Statistics for Data Science

No prerequisites

1. Understand the fundamentals of statistics

2. Work with different types of data

3. How to plot different types of data

4. Calculate the measures of central tendency, asymmetry, and variability

5. Calculate correlation and covariance

6. Distinguish and work with different types of distribution

7. Estimate confidence intervals

8. Perform hypothesis testing

9. Make data-driven decisions

10. Understand the mechanics of regression analysis

11. Carry out regression analysis

12. Use and understand dummy variables

13. Understand the concepts needed for data science even with Python and R

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 Category Data
Lesson 17 - Practical Example: Regression Analysis

Understand the fundamentals of statistics, work with different types of data and Learn 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 and carry out regression analysis
Use and understand dummy variables
Understand the concepts needed for data science even with Python and R

The online delivery blends synchronous and asynchronous components. Students complete self-directed assignments hosted on the course platform. Weekly Forums support a required active, rubric-based student contributions that foster collaboration.

Exams may include essays, short answers, or MCQs. Formats include open/closed book. Time zones are considered. Assessments are formative and summative. Students have 2 weeks to review grades and must follow the appeal process in the Student Handbook.

Certificate issued on the Blockchain.

Please note that there are three semesters as intake periods as noted on the academic calendar.
Scholarship Awarded (Total Fee: €100)

Registration is done online.