Homebreadcumb forward arrow iconBlogbreadcumb forward arrow iconData Sciencebreadcumb forward arrow iconGet Started in Data Science with Python

Get Started in Data Science with Python

Last updated:
22nd Nov, 2017
Read Time
8 Mins
share image icon
In this article
Chevron in toc
View All
Get Started in Data Science with Python

In October 2012, Thomas Davenport and DJ Patil made a landmark claim in the month’s Harvard Business Review issue. They boldly declared Data Science to be the ‘sexiest job of the 21st century.’ While this claim is certainly debatable, there is no denying the exponential interest the nascent field has sparked in recent years.
All major companies in the world have started hiring Data Scientists and forming dedicated Data and Analytics Teams. A shortage of Data Scientists and a high demand for good Data Scientists have led many companies (such as Airbnb) to set up their own internal Data Science Universities.
The consensus is clear: Data is the currency of the 21st century. Companies that leverage data in their favor to create superior products will survive. The rest will perish. In such a scenario, it is easy to see why the Data Scientist is as important as ever, now.
But who is a Data Scientist? The skeptics say it is just a fancy name for a Statistician. Others claim it is a Computer Scientist extremely competent in statistical modeling. My favorite definition happens to be the following:

Data Scientists are people who know more statistics than Computer Programmers and more programming than Statisticians.

In other words, it is a field that brings together tools from Computer Science, Statistics, and the particular domain that the data belongs to. Under such circumstances, it is easy to see why finding good data scientists is hard, to say the least. There simply aren’t enough people who are competent at these skills, simultaneously.
This is one of the major reasons why beginners find the prospect of Learning Data Science so overwhelming. Do I have to know calculus? How hard is the math? Should I learn how to programme first? What if I’m not very good at building software?
What Kind of Salaries do Data Scientists and Analysts Demand?

In this article, I will attempt at offering a path towards learning Data Science – that of the Python Programming Language. While this in no way is going to make you a star data scientist, it will put you en route towards that very goal.

Most data science projects (assuming you already have the raw data) involve the following components:

  1. Data Wrangling
  2. Exploratory Data Analysis and Visualization
  3. Data Preparation
  4. Building and Deploying Machine Learning Models.

We will be looking at these steps one by one by taking a glance at the tools available to us and potent resources to learn these tools:


We have already emphasised that Statistics and Computer Science are integral components of Data Science. As a prerequisite, it is also important for you to have knowledge of basic linear algebra and programming, as well.
This learning path will assume you are coding in the Python Programming Language. Therefore, it is important that you know how to code in Python. The good news is that Python is extremely easy to learn; especially for people who have never programmed before. Its syntax is very intuitive, readable by humans and involves a very shallow learning curve.

Downloading Anaconda

Python, being an interpreted language, is traditionally much slower than lower level languages such as C/C++. To combat this handicap, we will be using powerful Scientific Libraries which are written in C and C++. After that, we will apply extremely powerful techniques such as vectorisation to speed up the computation process.
The aforementioned libraries don’t come bundled with Python. However, they can be downloaded as a distribution (Python included), all at once, through Anaconda offered by Continuum Analytics. This will give you all the tools you need to follow this path. 

The Python Programming Language

As I have already mentioned, Python is an extremely easy language to learn. Keep in mind that you do not have to be an expert in the language. For now, learning the basics of programming and the Python syntax will do. Going through any of the above tutorials or books should suffice.

Get Started in Data Science with Python UpGrad Blog

Linear Algebra

In order to understand the logic and algorithms in Machine Learning, it is important that you have a good understanding of Linear Algebra. 
How Can You Transition to Data Analytics?[/su_button

Explore our Popular Data Science Online Certifications

Data Wrangling

The availability of data in the real world, in a form suitable for analysis or computation, has been rare. Data Cleaning and Wrangling, simply put, is the process of transforming unclean and malformed data into a form that is suitable for a particular piece of analysis.
The data wrangling tool of choice in Python is the Pandas library. Pandas gives us access to extremely powerful data structures called Data Frames which makes the data wrangling and analysis process substantially faster and simpler. It is an open secret that the data scientist spends more than 70% of his/her time collecting and wrangling data. Becoming proficient in Pandas, therefore, is well worth the investment.

upGrad’s Exclusive Data Science Webinar for you –

How upGrad helps for your Data Science Career?

Top Data Science Skills You Should Learn

Data Visualization

The power of the data scientist lies in the ability to extract information from data. And often, the best way to get that information and gain insights is by visualising the data in the best way possible.
Visualisation is also the most important step when it comes to communicating your story and results to non-technical people. Good visuals and graphs make a much more compelling case than dry numbers.
Python’s de facto visualisation library is Matplotlib. However, Matplotlib is notorious for being extremely difficult to use. To address these criticisms, the Seaborn library was created which makes creating graphs and visuals incredibly simple.

Our learners also read: Free Python Course with Certification

Get Started in Data Science with Python - become a data scientist UpGrad Blog

Machine Learning

The final and the most glamorous part of data science is predictive modeling and machine learning. This is the part that actually makes data-driven systems ‘intelligent’.
Machine Learning can be a complex subject with a substantially steep learning curve. However, Python’s Scikit-Learn library abstracts all the details of major Machine Learning Algorithms from us and makes training models as easy as typing out a couple of lines of code.
That said, I believe it is very important to know the basic logic underlying the algorithm that you are using to ensure that the right algorithm is used with the right problem and the right parameters

Keep an Eye Out for the Next Big Thing: Machine Learning

Read our popular Data Science Articles


Next Steps 

With this, you are now in a good position to get your hands dirty with real life Data Science Projects!
One strongly recommended next step is Kaggle Competitions. You can make submissions to Kaggle Contests for Beginners such as Titanic: Machine Learning from Disaster and Predicting Housing Prices to get started. 

Hopefully, this article has diminished if not eliminated some of your confusion on how to get started with Data Science. The road ahead might be challenging but it is also incredibly exciting. So, go ahead. There has never been a better time to be a data scientist, the ‘sexiest’ role of the century.

Check out all trending Python tutorial concepts in 2024.


Rounak Banik

Blog Author
Rounak is a final year undergraduate at IIT Roorkee. His professional interests lie in Web Development and Data Science. He has previously interned as a Software Engineer at Parceed, a New York-based startup and Springboard, a Data Science EdTech startup based in San Francisco and Bangalore. He has also worked as a Backend Development Instructor with Acadview, teaching Python and Django to around 35 college students from Delhi and Dehradun. He is currently working directly under the Director of IIT Roorkee and Dr. Durga Toshniwal on a project on Fake News and Review Detection.

Frequently Asked Questions (FAQs)

1What is the importance of Python for Data Science?

Data scientists and machine learning engineers that know Python currently have more job vacancies than all other languages combined. There are numerous reasons for its predominance in this domain, but three frequently stand out.

Firstly, Python's popularity stems from its simplicity. As a result, it is accessible to almost everybody. The less a developer needs to worry about the code, the more time and energy he or she can devote to discovering solutions.

Secondly, libraries are possibly the most important factor in Python's popularity. In Python, a library is a collection of pre-bundled code that you can use to extend the language’s capabilities.

Thirdly, Python gained popularity because of the Jupyter Notebook. Jupyter Notebooks are a fantastic method to write Python code. A Jupyter Notebook is a web-based tool for prototyping and sharing data-related projects. You can write lines of code and run them one at a time or in tiny batches, rather than writing and rewriting a full programme. This makes debugging and comprehending coding a lot easier.

2Which is the better programming language for data science: R or Python?

When it comes to selecting a programming language for data science, both Python and R are excellent choices. They are both open-source and free languages that can run on Windows, macOS or Linux. Python and R can handle data analysis tasks of any complexity and are relatively easy to learn, especially for beginners. There’s no right or wrong choice when it comes to choosing between the two. Both are in-demand and will enable you to implement any data analytics task you desire. However, if you have some experience with Java or C++ , then Python might be easier to learn than R. On the other hand, if you have some knowledge of statistics, then R will be a tad easier for you to learn.

3Is Linear Algebra required for Machine Learning?

The study of vectors, matrices, and linear transformations is known as linear algebra.

It provides a critical basis for the discipline of Machine Learning, From the notations used to describe the operation of algorithms through the implementation of algorithms in code.

Explore Free Courses

Suggested Blogs

42 Exciting Python Project Ideas & Topics for Beginners in 2024 With Source Code [Latest]
Summary: In this article, you will learn the 42 Exciting Python Project Ideas & Topics in 2024. Take a glimpse below. Mad Libs Generator Number
Read More

by Rohit Sharma

06 May 2024

Top 13 Highest Paying Data Science Jobs in India [A Complete Report]
In this article, you will learn about Top 13 Highest Paying Data Science Jobs in India. Take a glimpse below. Data Analyst Data Scientist Machine
Read More

by Rohit Sharma

12 Apr 2024

Most Common PySpark Interview Questions & Answers [For Freshers & Experienced]
Attending a PySpark interview and wondering what are all the questions and discussions you will go through? Before attending a PySpark interview, it’s
Read More

by Rohit Sharma

05 Mar 2024

Data Science for Beginners: A Comprehensive Guide
Data science is an important part of many industries today. Having worked as a data scientist for several years, I have witnessed the massive amounts
Read More

by Harish K

28 Feb 2024

6 Best Data Science Institutes in 2024 (Detailed Guide)
Data science training is one of the most hyped skills in today’s world. Based on my experience as a data scientist, it’s evident that we are in
Read More

by Harish K

28 Feb 2024

Data Science Course Fees: The Roadmap to Your Analytics Career
A data science course syllabus covers several basic and advanced concepts of statistics, data analytics, machine learning, and programming languages.
Read More

by Harish K

28 Feb 2024

Inheritance in Python | Python Inheritance [With Example]
Python is one of the most popular programming languages. Despite a transition full of ups and downs from the Python 2 version to Python 3, the Object-
Read More

by Rohan Vats

27 Feb 2024

Data Mining Architecture: Components, Types & Techniques
Introduction Data mining is the process in which information that was previously unknown, which could be potentially very useful, is extracted from a
Read More

by Rohit Sharma

27 Feb 2024

6 Phases of Data Analytics Lifecycle Every Data Analyst Should Know About
What is a Data Analytics Lifecycle? Data is crucial in today’s digital world. As it gets created, consumed, tested, processed, and reused, data goes
Read More

by Rohit Sharma

19 Feb 2024

Schedule 1:1 free counsellingTalk to Career Expert
footer sticky close icon