Course Overview

This course, "Data Analytics" (BA 6474 705), is designed for students in the Business Administration & Business Leadership Program at SMU. It provides a comprehensive introduction to statistical methodologies and their application in business decision-making. The course covers foundational topics in statistics, data analysis, and data science, with a focus on practical skills using Excel and Tableau.

Course Topics and Structure

• Data Science Process: Introduction to the steps involved in extracting insights from data, including data collection, exploratory analysis, modeling, evaluation, and communication of results.

• Probability Concepts: Fundamental principles of probability, essential for understanding uncertainty in business contexts.

• Descriptive Statistics & Visualization: Techniques for summarizing and visualizing data using tables, graphs, and summary statistics.

• Regression Analysis: Introduction to correlation, simple linear regression, and multiple linear regression, including the analysis of categorical variables and interaction terms.

• Sampling and Quality Control: Concepts of sampling distributions, confidence intervals, and methods for ensuring data quality.

• Forecasting: Methods for predicting future trends based on historical data, applicable to both short- and long-term business planning.

• Decision-Making Under Uncertainty: Application of statistical inference and hypothesis testing to support managerial decisions.

• Linear Programming: Introduction to optimization techniques for resource allocation and decision-making.

Student Learning Objectives

• Identify and apply appropriate statistical methods to business problems.

• Analyze statistical results to determine significance and randomness.

• Develop statistical literacy and confidence in data analysis.

• Integrate data analysis software (Excel, Tableau) with statistical learning.

Course Requirements and Tools

• Software: Microsoft Excel (with Data Analysis ToolPak) and Tableau for data analysis and visualization.

• Technical Requirements: Reliable internet, computer, and access to Google Colab for assignments and projects.

• Assignments: Individual assignments, quizzes, class participation, and a final project involving the full data science process.

Assessment and Grading

Grades are assigned on a curve, with no more than 45% of students receiving an A or A-.

Course Schedule (Selected Topics)

Key Statistical Concepts Covered

• Descriptive Statistics: Measures of central tendency (mean, median, mode), measures of dispersion (variance, standard deviation, range).

• Probability: Basic probability rules, random variables, probability distributions.

• Normal Distribution: Properties, applications, and the empirical rule.

• Sampling Distributions & Confidence Intervals: Estimating population parameters and quantifying uncertainty.

• Hypothesis Testing: Formulating and testing statistical hypotheses for means and proportions.

• Correlation & Regression: Assessing relationships between variables and making predictions.

• Logistic Regression: Modeling binary outcomes.

Course Policies and Support

• Attendance and participation are required for success.

• Assignments and quizzes are based on class material and are essential for mastering concepts.

• Use of AI tools is permitted with disclosure, except for data visualization assignments (Excel and Tableau only).

• Support services are available for writing, tutoring, counseling, and accommodations.

Instructor Information

• Instructor: Samira Fazel

• Email: samira@smu.edu

• Office: DBMQ 2295

• Office Hours: By appointment

• Faculty Website: https://www.smu.edu/cox/academics/faculty/samira-fazel

Additional info:

• This syllabus aligns closely with the standard topics of an Introductory Statistics course, including probability, descriptive statistics, distributions, inference, regression, and statistical software use.

• Students are expected to apply statistical thinking to real-world business problems and communicate results effectively.