get you going. So, without further ado, let’s jump straight into some data science project ideas that will strengthen your base and allow you to climb up the ladder.

1.1 Climate Change Impacts on the Global Food Supply

The first one to make it to the list of data science projects for beginners is climate change impacts on the global food supply.

Frequent Climate change and irregularities are big challenging environmental issues. These irregularities in climate divisions are drastically affecting the human lives residing on the Earth. This Data Science Project concentrates on how the climate impact will highly affect global food production worldwide and how much quantification will impact climate change.  

The main aim of development for this project is to calculate the potentialities on the staple crop productions due to climate change. Through this project, all the implications related to temperatures & precipitation change. It will then be taken into account how much carbon dioxide affects the growth of plants and the uncertainties happening in the climatic conditioning. Hence, this project will largely deal with Data Visualisations. It will also compare the production in various regions at different time zones. 

Source Code: Climate Change Impacts on the Global Food Supply

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1.2 Fake News Detection

Source

You can drive your Data Science career with this amazing Data Science Project idea for beginners – Detection of Fake News using Python language. The act of wrong or misleading journalism on a digital platform or fake news can be detected by this project. Falsifications are spreading out via social media platforms and online channels & digital media to attain any political agenda. 

With this data science project idea, you can use Python language to develop a specific model that can precisely detect whether the news is real journalism or false information.. For this, you need to build a ‘TfidfVectorizer’ classifier and then use a ‘PassiveAggressiveClassifier’ to classify the news into either a “Real” and “Fake” segmentations. There will be a dataset of the shape of 7796×4 dimensions and execute all these in the ‘JupyterLab’.

The main idea of this Data Science project is to develop a real-time machine learning model that can correctly detect social media news authenticity. ‘TF’, commonly known as ‘Term Frequency’, is the total number of times any word will appear in a single document. Whereas, ‘IDF’ or ‘Inverse Document Frequency’ is a calculative measure of the value of a word & it is based on the reputational frequency of its occurrence appearing in the various documents.  

The theory is on the ‘Common words’, if these common words happen to appear in multiple documents with a high frequency then they are considered as less important words. So, what ‘TFIDFVectorizer’ does is to analyze the collection of these documents and then accordingly create a ‘TF-IDF’ matrix to it. 

Along with this, a ‘PassiveAggressive’ classifier will remain ‘passive’ in case the ‘classification outcome’ is correct; but on the other hand, it will change aggressively if the ‘classification outcome’ is incorrect. So, you can create a machine learning model to detect social media news to be genuine or fake news using this Data Science Project idea.

Source Code: Fake News Detection

1.3 Human Action Recognition

This is a Data Science project on the human action recognition model. It will look at the short videos made on human beings where they are performing specific actions. This model tries to do a classification that is based on actions performed. In this Data science project, you need to use a complex neural network. This neural network is then trained on a specific dataset that contains these short videos. Then there is an accelerometer data that is associated with the dataset. The accelerometer data conversion is done first along with a ‘time-sliced’ representation. Thereafter, you have to use the ‘Keras’ library so that you can do training, validation, and testing of the network based on these datasets.

Source Code: Human Action Recognition

1.4 Forest Fire Prediction

One of the alarming & common disasters happening in today’s world is forest fires. These disasters are highly damaging to the ecosystem. To deal with such a disaster, a lot of money on infrastructure & controlling and handling is required. We can build a Data Science project using ‘k-means clustering’- it can identify any forest fires hotspots along with the severity of the fire at that particular spot.

It can be alternatively used for better resource allocation with the faster response time. Hence, using the meteorological data such as those seasons around which these kinds of fires tragedies are more likely to happen and various weather conditions that worsen them may increase these results’ accuracy levels.

Source Code: Forest Fire Prediction

1.5 Road Lane Line Detection

Another Data Science project ideas for beginners include a Live Lane-Line Detection Systems built-in Python language. In this project, a human driver receives guidance on lane detections through lines drawn on the road.

Not only this, it further refers to which direction the driver should steer their vehicle. This Data Science Project application is vital for the development of driverless cars. Hence, you can also develop an application with the powerful capability to identify a track line through the input images or via a continuous video frame.

Source Code: Road Lane Line Detection

Read: Top 4 Data Analytics Project Ideas: Beginner to Expert Level

2. Data Science Projects Ideas |Intermediate Level

2.1 Recognition of Speech Emotion 

Source

One of the popular Data Science project ideas is recognition of the speech emotion. If you want to learn the usage of different libraries, this project is perfect for you. You must have seen a lot of editor tools that can tell us how our speech emotion is appearing. This program model can be built as a Data Science project.

In this Data Science project, we will use ‘librosa’ that will perform a ‘Speech Emotion Recognition’ for us. The SER process is a trial process that can recognize human emotion. It can also recognise the speech from the affective states. As we use a combination of a tone and a pitch for expressing emotions through our voice.

The Speech Emotion Recognition model is absolutely possible. However, it can be a challenging project to perform as human emotions are very subjective. The annotation of the human audio is also quite challenging. So, here you will use the mfcc, mel & the chroma features. With this, you will also use the dataset known as ‘RAVDESS’ for the emotion recognition process. In this Data Science project, you will also learn how to develop an ‘MLPClassifier’ for this model.

Source Code: Recognition of Speech Emotion

2.2 Gender and Age Detection with Data Science

Source

So, one of the impressive project ideas on Data Science is the ‘Gender and Age Detection with OpenCV’. With this kind of real-time project, you can easily grab your recruiter’s attention in a Data Science interview.

Talking about the project, the ‘Gender and Age Detection’ is a machine learning project based on computer visioning. Through this Data Science Project, you can learn the practical application of CNN i.e, the convolutional neural networks. Down the line, you will also use models that are trained by ‘Tal Hassner’ and ‘Gil Levi’ for ‘Adience’ dataset.

Along with this, you will also use some files such as – .pb, .prototxt, .pbtxt, & .caffemodel files. Heard about these terms? Read about these files? Understand models too? But do you know how to implement them? Well, you can learn it if you opt to develop a Data Science Project on it. 

It’s a very practical project as you will create a model that can detect any human being’s age & gender through analyses of single face detection via an image. So, with this gender classification in a man or a woman can be classified. Also, the age can be classified among the ranges of 0-2/ 4-6/ 8- 2/ 15-20/ 25-32/ 38-43/ 48-53/ 60-100. 

But due to various factors such as makeup, or brighter dim lighting, or an unusual facial expression, the recognition of the gender and the age from a single source can become challenging. Therefore, in this Data Science project, you will use a classification model instead of a regression model. A lot of practical & technical learning can be grabbed to upscale your technical skills with these kinds of projects. So, take up the challenge & work hard towards it to make an impressive Data Science Resume.

Source Code: Gender and Age Detection with Data Science

2.3 Driver Drowsiness Detection in Python

An excellent Data Science project idea for intermediate levels is the ‘Keras & OpenCV Drowsiness Detection System’. Driving overnight is not only tough but a risky job too. We have heard of a lot of cases where accidents happen because the driver fell asleep while driving.

Thus, this project can help prevent numerous road accidents that happen due to such cases. This project’s main aim is to recognize whenever the driver may get drowsy & fall asleep while driving. This project uses Python language where you can build a model that can timely detect the sleepy driver behavior and raises an alert alarm through a high beeping alarm.

In this project, you can implement a ‘deep learning model’ & with its use, you can do a classification among images where a human eye is open or close. Not just this, in this model another formula line is to calculate the score.

This score is based on the time period of how long the eyes remain closed. The score is maintained throughout the driving session. If that score increases & crosses a specified threshold, this model will throw workflow automation through which the alarm will start buzzing heavily.

So, with these kinds of Data Science projects implementations, you will learn all the basics of Data Science projects. You will implement it using ‘Keras’ and ‘OpenCV’. So, why are these used? Well, you are using ‘OpenCV’ to detect face & eye movements. Whereas, with ‘Keras’, you can classify the eye’s state whether it is open or close while using techniques of the Deep neural network.

Source Code: Driver Drowsiness Detection in Python

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2.4 Chatbots 

Source

Chatbots are increasingly becoming popular these days. So, for a Data Science project, it is a high on-demand requirement by almost all organizations. It is an essential segment of the business nowadays. These days, chatbots are playing a very crucial role in businesses. They are helping business lines to save an enormous amount of time on their human resources. It is used to provide an improved and personalized business service simultaneously.

There are many businesses who are offering services to their customers. To provide customer service on a large scale, it requires a lot of human resources, ample time, and many efforts to handle each customer on time. On the other hand, these chatbots can provide automation for customer interaction services simply by answering a set of frequent questions commonly inquired by the customers. 

There are 2 types of chatbots available in today’s time: Domain-specific chatbot and Open-domain chatbot. The domain-specific chatbot is most often used for a particular problem solution. These are customized in a very strategic & smart manner so that they work strategically & effectively in relation to domain specifications. The second one, ‘Open-domain’ chatbots, needs a lot of training materials that are too continuously because, as per the name, it is developed to answer any kind of question.

Technically speaking, the chatbots are trained using the ‘Deep Learning’ techniques. They need a dataset with vocabulary listing, lists consisting of a common sentence, an intent which is behind them, and then the appropriate responses. This is one of the trending data science project ideas. 

The ‘Recurring Neural Networks’ (The RNN’s) are the common methodologies to train chatbots. These bots contain encoders that can update the states as per the input sentences alongside intent. It then passes the specified state to the Chatbot.

Thereafter, the chatbot uses the decoder to search an appropriate & subsequent response according to inputted words & also besides the intent. With this Data Science project, you can easily learn Python language implementation as the complete project is itself made in Python. You can upscale your Python technical skills to a certain extent.

Source Code: Chatbots

Learn: How to Make a Chatbot in Python Step By Step

2.5 Handwritten Digit & Character Recognition Project

Source

With this Data Science Project idea on ‘Handwritten Digit & Character Recognition with the help of CNN, you will practically learn Deep Learning concepts. So, if you are a budding Data Scientist or an enthusiast of machine learning then this is the perfect Data Science project idea for you. For this project development, you will use the ‘MNIST dataset’ of hand-written digits. This is a great project to get hands-on experience with Data Science as you will learn amazing ways that are involved in the process of project building. 

As discussed, this project is implemented through the ‘Convolutional Neural Networks’. After this, for a real-time prediction, you will build a creative graphical- based user interface for drawing digits on the canvas, and thereafter you will build a model that will be used for the prediction of the digits.

The project’s focus is on developing the computer’s ability & to empower the computer system so that it can recognize characters in hand-written formats by humans. It will then evaluate it further to understand it with reasonable accuracy. With this project implementation, you can learn the practical implementation of the ‘Keras’ and also ‘Tkinter’ libraries.

These are some intermediate data science project ideas on which you can work. If you still like to test your knowledge and take on some tough projects.

Source Code: Handwritten Digit & Character Recognition Project

3. Advance Level Data Science Projects Ideas

3.1 Credit Card Fraud Detection Project

Source

After implementing easy projects, you can now move to some advanced Data Science project ideas to learn more concepts. One such idea is Credit card Fraud Detection. With this project, you will learn how to use the R with different algorithms such as Decision Tree, Artificial Neural Networks, Logistic Regression, and the Gradient Boosting Classifier.

You can also learn to use the ‘Card Transactions’ datasets to classify the credit card transaction as a fraudulent activity or a genuine transaction. You will also learn to fit all the different types of models along with the plot performance curve for all of them. This is one of the best data science project ideas one can find. 

Source Code: Credit Card Fraud Detection Project

3.2 Customer Segmentations

Source

This is one of the most popular Data Science projects in the field of Data Science. Digital Marketing is an up & advanced way to target an audience for the companies through their online marketing activities for marketing purposes nowadays. So before running a marketing campaign, different customer segmentation is first done.

Customer Segmentation is among very popular applications of indeed unsupervised learning. Hereby, using clustering methods, companies can now easily identify the customers’ various segments for targeting the potential user-base. There are divisions made on customers & groups are formed according to the common characteristics such as gender, interest areas, age, and habits.

Based on these details they can effectively market each customer group. The project uses the ‘K-means clustering’ and you will learn how to perform visualizations on distributions such as gender and age. Customers annual incomes & average score values can also be analysed.

Source Code: Customer Segmentations

3.3 Traffic Signs Recognition

Source 

This project aims to develop a model to achieve high accuracy in self-driving car technologies using CNN techniques. Traffic signs and traffic rules are of utmost importance for every driver and it must be followed to avoid accidents. To follow these rules, the user must understand how the traffic signals appear to be. 

It’s a general rule that to obtain a driving license, an individual has to learn all the driving signals. But for autonomous vehicles, there are programs developed such as the ‘Traffic signs recognition’ using CNN, where you can learn how to program a model that can precisely identify various kinds of traffic signals by the input of an image.

There is a dataset called the ‘German Traffic signs recognition benchmark’. It is commonly known as the GTSRB that is used in the development of a Deep Neural Network for recognizing the class of all the traffic signs belonging to which class type. You will also learn practical knowledge of building a GUI for application interaction.

Source Code: Traffic Signs Recognition

Know more: 10 Exciting Python GUI Projects & Topics For Beginners

To find a data science project, consider identifying a problem or question that interests you, locate relevant datasets, and leverage various tools and techniques to analyze the data and derive insights. Online platforms like Kaggle, data repositories, or collaborating with organizations can offer opportunities to work on real-world projects.

Top Data Analytics Projects

Now that you have learned some of the best data science project topics, let’s take a look at some of the top data analytics projects ideas and data science topics that are currently trending in the market. Data analytics projects span a wide range of industries and applications, each with its unique challenges and insights. Here are some top data science projects for beginners that showcase the diversity and impact of data analysis:

  • Customer Segmentation for E-commerce: Analyze customer behavior and purchasing patterns to segment customers based on preferences, demographics, and buying habits. This segmentation can help tailor marketing strategies, improve product recommendations, and enhance customer experiences.
  • Predictive Maintenance in Manufacturing: Utilize sensor data from machinery to predict maintenance needs and prevent unplanned downtime. This can optimize maintenance schedules, reduce costs, and enhance production efficiency.
  • Healthcare Fraud Detection: Analyze medical claims data to identify patterns indicative of fraudulent activities. Building predictive models can help healthcare providers and insurers detect fraudulent claims and mitigate financial losses.
  • Energy Consumption Optimization: Analyze energy usage patterns in buildings to identify opportunities for energy efficiency improvements. This can lead to reduced energy costs and a smaller carbon footprint.
  • Financial Portfolio Optimization: Analyze historical financial data to optimize investment portfolios. Applying techniques like Modern Portfolio Theory can help investors balance risk and return.
  • Traffic Pattern Analysis: Analyze traffic data to understand congestion patterns, optimize traffic flow, and improve urban planning for transportation infrastructure.
  • Predicting Disease Outbreaks: Analyze health data and historical disease outbreaks to build predictive models that can forecast and mitigate the spread of diseases.
  • Real Estate Market Analysis: Analyze real estate data to identify trends, forecast property values, and assist buyers, sellers, and investors in making informed decisions.

1. Web Scraping

Knowing how to scrape data not only adds that boost to your portfolio, but also with the help of this, you can actually explore and use data sets that match with your interests, without the need for compiling the same. Various tools like Beautiful Soup or Scrapy are actually available with the help of which you can crawl the web for interesting data. 

Source Code: Web Scraping

2. Data Cleaning

One of the most important tasks for every data analyst is cleaning data to make it ready to analyze. Data cleaning, also called data scrubbing is basically ensuring that the data is consistent, by removing any duplicate or incorrect data and managing the holes in the data. This is one of the best data science topics that is boun dto add value to your candidature. 

Source Code: Data Cleaning

3. Exploratory Data Analysis

To put it simply, data analysis is all about answering questions with data. With the help of EDA, you can explore different questions that you want to ask. 

Source Code: Exploratory Data Analysis

4. Sentiment Analysis

Last but not least is sentiment analysis, which is basically a technique in natural language processing that determines whether the data is neutral, positive, or negative. They are especially useful for public review sites and social media platforms. Furthermore, with the help of sentiment analysis, you can also detect a particular emotion based on the list of words, and their corresponding emotions. This is known as a lexicon. 

Source Code: Sentiment Analysis

An Expression on Data Science Project Ideas

Data Science is continuously thriving as a great career option for this generation. It is among the most promising & happening choices altogether. The market is boosting up with more demands for Data Scientists. It has been reported recently that the demand will increase further to many folds in the coming years. So, if you are a data science beginner, the best thing you can do is work on some real-time data science project ideas.

You can also check out our free courses offered by upGrad under Data Science.

So, if you are an aspiring Data Scientist, it is highly recommended to practice skills to become an efficient professional for this field. After grabbing some very good theoretical knowledge on Data Science, if you are really looking ahead to explore what it seems like to be a professional, then now is the time to do some practical projects.

You must do some of the technical & real-time Data Science projects so that it helps you boost your career growth. The more you practice with Data Science projects, we assure you that you can keep up the pace towards becoming a sound Data Scientist professional.

Check out our Python Bootcamp created for working professionals.

Therefore, if you do some live Data Science Projects, it will enhance your knowledge, technical skills, and overall confidence. But most importantly, if you showcase even a few Data Science projects in your resume, then getting a good job is much easier for you. Why so? Because then the interviewer will know that you are really serious about a Data Science career.

Your real-time experience on Live Data Science Projectswill let you hold a strong grip on Data Science trends & technologies. So, layout your hands on real-time Data Science projects & you will know how beneficial it will be for your speedy career growth. After all these discussions, we know that finding that perfect Data Science Project ideafor your Data Science project concerns you even more than its actual implementation.

Our learners also read: Python online course free!

In this Data Science blog, we have listed out the names of a few Data Science Project ideas. And to answer your question – ‘What kind of Data Science project is good to start with?’, we have compiled a few good Data Science Project ideas for you to choose from.

The article also includes some of the best data science projects for beginners, that you can check out. 

No Coding Experience Required. 360° Career support. PG Diploma in Machine Learning & AI from IIIT-B and upGrad.

Data Science is a versatile discipline with a mix of various data science research topics and projects on data science like, statistics, mathematics, computer science, etc, to unearth meaningful insights from data. However, the process involves gathering, refining, scrutinizing, and interpreting extensive datasets to unveil patterns, trends, and correlations, facilitating informed data-driven decisions. 

Data Scientists employ an array of tools and techniques, including machine learning, data visualization, and predictive modeling, to extract valuable insights that propel business growth. By amalgamating diverse skills, data science projects or data science topics for project forms an important bridge between raw data and actionable insights, fostering a deeper understanding of complex datasets and empowering organizations to make strategic decisions based on empirical evidence.

What is the Future Demand for Data Science?

  • Sustained Growth in Industries

The future demand for data science projects or data science project ideas remains robust as industries across the spectrum increasingly rely on data-driven decision-making. From healthcare and finance to technology, the pervasive influence of data science project ideas 2023 is expected to grow, creating a sustained demand for skilled professionals.

  • Evolving Technological Landscape

As technology continues to advance, so does the demand for data science expertise or data science project ideas 2023. The artificial intelligence, machine learning, and big data technologies present new opportunities for data scientists while doing data science projects for final year to harness and interpret wide amounts of data, providing valuable insights that drive innovation and competitiveness.

  • Integration with AI and Automation

The integration of Data Science along with artificial intelligence (AI) and automation further fuels demand. Organizations are seeking data scientists after doing data science projects for final year to develop algorithms, machine learning models, and automation solutions that optimize processes, enhance efficiency, and contribute to strategic decision-making.

  • Emerging Fields and Specializations

Data science projects or data science research topics is branching into specialized fields such as data engineering, natural language processing, and computer vision. As these domains gain prominence, the demand for topics for data analysis project or data science projects for beginners and professionals with niche expertise is expected to rise. The diversification of roles within the broader field of data science projects or data science project ideas contributes to a nuanced demand landscape.

  • Enhanced Business Intelligence

In an era where data is often hailed as the new currency, businesses are increasingly recognizing the pivotal role of Data Science in gaining a competitive edge. The ability to transform raw data into actionable insights enhances business intelligence, enabling companies to make informed decisions, understand customer behavior, and adapt to market trends swiftly.

  • Global Adoption and Data Privacy

As data science projects or data science topics for project becomes a global phenomenon, challenges related to data privacy and security emerge. The demand for professionals and topics for data analysis project well-versed in ethical data practices and regulatory compliance is on the rise. Data scientists who can navigate these challenges in the form of data engineer projects while extracting valuable insights will be in high demand, ensuring the responsible and effective use of data.

Why is Data Science a Very Attractive Career Opportunity?

Embarking on a career as a data scientist and looking for project ideas for data analytics or data engineer projects isn’t just visually appealing from the outside; it also offers an engaging and rewarding journey within. Let’s delve into the various perks that make this career path stand out while getting into project ideas for data analytics.

  • Freedom

One of the foremost perks that data scientists revel in is the freedom to choose their data science projects for beginners and technologies. Unlike being confined to a specific industry, data scientists can navigate diverse realms, especially those brimming with enormous potential. This liberty fosters a dynamic work environment, keeping the profession consistently invigorating.

  • Working with Reputed Organizations

The marriage of data science projects with artificial intelligence and machine learning opens doors to collaborations with industry behemoths such as Uber, Apple, and Amazon. The sheer volume of data, or “big data,” stored by these global corporations ensures an enriching experience for data scientists, contributing to the enhancement of user interactions and overall business strategies.

  • Rewarding Salary

The financial allure of a data science projects or python data science projects is undeniable. With a median salary exceeding $120,000, data scientists are handsomely rewarded for the value they bring to organizations. This substantial remuneration cements data science as one of the most attractive and best career options.

  • In-Demand Skills

In a tech-driven era, the demand for data scientists is soaring, with a growth rate surpassing 100% annually. As predicted by IBM in 2018, this trend continues unabated. The skill set of data scientists remains in high demand, aligning with the ever-evolving technological landscape.

  • Stable Career Option

Unlike transient sectors in the corporate landscape, data science stands out as a stable career option. While industries may rise and fall, the relevance and growth trajectory of data science remains steadfast. With the integration of artificial intelligence as a driving force, big data, and consequently, data science, are poised for sustained significance.

  • Entrepreneurial Opportunities

A unique advantage for seasoned data scientists lies in the potential to venture into entrepreneurship. This means candidates full of comprehensive industry knowledge data scientists can seamlessly transition into business ownership. This entrepreneurial journey could manifest in ventures within the data science and big data domain or even branch into specific industries they’ve previously engaged with, such as e-commerce or video streaming platforms.

Skills Needed to Become a Data Science Professional

  • Technical Proficiency

At the core of a data science professional’s skill set lies technical proficiency. This includes mastery of programming languages like Python or R, as well as a strong command of statistical analysis and data manipulation. A solid foundation in these technical aspects empowers professionals to effectively navigate and manipulate datasets.

  • Data Visualization

The ability to translate complex data into visually understandable insights is a crucial skill. Data visualization tools like Tableau or Matplotlib help professionals while doing data analysis project ideas for students create compelling visuals that convey patterns and trends. This skill not only aids in the interpretation of data but also enhances communication with stakeholders by presenting findings in a clear and impactful manner.

  • Machine Learning Expertise

Proficiency in ML algorithms and techniques is imperative for a data science professional. Understanding supervised and unsupervised learning, regression, and classification algorithms equips individuals to apply predictive analytics, extract meaningful patterns, and make informed decisions based on data-driven models.

  • Domain Knowledge

Beyond technical understanding, a data scientist benefits greatly from domain knowledge. Understanding the industry or field in which they operate allows professionals to contextualize data findings. This bridge between data and industry insights enhances the relevance and impact of their analyses, facilitating more informed decision-making.

  • Problem-Solving Skills

Data science is inherently about solving problems, and strong problem-solving skills are a cornerstone of success. Professionals need to approach data challenges with a logical mindset, breaking down complex issues into manageable components. This skill ensures effective troubleshooting and the development of innovative solutions.

  • Continuous Learning

In the ever-evolving landscape of data science, a commitment to continuous learning is essential. Professionals should stay abreast of emerging technologies, tools, and methodologies. This proactive approach not only keeps their skills relevant but also positions them to leverage the latest advancements, contributing to their effectiveness as data science practitioners.

To conclude, becoming a proficient data science professional requires a combination of technical mastery, data visualization skills, machine learning expertise, industry-specific knowledge, problem-solving capabilities, and a commitment to continuous learning. This comprehensive skill set equips professionals to navigate the complexities of data analysis and contribute meaningfully to decision-making processes in a rapidly advancing field.

 

 

Why Should You Learn Data Science?

Before going further into the different data science project ideas that are available, let’s take a look at some of the reasons why data science projects are considered to be so important in today’s world. 

1. Data is the new driving force behind industries

Needless to say, in today’s technology-driven world, large enterprises across different industries rely heavily on data for everything, starting with their business growth to expansion. Thus, it wouldn’t be too wrong to say that data is the electricity that powers all the industries of today.

Industries make use of data to improve their performance, generate revenue, and provide better customer service. Infact, the automobile industry, too, is harnessing the power of data to improve the safety of their vehicles. Their goal is to create powerful machines that think in the form of data. 

2. Demand And Supply

Although there is a huge abundance of data, there are not enough resources available that can convert this data into powerful products. This basically means that there is still a huge dent in the data scientists, because of a lack of data literacy in the market. 

3. High Paying Job Opportunities

Currently, data science is considered a highly lucrative career. Infact, according to some researchers, a data scientist makes 63% more than the national average salary. Apart from this, data scientists also get to enjoy a position of prestige in the company. This is because companies rely heavily on data scientists to make data-driven decisions and guide the organization in the right direction. 

4. Data Science is the next big thing

As more and more industries are becoming data-driven, there is a constant need for data scientists. The field of technology is becoming more dynamic and new innovations are being made every day. Thus, data science is the career of the future.

Here are 50 Data Science Project ideas for you, and in the blog ahead, we are discussing a few of these projects in detail. So let’s begin!

  1. Chatbot
  2. Analyzing the impact of climate change on global food supply
  3. Weather Prediction
  4. Keyword generation for google ads
  5. Traffic Signs Recognition
  6. Wine Quality Analysis
  7. Stock Market Prediction
  8. Fake News Detection
  9. Video Classification
  10. Human Action Recognition
  11. Medical Report Generation using CT Scans
  12. Email Classification
  13. Uber Data Analysis
  14. Sound Classification
  15. Credit Card Fraud Detection
  16. Sign Language Recognition
  17. Class of Flower Prediction
  18. Colour Detection
  19. Loan Prediction
  20. Road Traffic Prediction
  21. Income Classification
  22. Speech Emotion Recognition
  23. Celebrity Voice Prediction
  24. Store Sales Prediction
  25. Detecting Parkinson’s Disease
  26. Air Pollution Prediction
  27. Age and Gender Detection
  28. Optimizing Product Price 
  29. IMDB Predictions
  30. Handwritten Digit Recognition
  31. Quora Insincere Questions Classification
  32. Driver Drowsiness Detection 
  33. Web Traffic Time Series Forecasting
  34. Survival Prediction on the Titanic
  35. Time Series Modelling
  36. Image Caption Generator
  37. Insurance Purchase Prediction
  38. Crime Analysis
  39. Customer Segmentation
  40. Taxi Trip Time Prediction
  41. Job Recommendation System
  42. Boston Housing Predictions
  43. Sentiment Analysis
  44. Interest Level in Rental Properties
  45. Keyword generation for Google Ads
  46. Breast Cancer Classification
  47. Employee Computer Access Needs
  48. Tweets Classification
  49. Movie Recommendation System
  50. Product Price Suggestions

Also, check out our business analytics course to widen your horizon.

Data analytics projects for final-year students

Here are some data science project ideas for final year students:

  • Predictive Modeling for Student Performance: Analyze historical academic data to predict student performance based on various factors like attendance, study habits, socioeconomic background, etc.
  • Customer Segmentation for E-commerce: Cluster customers based on their purchasing behavior and demographics to provide targeted marketing strategies.
  • Movie Recommendation System: Build a recommendation system that suggests movies to users based on their viewing history and preferences.
  • Healthcare Analytics: Analyze patient records to identify trends, predict disease outbreaks, or assess the impact of different treatments.
  • Social Media Sentiment Analysis: Analyze sentiment on social media platforms regarding a specific topic, brand, or event.
  • Predicting Stock Prices: Use historical stock data to build a model that predicts future stock prices.
  • Energy Consumption Analysis: Analyze energy consumption patterns in a specific region and suggest strategies for more efficient energy use.
  • Crime Pattern Analysis: Analyze crime data to identify patterns and trends in criminal activities for better resource allocation in law enforcement.
  • Sports Analytics: Analyze player performance, team strategies, and historical game data to gain insights into sports dynamics.
  • Real Estate Market Analysis: Analyze housing market data to predict property values, identify investment opportunities, or understand market trends. 

Bottom Line

In this article, we have covered top data science project ideas. We started with some beginner projects which you can solve with ease. Once you finish with these simple data science projects, I suggest you go back, learn a few more concepts and then try the intermediate projects.

When you feel confident, you can then tackle the advanced projects. If you wish to improve your data science skills, you need to get your hands on these data science project ideas. Now go ahead and put to test all the knowledge that you’ve gathered through our data science project ideas guide to build your very own data science project!

We wish that you will drastically improve all the skills of Data Science with the project ideas we presented to you here in this blog. But in case you are new to the Data Science field & would love to learn the Data Science & build similar models for the technological advancements, we recommend you to check out the online course on upGrad & IIIT-B’s PG Diploma programs to learn & upskill in the Data Science world with experienced & expert professionals.

With the right set of knowledge, guidance & tools, you can learn any Data Science project. No level is difficult for learners. That’s why all these live projects are a perfect way to enhance one’s skills and fast progress in attaining mastery. At upGrad, we offer 3 Data Science Online Certification:

1. Executive PG Programme in Data Science (12 months)

From IIIT Bangalore

2. Master of Science in Data Science (18 months)

From Liverpool John Moores University

3. Advanced Certificate Programme in Data Science (7 months)

From IIIT Bangalore

Try these Data science online certifications by upGrad as we are sure that they will help you in your Data Science career path. Therefore, don’t delay! Start your practice now!

Frequently Asked Questions (FAQs)

1. How to make a good Data Science project?

The following points should be kept in mind before starting any Data Science project:
Choose the programming language that you are comfortable with. However, the language chosen should be one of the in-demand languages such as Python, R, and Scala.
Use datasets from trusted sources. You can use Kaggle datasets. Moreover, make sure that the dataset you are using does not contain errors.
Find errors or outliers in your dataset and rectify them before training your model. You can use visualization tools to find the errors in your dataset.

2. Describe the major components that a Data Science project should have?

The following components highlight the most general architecture of a Data Science project:
Problem Statement: This is the fundamental component on which the whole project is based. It defines the problem that your model is going to solve and discusses the approach that your project will follow.
Dataset: This is a very crucial component for your project and should be chosen carefully. Only large enough datasets from trusted sources should be used for the project.
Algorithm: This includes the algorithm you are using to analyze your data and predict the results. Popular algorithmic techniques include Regression Algorithms, Regression Trees, Naive Bayes Algorithm, and Vector Quantization.
Training Models: This involves training your model against various inputs and predicting the output. This component decides the accuracy of your project. Using proper training techniques can produce better outcomes.

3. What are the skills required to be a Data Scientist?

The following are the essential skills and tools any Data Science enthusiast should master:
1. Statistical Skills including Probability
2. Analytical Skills to analyze and test the data.
3. Programming languages such as Python, R, Scala, and JAVA.
4.Data Visualization Tools such as Power BI, Tableau
5. Algorithms including Regression, Decision Trees, Bayes Algorithm
6. Calculus and Algebra.
7. Communication and Presentation Skills
8. Databases such as SQL
9. Cloud Computing to manage the resources
Apart from these technical skills, a professional Data Scientist should also have some soft skills to provide value to the company and improve interpersonal relationships. These skills include critical and curious thinking, business orientation, smart communication skills, problem-solving, team management, and creativity.

Did you find this article helpful?

Rohit Sharma

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

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(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?

5.14K+

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!

5.22K+

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