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

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

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

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

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

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