Data Analysis And Statistical Modeling In R

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

What you'll 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

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.

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 PartsIn the 1st section we will cover following concepts1. Normal Distribution2. Binomial Distribution3. Chi-Square Distribution4. Densities5. Cumulative Distribution function CDF6. Quantiles7. Random Numbers8. Central Limit Theorem CLT9. R Statistical Distribution10. Distribution Functions11. Mean12. Median13. Range14. Standard deviation15. Variance16. Sum of squares17. Skewness18. Kurtosis2nd Section1. Bar Plots2. Histogram3. Pie charts4. Box plots5. Scatter plots6. Dot Charts7. Mat Plots8. Plots for groups9. Plotting datasets3rd Section of this course will elaborate following concepts1. Parametric tests2. Non-Parametric Tests3. What is statistically significant means?4. P-Value5. Hypothesis Testing6. Two-Tailed Test7. One Tailed Test8. True Population mean9. Hypothesis Testing10. Proportional Test11. T-test12. Default t-test / One sample t-test13. Two-sample t-test / Independent Samples t-test14. Paired sample t-test15. F-Tests16. Mean Square Error MSE17. F-Distribution18. Variance19. Sum of squares20. ANOVA Table21. Post-hoc test22. Tukey HSD23. Chi-Square Tests24. One sample chi-square goodness of fit test25. chi-square test for independence26. Correlation27. Pearson Correlation28. Spearman CorrelationIn all the analysis we will practically see the real world applications using data sets csv files and r built in Datasets and packages.

Who this course is for:
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