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 R 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 R programming, and you can learn R programming from my "Create Your Calculator: Learn R Programming Basics Fast" course.  You will learn R Programming for applied statistics and you will be able

You can take the course as follows, and you can take an exam at EMHAcademy to get SVBook Certified Data Miner using the R certificate : 

- Create Your Calculator: Learn R Programming Basics Fast (R Basics)

- Applied Statistics using R with Data Processing (Data Understanding and Data Preparation)

- Advanced Data Visualizations using R with Data Processing (Data Understanding and Data Preparation, in the future)

- Machine Learning with R (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 2

12. Range 3

13. IQR

14. Quantile

15. Population Variance

16. Sample Variance

17. Variance

18. Standard Deviation

19. Normal Distribution

20. Skewness and Kurtosis

21. Summary() and Str()

22. Correlation

23. Covariance

24. Inferential Statistics Tests

25. One Sample T Test

26. Two Sample Unpaired T Test

27. Two Sample Unpaired T-Test (Variance not Equal)

28. Two Sample Paired T Test

29. Chi-Square Test

30. One Way ANOVA

31. Two Way ANOVA

32. MANOVA

33. Simple Linear Regression

34. Multiple Linear Regression

35. Data Processing: Select Variables

36. Data Processing: Sort Data

37. Data Processing: Filter Data

38. Data Processing: Remove Missing Values and Remove Duplicates

References: 

This course is actually based on the Learn R for Applied Statistics book I have published at Apress.