The course will follow below structure

Section 1: Getting started with Python

• This section explains how to install Aanconda distribution and write first code

• Additionally, a walk through of Spyder Platform

Section 2: Working on Data

• P02 01A running SQL in python

• P02 01 Understand Data n Add Comments in the code

• P02 02 Know Contents of the Data

• P02 03A Missing Value detection n treatment Part1

• P02 03B Getting Familar with Jupyter IDE

• P02 03C treating Numeric Missing value with mean n treating date missing value

• P02 03D Creating copy of a dataframe n dropping records based on missing value of a particular field

• P02 03E Replacing missing Value with median or mode

• P02 04 Filtering data n keeping few columns in data

• P02 05 use iloc to filter data

• P02 06 Numeric Variable Analysis with Group By n Transpose the result

• P02 07 Frequency Distribution count n percentage including missing percentage

• P02 08 Introduction to function n substring stuff

Section 3: working on multiple datasets

• P03 01 Creating Dataframe on the run Append concatenate dataframe

• P03 02 Merging DataFrames

• P03 03 Remove Duplicates Full or column based Sorting Dataframe Keep First Last Max Min

• P03 04 Getting row for max value of any column easy way n then through idxmax

• P03 05 use idxmax iterrows forloop to solve a tricky question

• P03 06 Create derived fields using numerical fields

• P03 07 Cross Tab Analysis n putting reult into another dataframe transpose result

• P03 08 Derive variable based on character field

• P03 09 Derive variable based on date field

• P03 10 First Day Last Day Same Day of Last n month

Section 4: Data visualization and some frequently used terms

• P04 01 Histogram n Bar chart in Jupyter and Spyder

• P04 02 Line Chart Pie Chart Box Plot

• P04 03 Revisit Some nitty gritty of Python

• P04 04 Scope of a variable global scope local scope

• P04 05 Range Object

• P04 06 Casting or Variable type conversion n slicing strings

• P04 07 Lambda function n dropping columns from pandas dataframe

Section 5: Some statistical procedures and other advance stuffs

• P05 01 Simple Outlier detection n treatment

• P05 02 Creating Excel formatted report

• P05 03 Creating pivot table on pandas dataframe

• P05 04 renaming column names of a dataframe

• P05 05 reading writing appending data into SQLlite database

• P05 06 writing log of code execution

• P05 07 Linear regression using python

• P05 08 chi square test of independence

Who this course is for:

• Anyone Interested in using Python for Data analysis purpose
• Data Analytics professional
• People who want to migrate from other platform like SAS to Python
• Data Scientist