ExpiredApplied Statistics and Data Preparation with Python

This is the bite size course to learn Python Programming for Applied Statistics. In 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 a 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

1. Getting Started
2. Getting Started 2
3. Getting Started 3
4. Data Mining Process
5. Download Data set
6. Read Data set
7. Mode
8. Median
9. Mean
10. Range
11. Range One Column
12. Qunatile
13. Variance
14. Standard Deviation
15. Histogram
16. QQPLot
17. Shapiro Test
18. Skewness and Kurtosis
19. Describe()
20. Correlation
21. Covariance
22. One Sample T Test
23. Two Sample TTest
24. Chi Square Test
25. One Way ANOVA
26. Simple Linear Regression
27. Multiple LInear Regression
28. Data Processing: DF.head()
29. Data Processing: DF.tail()
30. Data Processing: DF.describe()
31. Data Processing: Select Variables
32. Data Processing: Select Rows
33. Data Processing: Select Variables and Rows
34. Data Processing: Remove Variables
35. Data Processing: Append Rows
36. Data Processing: Sort Variables
37. Data Processing: Rename Variables
38. Data Processing: GroupBY
39. Data Processing: Remove Missing Values
40. Data Processing: Is THere Missing Values
41. Data Processing: Replace Missing Values
42. Data Processing: Remove Duplicates