## Statistics Essentials for Data Science

Statistics Essentials for Data Science. Key Learning Objectives Understand the fundamentals of statistics Work with different types of data How to plot different types of data Calculate the measures of central tendency, asymmetry, and variability Calculate correlation and covariance Distinguish and work with different types of distribution Estimate confidence intervals...

Deviare

OSL

3263

Statistics Essentials for Data Science.

**Key Learning Objectives**

- Understand the fundamentals of statistics
- Work with different types of data
- How to plot different types of data
- Calculate the measures of central tendency, asymmetry, and variability
- Calculate correlation and covariance
- Distinguish and work with different types of distribution
- Estimate confidence intervals
- Perform hypothesis testing
- Make data-driven decisions
- Understand the mechanics of regression analysis
- Carry out regression analysis
- Use and understand dummy variables
- Understand the concepts needed for data science even with Python and R!

**Course curriculum**

- Lesson 1 - Introduction
- Lesson 2 - Sample or population data?
- Lesson 3 - The fundamentals of descriptive statistics
- Lesson 4 - Measures of central tendency, asymmetry, and variability
- Lesson 5 - Practical example: descriptive statistics
- Lesson 6 - Distributions
- Lesson 7 - Estimators and estimates
- Lesson 8 - Confidence intervals: advanced topics
- Lesson 9 - Practical example: inferential statistics
- Lesson 10 - Hypothesis testing: Introduction
- Lesson 11 - Hypothesis testing: Let’s start testing!
- Lesson 12 - Practical example: hypothesis testing
- Lesson 13 - The fundamentals of regression analysis
- Lesson 14 - Subtleties of regression analysis
- Lesson 15 - Assumptions for linear regression analysis
- Lesson 16 - Dealing with categorical data
- Lesson 17 - Practical example: regression analysis

**Pre-requisites**

There are no prerequisites for taking this Statistics Essentials for Data Science certification course.

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**Modules**

- Introduction
- Sample or population data
- The fundamentals of descriptive statistics
- Measures of central tendency, asymmetry, and variability
- Practical example descriptive statistics
- Distributions
- Estimators and Estimates
- Confidence intervals advanced topics
- Practical example inferential statistics
- Hypothesis testing Introduction
- Hypothesis testing Let's start testing
- Practical example hypothesis testing
- The fundamentals of regression analysis
- Subtleties of regression analysis
- Assumptions for linear regression analysis
- Dealing with categorical data
- Practical example regression analysis