Data Analysis with Python and Pandas

Data Analysis with Python and Pandas

₹4,500 ₹4,000

As a Python programmer, you already know that this general purpose programming language, as used by Google, Pinterest and Instagram to name a few, is the most accessible and versatile coding language out there. So why not use the Python knowledge you already have to get yourself work-ready in one of the most in-demand job skills of the moment, data analysis?

Be a Data Goldminer

These days, almost every company we interact with gathers data on us. However to benefit from this vast mine of information, businesses need skilled experts like you to interpret their data so they can boost their profits and improve their customer experience. By learning to efficiently analyse data, manipulate data sets and master data mining in Python, you’ll not only be at the forefront of this exciting and ever-expanding new career option, you’ll gain a brand new skill set that can also be used for various other applications.

Learn in Your Spare Time

With 51 easy-to-access lectures across six hours of video, the Data Analysis with Python and Pandas course is accessible at a time and a place that suits you. Not only that, informal style makes learning a pleasure. With Python installed and a knowledge of the language, you are all set to start your journey in a much in-demand new career in data analysis.


Start building on your Python expertise today to become a professional and proficient data analyst.

  • Learn the fundamental principles of Pandas, the library of data structures you’ll be using in conjunction with Python.
  • Move on to more complex operations you’ll be running in conjunction with Pandas including data manipulation, logical categorising, statistical functions and applications and more.
  • Get confident working with missing data, combining data, working with databases before tackling advanced operations such as resampling, correlation, mapping and buffering.
  • As well as mastering the Pandas open source library for numerical data, you’ll also work with the NumPy library of high level mathematical functions, which was created as an extension to Python to support large multi-dimensional arrays and matrices.


  • Gaining more in-depth knowledge of the Python programming language not only gives you new skills in data analysis, it allows for a wide variety of applications and ultimately makes learning more advanced coding languages easier.
  • Boost your CV by learning this much in demand skill in the modern workplace.
  • Print off your certificate of completion to prove your data analysis know-how to potential employers.
  • Access the course at a time and place that suits you, across all devices including your phone.
  • The course is yours for 12 months to revisit whenever you need to and the informal style is designed to keep distraction at bay, meaning you stay alert and engaged throughout your learning journey.

It makes sense to join an exploding career sector using what is fast becoming one of the world’s most popular programming languages, so sign up to Data Analysis with Python and Pandas today!

Units of study
Introduction to the Course
  • Course Introduction
  • Getting Pandas and Fundamentals
Introduction to Pandas
  • Section introduction
  • Creating and Navigating a Dataframe
  • Slices, head and tail
  • Indexing
  • Visualizing The Data
  • Converting To Python List Or Pandas Series
  • Section Conclusion
IO Tools
  • Section introduction
  • Read Csv And To Csv
  • io operations
  • Read_hdf and to_hdf
  • Read Json And To Json
  • Read Pickle And To Pickle
  • Section Conclusion
Pandas Operations
  • Pandas Operations
  • Column Manipulation (Operatings on columns, creating new ones)
  • Column and Dataframe logical categorization
  • Statistical Functions Against Data
  • Moving and rolling statistics
  • Rolling apply
  • Section Outro
Handling for Missing Data / Outliers
  • Section Intro
  • drop na
  • Filling Forward And Backward Na
  • detecting outliers
  • Section Conclusion
Combining Dataframes
  • Section Introduction
  • Concatenation
  • Appending data frames
  • Merging dataframes
  • Joining dataframes
  • Section Conclusion
Advanced Operations
  • Section Introduction
  • Basic Sorting
  • Sorting by multiple rules
  • Resampling basics time and how (mean, sum etc)
  • Resampling to ohlc
  • Correlation and Covariance Part 1
  • Correlation and Covariance Part 2
  • Mapping custom functions
  • Graphing percent change of income groups
  • Buffering basics
  • Buffering Into And Out Of Hdf5
  • Section Conclusion
Working with Databases
  • Section Introduction
  • Writing to reading from database into a data frame
  • Resampling data and preparing graph
  • Finishing Manipulation And Graph
  • Section and course Conclusion
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