Applied Statistics and Data Preparation with Python

Why learn Data Analysis and Data Science?

According to SAS, the five reasons are

1. Gain problem solving skills

The ability to think analytically and approach problems in the right way is a skill that is very useful in the professional world and everyday life.

2. High demand

Data Analysts and Data Scientists are valuable. With a looming skill shortage as more and more businesses and sectors work on data, the value is going to increase.

3. Analytics is everywhere

Data is everywhere. All company has data and need to get insights from the data. Many organizations want to capitalize on data to improve their processes. It's a hugely exciting time to start a career in analytics.

4. It's only becoming more important

With the abundance of data available for all of us today, the opportunity to find and get insights from data for companies to make decisions has never been greater. The value of data analysts will go up, creating even better job opportunities.

5. A range of related skills

The great thing about being an analyst is that the field encompasses many fields such as computer science, business, and maths.  Data analysts and Data Scientists also need to know how to communicate complex information to those without expertise.

The Internet of Things is Data Science + Engineering. By learning data science, you can also go into the Internet of Things and Smart Cities.

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 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 the 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. Quantile

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