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2021-01-28 07:01:04
30-Day Money-Back Guarantee
Development Data Science Statistical Modeling

Data Analysis and Statistical Modeling in R

Learn the foundation of Data Science, Analytics and Data interpretation using statistical tests with real world examples
Hot & New
Rating: 4.3 out of 54.3 (30 ratings)
9,951 students
Created by Jazeb Akram
Last updated 2/2021
English
English [Auto]
30-Day Money-Back Guarantee

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

Course content

5 sections • 37 lectures • 4h 58m total length

  • Preview04:06
  • Preview01:28
  • All Exercise files downloadable link
    00:07
  • Preview00:52
  • Preview04:01
  • Preview09:37

  • Preview04:44
  • Preview08:15
  • Preview13:53
  • Preview03:37
  • Preview12:06
  • Chapter Assessment
    4 questions

  • Preview16:07
  • Bar plots for groups
    07:48
  • Pie Charts and Graphical Parameters
    10:35
  • Finishing Pie charts
    06:50
  • Histograms
    15:27
  • Understanding Urban Population of US using Histogram6
    02:54
  • Box Plots
    09:07
  • Box plots for groups
    03:44
  • Scatter Plots
    15:03
  • Mat Plots
    09:01
  • Chapter Assessment
    4 questions

  • Statistical tests PDF for reference
    00:01
  • statistical tests
    22:08
  • Power of a test; Type 1 and 2 errors
    00:09
  • Data Distribution and Simulation Finished
    10:47
  • Single Proportional Test
    15:00
  • Double Proportion
    05:38
  • T-Test Overview
    06:06
  • One Sample T-Test Default T-Test
    06:56
  • Two sample T-Test Independent sample T-test
    11:33
  • Paired T-Test
    10:27
  • F-Test ANOVA Tukey HSD
    06:27
  • Performing F-Test ANOVA Tukey HSD
    12:39
  • Preview07:41
  • Chi-Square test for Independence
    07:04
  • Correlation Test
    14:50
  • Chapter Assessment
    10 questions

  • Course Assessment
    16 questions
  • See yOu
    01:19

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 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.

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

Instructor

Jazeb Akram
Data Scientist, Web Consultant
Jazeb Akram
  • 4.2 Instructor Rating
  • 3,439 Reviews
  • 74,147 Students
  • 11 Courses

Jazeb Akram is a Data Scientist, and have worked as a web consultant previously. He has been working as a Freelancer since 2011.He designed various Applications for many companies as a consultant. Jazeb Also has a university degree in computer science  and a master degree in Data Science from Sydney.

You can read his full portfolio on his website jazebakram


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