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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.

  • Access to GreyCampus platform

  • GreyCampus Course completion certificate

Subscribe to this course + 29 courses
USD 50

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Program 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

Program Outline

  • Introduction to the Course
    Course Introduction
    Getting Pandas and Fundamentals
    Section Conclusion
  • Introduction to Pandas
    Section introduction
    Creating and Navigating a Dataframe
    Slices, head and tail
    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
    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
    • Handling for Missing Data / Outliers
      Section Intro
      drop na
      Filling Forward And Backward Na
      detecting outliers
      Section Conclusion
    • Combining Dataframes
      Section Introduction
      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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