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 Will You Learn?
- Statistical modelling in R with real world examples and datasets
- Develop and execute Hypothesis 1-tailed and 2-tailed tests in R
- Understand statistical Data Distributions and their functions in R
- Custom Data visualisations using R with limitations and interpretation
- Applications of Statistical tests
Requirements
- 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.
Curriculum
Course consist of total 4h 57min of content, in total.
Section 1: Introduction
Section 2: Statistical Data Distribution
Section 3: Plots and Charts
Section 4: Statistical Tests and Applications
Section 5: Good Bye
About the Instructor
Jazeb Akram is a Data Scientist and has been working as a Developer consultant. He has been working as a Freelancer since 2011. He designed various Applications for many companies and also training individuals on development tools and languages. Jazeb Also has a university degree in computer science from the University of the Punjab, Pakistan, and a master's degree in Data Science from Western Sydney University, Australia.
You can read his full portfolio on his website jazebakram.com
Another Jazeb’s passion is teaching. He has joined the world's best learning platforms as an online instructor. Jazeb Akram says that the reason he came into teaching is his successful freelance experience because he loves to teach and gets very passionate and assisting others to achieve success in programming. That’s why at the end of his courses, students develop unique skill sets that they can use in a real-life and improve their career.
Jazeb loves to travel and explore new places.
On BitDegree, you have an opportunity to be guided by this amazing instructor to learn programming, complete projects and build apps. Jazeb Akram will teach you JavaScript, PHP, CSS, SQL and more. Choose the best course for you and dive into learning today!
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