Phyton: Applied Statistics And Data Preparation

This is the bite-size course to learn Python Programming for Applied Statistics. In the CRISP-DM data mining process, Applied Statistics is at the Data Understanding stage. This course also covers Data processing, which is at the Data Preparation Stage. 

 

You will need to know some Python programming, and you can learn Python programming from my ""Create Your Calculator: Learn Python Programming Basics Fast"" course. You will learn Python Programming for applied statistics.

 

You can take the course as follows, and you can take an exam at EMHAcademy to get SVBook Certified Data Miner using Python certificate : 
Create Your Calculator: Learn Python Programming Basics Fast (R Basics)
Applied Statistics using Python with Data Processing (Data Understanding and Data Preparation)
Advanced-Data Visualizations using Python with Data Processing (Data Understanding and Data Preparation, in future)
Machine Learning with Python (Modeling and Evaluation)



Content
Getting Started
Getting Started 2
Getting Started 3
Data Mining Process
Download Data set
Read Data set
Mode
Median
Mean
Range
Range One Column
Quantile
Variance
Standard Deviation
Histogram
QQPLot
Shapiro Test
Skewness and Kurtosis
Describe()
Correlation
Covariance
One-Sample T-Test
Two Sample TTest
Chi-Square Test
One Way ANOVA
Simple Linear Regression
Multiple Linear Regression
Data Processing: DF.head()
Data Processing: DF.tail()
Data Processing: DF.describe()
Data Processing: Select Variables
Data Processing: Select Rows
Data Processing: Select Variables and Rows
Data Processing: Remove Variables
Data Processing: Append Rows
Data Processing: Sort Variables
Data Processing: Rename Variables
Data Processing: GroupBY
Data Processing: Remove Missing Values
Data Processing: Is THere Missing Values
Data Processing: Replace Missing Values
Data Processing: Remove Duplicates


Basic knowledge
Fundamentals Python programming

 

What will you learn
Applied Statistics using Python