| Pre-Requisites | DA120 |
| Co-Requisites | None |
| Instructional Hours | 40 |
| Instructional Mode | Lecture |
| Delivery Mode | In-Person / Blended / Online |
This course, Probability and Statistics for Data Analytics (DS221), focuses on probability theory, descriptive and inferential statistics, hypothesis testing, and regression analysis. These topics are essential for understanding and interpreting data in various domains. Probability and statistics provide the foundation for data analytics and are used to make informed decisions based on data.
By the end of this course, students will be able to:
The course content will be presented through a series of lectures and practical exercises. Students will be evaluated through assignments and a final exam.
Throughout the semester, students will be given assignments that will require them to apply the concepts and techniques learned in the lectures to analyze real-world data. These assignments will allow students to practice their statistical analysis skills and will count towards the overall course grade.
At the end of the semester, students will take a final exam that will cover all the material presented in the lectures. The final exam will prioritize the understanding and application of probability and statistics concepts. The exam will count towards a significant portion of the overall course grade.
The following is a general outline of the topics covered in the course:
| Week | Topic |
|---|---|
| 1 | Introduction to Probability Theory |
| 2 | Probability Distributions |
| 3 | Inferential Statistics |
| 4 | Sampling Distributions and Central Limit Theorem |
| 5 | Estimation and Confidence Intervals |
| 6 | Hypothesis Testing |
| 7 | Measurement of Uncertainty |
| 8 | Analysis of Variance |
| 9 | Simple Linear Regression |
| 10 | Multiple Linear Regression |
| 11 | Nonparametric Methods |
| 12 | Course Review |