Description

Learn the foundation of Data Science, Analytics, and Data interpretation using statistical tests with real-world examples.

Before applying any data science model it's always a good practice to understand the true nature of your data. In this course, we will cover the fundamentals and applications of statistical modeling. We will use R Programming Language to run this analysis. We will start with Math, Data Distribution, and statistical concepts than by using plots and charts we will interpret our data. We will use statistical modeling 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 the 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.

Basic knowledge

The 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

Learn the foundation of Data Science, Analytics, and Data interpretation using statistical tests with real-world examples.

Before applying any data science model it's always a good practice to understand the true nature of your data. In this course, we will cover the fundamentals and applications of statistical modeling. We will use R Programming Language to run this analysis. We will start with Math, Data Distribution, and statistical concepts than by using plots and charts we will interpret our data. We will use statistical modeling 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 the 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.

Basic knowledge

The 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

The 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

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

Statistical modeling 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 visualizations 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 a suitable statistical technique according to problem

To pick a suitable visualization technique according to problem

R packages that can improve statistical modeling

Develop and execute Hypothesis 1-tailed and 2-tailed tests in R

Test differences, durability, and data limitations

Custom Data visualizations 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 a suitable statistical technique according to problem

To pick a suitable visualization technique according to problem

R packages that can improve statistical modeling

University and college data science students

Data Science aspirants

Beginners who want to perform statistical modelling and learn about its applications

people who want to shift from SPSS and EXCEL to R to perform statistical analysis

Data Science aspirants

Beginners who want to perform statistical modelling and learn about its applications

people who want to shift from SPSS and EXCEL to R to perform statistical analysis

• Lifetime Access to Each Course

• Certificate on Completion of Course

• No Extra Charges Or Admin Fees

• Easy Access to Courses

• High Priority Support After Sales.

• Big Discounts on Individual Courses

• Certificate on Completion of Course

• No Extra Charges Or Admin Fees

• Easy Access to Courses

• High Priority Support After Sales.

• Big Discounts on Individual Courses

IT and Computing courses are available to study on our learning platform.

Adult education is the non-credential activity of gaining skills and improved education.

Online education is electronically supported learning that relies on the Internet for teacher/student interaction.

A short course is a learning programme that gives you combined content or specific skills training in a short period of time. Short courses often lean towards the more practical side of things and have less theory than a university course – this gives you a more hands-on experience within your field of interest.

Course duration is 24 hours.