# Statistics for Data Science

A brief summary of the topics covered in this course is as below. This is 25 hours course, it is suggested to complete this course in 3 weeks.

## Introduction of Statistics for Data Scientist

1. Introduction to basic statistics terms
2. Types of statistics
3. Types of data
4. Levels of measurement (nominal, ordinal, and interval/ratio)
5. Measures of central tendency
6. Measures of dispersion
7. Random variables
8. Concept of Set
9. Skewness, Kurtosis
10. Covariance and correlation
11. Data Visualization
12. Data summarization methods
13. Tables, Graphs, Charts, Histograms,
14. Frequency distributions
15. Box Plot
16. Chebychev’s Inequality on relationship

## Descriptive & Inferential Statistics for Data Scientist

1. Type of Probability distributions – discrete vs continuous distributions,
2. Cumulative Probabilities, Normal & Standard Normal Distribution
3. Discrete Distributions
4. Binomial Distributions
5. Poisson Distribution
6. Continuous Distributions
7. Uniform Distribution
8. Normal Distribution
9. Standard Normal Distribution
10. Exponential Distribution
11. Sampling methods
12. Interval Estimation
13. Central limit theorem – sampling, sampling distribution, properties of sampling distribution, central limit theorem, estimating mean using CLT

## Hypothesis Testing for Data Scientist

1. Concepts of hypothesis testing – business relevance, framing hypotheses, hypothesis testing process and p-value
2. Types of hypothesis tests – left- and right-tailed tests, two-tailed tests, types of errors, hypothesis testing using T-distribution
3. Industry demos on hypothesis testing (Excel) – two-sample mean test, two-sample proportion test, A/B testing
4. Z-Test, normal standard distribution
5. T-Test, t-stats, Student t distribution
6. T-stats vs. Z-stats
7. Type 1 & type 2 error
8. Bayes statistics (Bayes theorem)
9. Confidence interval (CI),  margin of error
10. Interpreting confidence levels and confidence intervals
11. Chi-square test
12. Chi-square distribution using python
13. Chi-square for goodness of fit test
14. When to use which statistical distribution?
15. Analysis of variance (ANOVA)
16. Assumptions to use ANOVA
17. ANOVA three type
18. Partitioning of variance in the ANOVA
19. Calculating using python
20. F-distribution
21. F-test (variance ratio test)
22. Determining the values of f
23. F distribution using python

## Project & Resources

1. Resources for practice
2. A Final Assignment