**Example Curriculum**

- 1 Bar plot (16:07)
- 2 Bar plots for groups (7:48)
- 3 Pie Charts and Graphical Parameters (10:35)
- 4 Finishing Pie charts (6:50)
- 5 Histograms (15:27)
- 6 Understanding Urban Population of US using Histogram (2:54)
- 7 Box Plots (9:07)
- 8 Box plots for groups (3:44)
- 9 Scatter Plots (15:03)
- 10 Mat Plots (9:01)

- 1 statistical tests (22:08)
- 2 Data Distribution and Simulation Finished (10:47)
- 3 Single Proportional Test (15:00)
- 4 Double Proportion (5:37)
- 5 T-Test Overview (6:06)
- 6 One Sample T-Test Default T-Test (6:56)
- 7 Two sample T-Test Independent sample T-test (11:33)
- 8 Paired T-Test (10:27)
- 9 F-Test ANOVA Tukey HSD (6:26)
- 10 Performing F-Test ANOVA Tukey HSD (12:39)
- 11 Chi Square One Sample Goodness of fit Test (7:40)
- 12 Chi-Square test for Independance (7:04)
- 13 Correlation Test (14:50)
- Good bye (1:19)

## Intended learners

This course is for all those who want to learn

- Statistical modelling in R with real world examples and datasets
- Develop and execute Hypothesis 1-tailed and 2-tailed tests in R
- Test differences, durability and data limitations
- Custom Data visualisations using R with limitations and interpretation
- Applications of Statistical tests
- Understand statistical Data Distributions and their functions in R
- How to interpret different output values and make conclusions
- To pick suitable statistical technique according to problem
- To pick suitable visualisation technique according to problem
- R packages which can improve statistical modelling

**Course description**

Before applying any data science model its always a good practice to understand the true nature of your data. In this Course we will cover fundamentals and applications of statistical modelling. We will use R Programming Language to run this analysis. We will start with Math, Data Distribution and statistical concepts then by using plots and charts we will interpret our data. We will use statistical modelling to prove our claims and use hypothesis testing to confidently make inferences.

This course is divided into 3 Parts

In the **1st section** we will cover following concepts

1. Normal Distribution

2. Binomial Distribution

3. Chi-Square Distribution

4. Densities

5. Cumulative Distribution function CDF

6. Quantiles

7. Random Numbers

8. Central Limit Theorem CLT

9. R Statistical Distribution

10. Distribution Functions

11. Mean

12. Median

13. Range

14. Standard deviation

15. Variance

16. Sum of squares

17. Skewness

18. Kurtosis

**2nd Section**

1. Bar Plots

2. Histogram

3. Pie charts

4. Box plots

5. Scatter plots

6. Dot Charts

7. Mat Plots

8. Plots for groups

9. Plotting datasets

**3rd Section** of this course will elaborate following concepts

1. Parametric tests

2. Non-Parametric Tests

3. What is statistically significant means?

4. P-Value

5. Hypothesis Testing

6. Two-Tailed Test

7. One Tailed Test

8. True Population mean

9. Hypothesis Testing

10. Proportional Test

11. T-test

12. Default t-test / One sample t-test

13. Two-sample t-test / Independent Samples t-test

14. Paired sample t-test

15. F-Tests

16. Mean Square Error MSE

17. F-Distribution

18. Variance

19. Sum of squares

20. ANOVA Table

21. Post-hoc test

22. Tukey HSD

23. Chi-Square Tests

24. One sample chi-square goodness of fit test

25. chi-square test for independence

26. Correlation

27. Pearson Correlation

28. Spearman Correlation

*In all the analysis we will practically see the real world applications using data sets csv files and r built in Datasets and packages. *

**What are the requirements or prerequisites for taking your course?**

- Course will teach how to install R and R-studio on Windows OS
- Students should know and familiar with MAC/Linux distribution software installation, if they are using one.
- Should know basic R fundamentals such as vectors, data frames etc.

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