Goal: The primary objective of an introductory statistics course is to establish a solid foundation in statistical concepts and methodologies, equipping students with the necessary skills to analyze and interpret data effectively. By the end of the course, students will have achieved the following learning outcomes:

• Proficiency in essential mathematical terms and concepts required for understanding statistical inference.

• Familiarity with the core principles of statistical inference, including probability theory, sampling methods, and estimation.

• Competence in applying descriptive statistics to summarize and visually represent data, encompassing measures of central tendency, variability, and correlation.

• Ability to employ inferential statistics to draw meaningful conclusions about a population based on sample data, employing techniques such as hypothesis testing and confidence intervals.

• Skill in utilizing statistical software and tools like STATA, R, and/or Python to analyze and interpret data, as well as effectively communicate and interpret the results of statistical analyses.

Through this course, students will develop a strong statistical foundation, enabling them to apply statistical techniques confidently in various academic and professional settings.

Textbooks:
(1) Walpole et al. (2022), Probability & Statistics for Engineers & Scientists (9th edition), Pearson. [Link]
(2) Békés and Kézdi (2021), Data Analysis for Business, Economics, and Policy, Cambridge University Press. [Link]
(3) James et al. (2021), An Introduction to Statistical Learning (2nd edition), Springer. [Link]

Topics:

Probability and Random Variables

Mathematical Expectation

Probability Distributions

Sampling

Sample Estimation Problems

Hypothesis Tests

Linear Regressions - optional

Goal: The purpose of this course is to familiarize students with the methods of making data-driven decisions that can enhance business performance and results. The course focuses on achieving the following objectives:

• utilizing tools and techniques to investigate and analyze data.

• comprehending patterns and relationships within the data.

• summarizing the data in a manner that is meaningful and informative.

• presenting insights in a clear and actionable manner for stakeholders.

Ultimately, the Advanced Business Analytics course aims to empower decision-makers within an organization to make well-informed choices based on data-driven insights, resulting in improved business performance and outcomes.

Textbooks:
(1) Mike X Cohen (2021), Linear Algebra: Theory, Intuition, Code, Sincxpress. [Link]
(2) Prince (2018), Predictive Analytics for Business Strategy, McGraw Hill. [Link]
(3) Békés and Kézdi (2021), Data Analysis for Business, Economics, and Policy, Cambridge University Press. [Link]
(4) James et al. (2021), An Introduction to Statistical Learning (2nd Edition), Springer. [Link]

Topics:

Introductory Linear Algebra for Data Science

Linear Regressions

Dimension Reduction

Experimental Design

[Link to the course page]

TBD