Data Analysis with Python: Zero to Pandas

Data Analysis with Python: Zero to Pandas


Course Duration: August 15, 2020 to September 26, 2020 (6 weeks)

"Data Analysis with Python: Zero to Pandas" is a practical, beginner-friendly and coding-focused introduction to data analysis covering the basics of Python, Numpy, Pandas, data visualization and exploratory data analysis. This course runs over 6 weeks, with a 2-hour video lecture every week with live interactive coding using Jupyter notebooks. You can earn a verified certificate of accomplishment by completing weekly assignments and doing a course project.

The course is completely FREE of cost, and there are no fees for enrollment or certification. We're excited to offer this course in partnership with freeCodeCamp. All lectures will be live-streamed on YouTube and recordings will be available to watch later. Important Links:

Lesson 1 - Introduction to Programming with Python

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  • Course overview & curriculum walkthrough
  • First steps with Python and Jupyter notebooks
  • A quick tour of variables and data types
  • Branching with conditional statements and loops

Assignment 1 - Python Basics Practice

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  • Solve word problems using variables & arithmetic operations
  • Manipulate data types using methods & operators
  • Use branching and iterations to translate ideas into code
  • Explore the documentation and get help from the community

Lesson 2 - Next Steps with Python

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  • Branching with conditional statements and loops
  • Write reusable code with Functions
  • Working with the OS & Filesystem
  • Assignment and course forum walkthrough

Lesson 3 - Numerical Computing with Numpy

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  • Going from Python lists to Numpy arrays
  • Working with multi-dimensional arrays
  • Array operations, slicing and broadcasting
  • Working with CSV data files

Assignment 2 - Numpy Array Operations

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  • Explore the Numpy documentation website
  • Demonstrate usage 5 numpy array operations
  • Publish a Jupyter notebook with explanations
  • Share your work with the course community

Lesson 4 - Analyzing Tabular Data with Pandas

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  • Reading and writing CSV data with Pandas
  • Querying, filtering and sorting data frames
  • Grouping and aggregation for data summarization
  • Merging and joining data from multiple sources

Assignment 3 - Pandas Practice

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  • Create data frames from CSV files
  • Query and index operations on data frames
  • Group, merge and aggregate data frames
  • Fix missing and invalid values in data

Lesson 5 - Visualization with Matplotlib and Seaborn

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  • Basic visualizations with Matplotlib
  • Advanced visualizations with Seaborn
  • Tips for customizing and styling charts
  • Plotting images and grids of charts

Course Project - Exploratory Data Analysis

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  • Find a real-world dataset of your choice online
  • Use Numpy & Pandas to parse, clean & analyze data
  • Use Matplotlib & Seaborn to create visualizations
  • Ask and answer interesting questions about the data

Lesson 6 - Exploratory Data Analysis - A Case Study

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  • Finding a good real-world dataset for EDA
  • Data loading, cleaning and preprocessing
  • Exploratory analysis and visualization
  • Answering questions and making inferences

Certificate of Completion

Participants who register for the course and make valid submissions for all assignments will be eligible to receive a Certificate of Completion by Selected projects will also be receive a Best Project Award based on evaluation criteria determined by the instructors.

Instructor - Aakash N S

Aakash is the co-founder and CEO of, a project management and collaboration platform for machine learning. Prior to starting, Aakash worked as a software engineer (APIs & Data Platforms) at Twitter in Ireland & San Francisco and graduated from Indian Institute of Technology, Bombay. He’s also an avid blogger, open source contributor and online educator.


This is a beginner-friendly course, and no prior knowledge of Python or data science is assumed. Some previous exposure to programming is helpful, but it is not a strict requirement for this course.


For queries, you can reach out to the course support team at