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Data Analysis And Statistical Modeling In R

Data Analysis And Statistical Modeling In R

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

Overview

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

Course Information

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

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

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

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Course Specifications

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Course duration is 24 hours.

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