Python for Data Analysis / Data Science: A Crash Course

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