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About this course

This Course is designed and developed to provide the learner with an opportunity to gain knowledge in the Data Analytics field using a Pandas Library written in Python Language. Pandas is a popular library used for data manipulation and analysis in the field of Data Analytics and Data Science. This course assumes that you have basic knowledge of Python Programming like Python code syntax, it’s 
data structure, functions, generators etc. All the modules in this course use various data sets from different sources to learn how to manipulate and analyse the data. We use the Jupyter Notebook environment for practicing and demonstrating the data analysis of datasets. This course is divided into 10 different modules and it starts with basics of Pandas upto making good projects. Join this course to get more insights and learn how to analyse the data in the Industry. 

Prerequisite: 

● Understanding of Python 3 Basics - Data Structure, Functions, loops, control structure, and Object Oriented Programming 
● Understanding Indentation and syntax of Python version 3 

What You’ll Learn: 

  • Able to understand the role of Pandas in Data Analytics Field 
  • Understand how to install and use Pandas library in your source code 
  • Learn two types of pandas structure name series and data frame 
  • Know about reading and writing tabular data in Pandas 
  • Learn various functionalities of Pandas data structures 
  • Able to Work with several input and output tools in Pandas library 
  • Able to understand indexing and selecting data in Pandas 
  • Know about text data and learn how to work with text data 
  • Understand the basics in Plotting using Pandas Library 
  • Able to understand and work on chart and table visualization 
  • Understand time series and date related data and know representations of time series and date data 
  • Able to built a project named Time Series with Pandas on Jupyter Notebook 
  • Practice several questions in Pandas for Data Analytics Interview Preparation. 

SKILLS YOU WILL GAIN
  • Python
  • Data Analytics
  • Panda
  • Pandas Data Structure Data Visualization
avatar  Learner Career Outcomes
  • 52%

    started a new career after completing these courses
  • 46%

    started a new career after completing these courses
  • 14%

    got a pay increase or promotion
  •   Flexible deadlines
    Reset deadlines in accordance to your schedule.
  •   Shareable Certificate
    Earn a Certificate upon completion
  •   100% online
    Start instantly and learn at your own schedule.
  •   English

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Syllabus - What you will learn from this course

Module

    1    

2 Hours to complete

Module 1: Pandas Introduction and installation

  • Introduction of Pandas package
    • Pandas package overview
    • Data Processing using Pandas package
  • What kind of Data does Pandas handle ?
  • Importance of Pandas in Data Analytics
  • Installing Pandas
    • Python version support for Pandas
    • Install Pandas with Anaconda Distribution 
    • Install Pandas with Miniconda Distribution
    • Install Pandas from PyPi
    • Other Installation option for Pandas
  • Running Test Suite
  • Pandas Dependencies

Module

    2    

2 Hours to complete

Module 2: Pandas Data Structure

  • Pandas Data Structure 
    • Series Data Structure
    • DataFrame Data Structure
  • Why more than one Data Structure in Pandas ?
  • Mutability and copying of data 
  • Importing Pandas package in your code 
  • Object Creation in Pandas
  • Read and Write Tabular Data in Pandas
    • Read data from Excel File
    • Read data from CSV File
    • Analysing CSV File data using Pandas DataFrame
    • How to select specific data from CSV File ?
      • Select certain rows
      • Select certain columns
      • Select certain rows and columns
    • Write data to other file

Module

    3    

3 Hours to complete

Module 3: Functionality in Pandas Data Structure

  • Head and Tail Method
  • Attributes and underlying data
  • Comparison in binary and array-like objects 
  • Descriptive Statistics
  • Function application
    • Table-Wise function application
    • Row or Column-Wise application
    • Applying Element Wise functions
  • Sorting 
  • Copying
  • dtypes

Module

    4    

2 Hours to complete

Module 4: Input/Output Tools

  • Introduction to Input and output in Pandas
  • Input/Output Tools
    • CSV Vs Text Files
    • HTML and XML
    • JSON
    • Excel files
    • SQL Queries
  • Other File formats

Module

    5    

2 Hours to complete

Module 5: Indexing and Selecting Data in Pandas

  • Basics of Indexing
  • Different choices for Indexing
    • Attribute access
    • Boolean Indexing
    • Indexing with list
  • Selection of data
  • Selection by
    • Label
    • Position
    • Callable
  • Multi-Indexing

Module

    6    

1 Hours to complete

Module 6: Working with Text Data

  • Text Data Types 
  • String Methods
    • Splitting and replacing strings
  • Indexing with .str
  • Extracting substrings
  • Testing for strings that match or contain a pattern

Module

    7    

1 Hours to complete

Module 7: Chart Visualization

  • Basic Plotting: Plot
  • Other Plots in Pandas
  • Plotting Tools
    • Plotting with Matplotlib
  • Plotting Backends
  • Example of Plotting with Dummy data

Module

    8    

1 Hours to complete

Module 8: Table Visualization

  • Styler Object and HTML
  • Display Formatting
    • Formatting values
    • Hiding Data
  • Table Styles
    • Builtin styles
  • Tooltips and Captions
  • Exporting Data
    • Export to Excel
    • Export to LaTex

Module

    9    

2 Hours to complete

Module 9: Time Series / Date Functionality

  • Overview of Time Series
    • Timestamps Vs time spans
    • Converting to timestamps
    • Timestamp limitations
  • Time/Data components
  • Time Series-related instance methods 
  • Converting between representations
  • Practical Example of Time Series/Date Data
  • Options and Settings module in Pandas
    • Changing Pandas options with Methods

Module

    10    

4 Hours to complete

Module 10: Built a project using Pandas

  • Time Series with Pandas using Jupyter Notebook
    • Creating Data ranges
      • From files
      • From Scratch
    • Manipulations 
    • Field accessors
    • Plotting
    • Time zones 
    • Data Representations
  • Dataset Analysis and Visualization
    • Take Data from Data Source
      • data.gov.in
    • Data Munging
    • Converting to CSV File
    • Visualization of Data
  • Excercise Questions in Pandas for Interview Preparation


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