Data Analysis with Python and Pandas Training Course

4k learners

In today’s data-driven business, data analysis is the utmost priority for decision making and hence this is followed by the requirement of data analysts in various industries. One of the most in-demand job opportunities is for individuals with the ability to analyze data with popular programming languages such as Python and Pandas. In this course, you will learn how to use these frameworks to extract impactful results from huge data to any form spreadsheet for a quick and easy view.

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  • Course completion certificate

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Course Overview

Data frameworks is a tool used for data analysis very similar to spreadsheets. Pandas use data frameworks in a way that the outcome of the analysis is delivered faster.  Adaptable to all data types, this tool can be used for all kinds of analysis. In this course, we provide you a tutorial that imparts the user with the knowledge of how to use the Pandas tool. You will learn how to install, and how to use it for analysis of various data types. This tutorial uses Python, Pandas, and NumPy. The course is for individuals associated with Python and wish to perform Data Analysis using Python. It is also useful for people who deal with multiple data types and want to analyze them.

  • 1-year access to audio-video lectures
  • GreyCampus course completion certificate

Course Outline

Course Introduction
Getting Pandas and Fundamentals
Section Conclusion
Section introduction
Creating and Navigating a Dataframe
Slices, head and tail
Visualizing The Data
Converting To Python List Or Pandas Series
Section Conclusion
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
Section introduction
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
Section Intro
drop na
Filling Forward And Backward Na
detecting outliers
Section Conclusion
Section Introduction
Appending data frames
Merging dataframes
Joining dataframes
Section Conclusion
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
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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