The Ultimate Getting Started with Python Course!

Python is the most popular and widely used programming language in the data science industry. There’s no other language even close to the might of Python. And the most promising aspect of learning Python as a newcomer in data science?

Python is still the fastest growing major programming language today!

As a beginner in the field of data science, you need to quickly get up to speed with how Python works, the various aspects of Python, how to leverage Python for data science tasks like cleaning messy data, building awesome and impactful visualization to explore data and building predictive models, among other things.

So if you want to break into the data science field and are looking to learn Python, you’ve come to just the right course!

  • Most industry experts recommend starting your Data Science journey with Python
  • Across the world’s biggest companies and startups, Python is the most used language for Data Science and Machine Learning Projects
  • Stackoverflow survey for 2019 had Python outrank Java in the list of most used programming languages

Python is a very versatile language since it has a wide array of functionalities already available. The sheer range of functionalities might sound exhaustive and complicated, but you don’t need to be well-versed with them all. This course will guide you through the Python features you need to know to start your data science journey.

Most data scientists have a few go-to Python libraries for their daily tasks like:

  • for performing data cleaning and analysis - pandas
  • for basic statistical tools – numpy, scipy
  • for data visualization – matplotlib, seaborn

So in this course, you will learn everything you need to get started with Python for Data Science. We will start from the absolute basics of Python programming, including Python data types, variables, control flow and how to work with Python functions. We will then discuss the popular Python libraries for data science like Pandas, NumPy, SciPy, matplotlib, seaborn, scikit-learn, etc., and implement them in a hands-on manner in Python.

So, are you ready to power up your career and learn the best programming language for data science?

This course starts from basics of Python programming, Python data types, variables, control flow and working with Python functions.

We will then discuss the popular Python libraries for data science like Pandas, NumPy, SciPy, matplotlib, seaborn, scikit-learn, etc., and implement them in a hands-on manner in Python.

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Why Python for Data Science?

It’s a fair question so we decided to put together the key points to answer it:


  • Python has rapidly become the go-to language in the data science space and is among the first things recruiters search for in a data scientist's skill set
  • Python consistently ranks top in global data science surveys and its widespread popularity will only keep on increasing in the coming years
  • Over the years, with strong community support, the Python programming language has obtained a dedicated library for data analysis and predictive modelling



Who is the Python for Data Science Course for?

The Python for Data Science course is for anyone who:

  • Wants to learn Python programming from scratch
  • Wants to start their data science journey
  • Is curious about the most popular programming language for data science
  • Wants to explore the various Python libraries like Pandas, NumPy, scikit-learn, etc.

Key Takeaways from the Python for Data Science Course:

Here’s a quick summary of what you can expect to learn from the course:

  • Learn the absolute basics of Python, including:
    • What is Python?
    • How to install Python?
    • Getting started with Jupyter notebooks
  • Variables and data types in Python
  • Operators in Python
  • How to build conditional statements in Python
  • Work with Python functions
  • Data structures in Python
  • Learn about the different libraries and packages in Python
  • How to import different types of file formats in Python
  • How to work with different Python libraries for data science, including:
    • Pandas
    • NumPy
    • SciPy
    • Scikit-learn (sklearn)
    • Matplotlib
    • Seaborn, and many more
  • Basic operations on a Pandas dataframe
  • Learn how to manipulate dataframes using Pandas in Python
  • Master data summarization in Python
  • Aggregation functions:
    • Pivot table
    • GroupBy
    • Merge and joins
  • Data exploration in Python
  • Basic understanding of dealing with time and date data

And a whole host of other topics!

What do I need to start with the Python for Data Science course?

  • A working laptop / desktop with 4 GB RAM
  • A working Internet connection

This is all it takes for you to learn the most popular language for performing Data Science tasks. What are you waiting for?


Which companies use Python?

Many of the biggest and most popular companies use Python. Some of them are:

  • Google, NASA, Amazon
  • Social networking sites like Instagram, Reddit, Quora, etc
  • Media streaming companies like Netflix and Spotify
  • Rideshare companies like Uber and Lyft

“Python has been an important part of Google since the beginning and remains so as the system grows and evolves. Today dozens of Google engineers use Python, and we are looking for more people with skills in this language.” - Peter Norvig, Director of Research at Google Inc.

So get onboard the Data Science train by learning Python and upskilling yourself!

Course curriculum

  • 1
    Welcome to the Python for Data Science Course
    • Course Overview
    • Instructions for students
    • Getting to know each other
    • Know your instructors
  • 2
    Course Handouts
    • Download Course Handouts
  • 3
    Python Basics Section
    • Starting with Python Basics
  • 4
    Introduction to Python
    • Introduction to Python
    • Regarding Python Version
    • Python Installation in Windows
    • Python Installation in Mac
    • Python Installation in Linux
    • Introduction to Jupyter Notebook
  • 5
    Variables and Data Types
    • Introduction to Variables
    • Implementing Variables in Python
    • Quiz: Variables and Data Types
  • 6
    • Introduction to Operators
    • Implementing Operators in Python
    • Quiz: Operators
  • 7
    Conditional Statements
    • Introduction to Conditional Statements
    • Implementing Conditional Statements in Python
    • Quiz: Conditional Statements
    • Assignment - Conditional Statements
  • 8
    Looping Constructs
    • Introduction to Looping Constructs
    • Implementing Loops in Python
    • Quiz: Loops in Python
    • Break, Continue and Pass Statements
    • Quiz: Break, Continue and Pass Statement
  • 9
    Data Structures
    • Introduction to Data Structures
    • List and Tuple
    • Implementing List in Pyhton
    • Quiz: Lists
    • List - Project in Python
    • Implementing Tuple in Python
    • Quiz: Tuple
    • Introduction to Sets
    • Implementing Sets in Python
    • Quiz: Sets
    • Introduction to Dictionary
    • Implementing Dictionary in Python
    • Quiz: Dictionary
    • Assignment: Data Structures
  • 10
    String Manipulation
    • Introduction to String Manipulation
    • Quiz: String Manipulation
  • 11
    • Introduction to Functions
    • Implementing Functions in Python
    • Quiz: Functions
    • Lambda Expression
    • Quiz: Lambda Expressions
    • Recursion
    • Implementing Recursion in Python
    • Quiz: Recursion
  • 12
    Modules, Packages and Standard Libraries
    • Introduction to Modules
    • Modules: Intuition
    • Introduction to Packages
    • Standard Libraries in Python
    • User Defined Libraries in Python
    • Quiz: Modules, Packages and Standard Libraries
  • 13
    Handling Text Files in Python
    • Handling Text Files in Python
    • Quiz: Handling Text Files
    • Assignment - Handling Text Files
  • 14
    Introduction to Python Libraries for Data Science
    • Important Libraries for Data Science
    • Quiz: Important Libraries for Data Science
  • 15
    Python Basics Summary
    • Python Basics Summary
  • 16
    Python for Data Science Section
    • Starting with Python for Data Science
  • 17
    Python Libraries for Data Science
    • Basics of Numpy in Python
    • Basics of Scipy in Python
    • Quiz: Numpy and Scipy
    • Basics of Pandas in Python
    • Quiz: Pandas
    • Basics of Matplotlib in Python
    • Basics of Scikit-Learn in Python
    • Basics of Statsmodels in Python
  • 18
    Reading Data Files in Python
    • Reading Data in Python
    • Reading CSV files in Python
    • Reading Big CSV Files in Python
    • Quiz: Reading CSV files in Python
    • Reading Excel & Spreadsheet files in Python
    • Quiz: Reading Excel & Spreadsheet files in Python
    • Reading JSON files in Python
    • Quiz: Reading JSON files in Python
    • Assignment: Reading Data Files in Python
  • 19
    Preprocessing, Subsetting and Modifying Pandas Dataframes
    • Subsetting and Modifying Data in Python
    • Overview of Subsetting in Pandas I
    • Overview of Subsetting in Pandas II
    • Subsetting based on Position
    • Subsetting based on Label
    • Subsetting based on Value
    • Quiz: Subsetting Dataframes
    • Modifying data in Pandas
    • Quiz: Modifying Dataframes
    • Assignment: Subsetting and Modifying Pandas Dataframes
  • 20
    Sorting and Aggregating Data in Pandas
    • Preprocessing, Sorting and Aggregating Data
    • Sorting the Dataframe
    • Quiz: Sorting Dataframes
    • Concatenating Dataframes in Pandas
    • Concept of SQL-Like Joins in Pandas
    • Implementing SQL-Like Joins in Pandas
    • Quiz: Joins in Pandas
    • Aggregating and Summarizing Dataframes
    • Preprocessing Timeseries Data
    • Quiz: Preprocessing Timeseries Data
    • Assignment: Sorting and Aggregating Data in Pandas
  • 21
    Visualizing Patterns and Trends in Data
    • Visualizing Trends & Pattern in Data
    • Basics of Matplotlib
    • Data Visualization with Matplotlib
    • Quiz: Matplotlib
    • Basics of Seaborn
    • Data Visualization with Seaborn
    • Quiz: Seaborn
    • Assignment: Visualizing Patterns and Trends in Data
  • 22
    Python for Data Science Summary
    • Python for Data Science Summary


  • Mohd Sanad Zaki Rizvi

    Mohd Sanad Zaki Rizvi

    Mohd Sanad Zaki Rizvi is a Data Scientist at Analytics Vidhya. His strength includes his expertise in Machine Learning, NLP and Software Engineering. He has conducted multiple trainings around Data Science and NLP and will be your instructor for this course. He has previously worked in a research capacity at the University of Southern California, Los Angeles where he was working at the intersection of NLP and Deep Learning to build better virtual STEM mentors. When Sanad is not busy trying to explore the breakthroughs in NLP, he is an avid contributor to open-source projects including the Python programming language.

Common Questions Beginner ask about Python for Data Science?

Do I need to learn coding to learn Python?

If you are totally new to programming, no need to get intimidated by learning a whole new language.

Python is a very easy language to learn. It does not have a complicated syntax and understanding Python is very intuitive. You don’t need to be skilled in coding for getting started in Python. This course is for beginners we will start right from the foundations to performing data analysis tasks in Python.

I am familiar with other Programming Languages like Java/C++. Will this course help me to migrate to Python?

Do you know that Python is essentially a wrapper on C? That is what makes it fast and easy to understand!

Though Python has recently become popular amongst Data Scientists, it was originally a general-purpose language. Python is still object-oriented and follows many of the paradigms that Java does. So if you are familiar with the concepts of programming, you can migrate to Python easily with this course.

How much Python do I need to know to enter Data Science?

Though Python has hundreds of libraries and many more functionalities, you don’t need to know all of them for learning Data Science

Rather than becoming an expert in the entire language, you would need to just be acquainted with the basic syntax of Python. We will also cover the most popular libraries used by Data Scientists and which you would be using too as a future Data Scientist!

What if I don’t have Python installed on my system?

One of the best things about Python is the wide variety of platform that support writing it.

We will provide easy to follow instructions to work with Python using Anaconda, an extremely popular package manager platform.  No matter what Operating System you are using, we have you covered with guides for all of them.

What are the most popular open-source libraries that Python supports?

Pandas, numpy, scipy, matlplotlib, seaborn are used for Data Science and Data Analysis. scikit-learn, tensorflow, keras are used for basic and advanced machine learning libraries for deep learning like OpenCV(Computer Vision), NLTK(Natural Language Processing)

Will I be able to apply what I have learnt here to machine learning and data science projects?

The Python for Data Science course is designed to help you completely understand Python and start using it immediately for Data Science projects.

With regular assignments, quizzes and hands-on projects, you will be full equipped with the essential data science skillsets.

Certificate of Completion

Upon successful completion of the course, you will be provided a block chain enabled certificate by Analytics Vidhya with lifetime validity.
Certificate of Completion

Frequently Asked Question

Popular customer questions about Python for Data Science

  • Who should take this course?

    This course is for people who want to learn basic Python skills and get started with Data Science using Python

  • I have an experience of 2+ years, but no background in Data Science nor in Programming. Is the course right for me?

    The course assumes no prior background in Data Science or programming. We cover the basics of programming and Data Science in this course comprehensively.

  • How long would I have access to the “Python for Data Science” course?

    Once you register, you will have 6-month access to complete the course. If you visit the course 6 months after your initial registration - you will need to enrol in the course again. Your past progress will be lost.

  • How much effort will this course take?

    You can complete "Python for Data Science" course in a few hours. You are also expected to apply Python and learning of this course to perform basic data operations on data. The time taken in projects varies from person to person.

  • How can I apply and test my learnings about Python?

    You can start by doing the tests at the end of various chapters. In addition, you can enroll for the Applied Machine Learning course to continue your Data Science and Machine Learning Journey.

  • Can I download the videos from this course?

    We regularly update "Python for Data Science" course and hence do not allow for videos to be downloaded. You can visit this free course anytime to refer to these videos.

  • Which programming languages is used to teach this course?

    This entire course uses Python(version 3) programming language and its open source libraries pandas, numpy, matplotlib and seaborn to teach you Data Science.

Enroll in Python for Data Science today

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