Statistics for Data Science

Course Information:
Course Title: Statistics for Data Science
Type: Fundamental
Credit: 6
Coefficient: 3

Lecturer's Information:

Lecturer: Dr. Khadra Bouanane

Contact: bouanane.khadra@univ-ouargla.dz
Office Hours: Monday: 8 am-12 am, Wednesday: 8 am-12 am

Course Description:

This course comprehensively introduces statistical concepts essential for AI and Data Science. Topics include random variables, probability distributions, inequalities, transformations, random vectors, convergence theorems, central limit theorem, sampling distribution, estimation, and hypothesis testing. Emphasis is placed on the application of statistical methods in data science scenarios.

Targeted Audience

This course is dedicated to AI and DS Master 1 students.

 
 
Prerequisites:
Basic Knowledge of algebra and Calculus.
Probability Theory.
Descriptive Statistics.
 
Learning Objectives:

Understand the fundamentals of random variables and their role in statistical analysis.
Explore various probability distributions and their applications.
Investigate the properties of random vectors and analyze the distribution of the sum of random variables.
Gain insight into essential convergence theorems, including the central limit theorem.
Understand the concept of sampling distribution and its significance in inferential statistics.
Develop skills in estimation techniques, including point estimation and interval estimation.
Learn hypothesis testing principles and apply them to real-world data science problems.

Assessment 
  1. Grading: 40%

- Assessments: 85%
- Class Participation: 15%

     2. Exam: 60%
 Schedule:

 
References:

  1. Spiegelhalter, D. (2019). "The Art of Statistics: Learning from Data."

  2. James, G., Witten, D., Hastie, T., and Tibshirani, R. (2013). "An Introduction to Statistical Learning."

  3. Hastie, T., Tibshirani, R., and Friedman, J. (2009). "The Elements of Statistical Learning."

  4. Witte, R. S., and Witte, J. S. (2005). "Statistics."

  5. Urdan, T. C. (2005). "Statistics in Plain English."

  6. Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., and Rubin, D. B. (2013). "Bayesian Data Analysis."

  7. McKinney, W. (2018). "Python for Data Analysis."

  8. Wickham, H., and Grolemund, G. (2017). "R for Data Science."

  9. Reinhart, A. (2015). "Statistics Done Wrong: The Woefully Complete Guide."

  10. Downey, A. B. (2014). "Think Stats."

Statistics for Data Science

Teacher: BOUANANE Khadra