Data Analytics and Visualization

This course introduces students to the foundations of data analytics, focusing on descriptive analytics and effective data visualization techniques. Students learn how to summarize and explore datasets to uncover initial insights, using tools such as spreadsheets and business intelligence software (e.g., Microsoft Excel and Power BI) to perform exploratory data analysis. Key topics include data cleaning, summary statistics, and principles of visual design for dashboards and reports. Students practice translating complex data into clear, impactful charts and graphs, with case examples drawn from various industries (such as retail sales data and healthcare quality metrics) to illustrate how descriptive analytics informs business decisions. Hands-on exercises involve creating interactive visualizations and dashboard presentations that communicate data-driven stories to stakeholders.

Learning Outcomes

By the end of this course, students should be able to:

• Summarize and describe business datasets using appropriate statistical measures and visual representations.
• Utilize data visualization tools to create effective charts, dashboards, and infographics that highlight key insights.
• Identify meaningful patterns and trends in data and interpret their implications for business contexts.
• Communicate analytical findings clearly and concisely to both technical and non-technical audiences through well-designed visual presentations.

Applied Statistical Modelling

This course builds proficiency in statistical methods for business analytics. Students study probability distributions, sampling methods, and inferential statistics as a basis for rigorous data analysis. Key topics include hypothesis testing, confidence intervals, and regression modelling (linear and logistic regression) to examine relationships between variables and to make predictions. Using statistical software (such as R or Python libraries), students analyse real-world datasets – for example, modelling factors affecting consumer purchasing behaviour or forecasting sales – to gain practical experience. The course emphasizes proper model formulation, assumption checking, and result interpretation. Through applied assignments, students learn to validate models and assess their predictive power, setting the stage for more advanced analytical techniques.

Learning Outcomes

Upon completing this course, students will be able to:

• Apply fundamental probability and statistical concepts to model uncertainty in business data.
• Conduct hypothesis tests and construct confidence intervals to support data-driven business decisions.
• Build and interpret regression models (including multiple linear regression and logistic regression) to identify key predictors and forecast outcomes.
• Use statistical software to analyze datasets and draw valid conclusions, while articulating the limitations and assumptions of statistical models.

Data Management and Business Intelligence

This course covers the technologies and practices for managing data and extracting business intelligence from databases. Students learn how organizational data is stored and organized, exploring relational database systems and data warehousing concepts. Topics include data modeling (entity-relationship modeling), Structured Query Language (SQL) for querying databases, ETL (Extract, Transform, Load) processes for data integration, and the design of data warehouses and data marts to support reporting and analysis. The course also introduces business intelligence (BI) tools and techniques for generating reports and dashboards from enterprise data. Students engage in practical exercises such as writing SQL queries to retrieve and aggregate data from transactional databases, and developing a simple data warehouse schema for a case company (e.g., a retail chain consolidating sales and inventory data). Emphasis is placed on data quality, governance, and ensuring that data is transformed into meaningful insights through BI platforms.

Learning Outcomes

By the end of this course, students will be able to:

• Design basic relational database schemas and use SQL to store, manipulate, and query data effectively.
• Explain the concepts of data warehouses and data lakes, and implement simple ETL workflows to combine data from multiple sources.
• Utilize business intelligence software to create standardized reports and interactive dashboards that support decision-making.
• Apply data governance and quality assurance practices to ensure the reliability and integrity of data used for business analytics.

Predictive Analytics and Machine Learning

This course focuses on predictive modeling techniques and machine learning algorithms used to uncover patterns and forecast future outcomes from data. Building on statistical modeling foundations, students explore both supervised learning methods (for prediction and classification) and unsupervised learning methods (for pattern discovery). Key topics include decision trees, random forests, clustering (e.g., k-means), and time series forecasting, as well as model evaluation techniques such as cross-validation and performance metrics (e.g., accuracy, RMSE, ROC-AUC). Students gain hands-on experience using programming tools (such as Python with scikit-learn or R) to develop and refine predictive models on real datasets – for instance, predicting customer churn for a telecom company or segmenting consumers based on purchasing behavior. The course emphasizes the end-to-end analytics process: from data preprocessing and feature engineering to model training, tuning, and interpretation of results. Ethical considerations in model building, such as avoiding algorithmic bias, are also discussed.

Learning Outcomes

Upon completion of this course, students will be able to:

• Develop predictive models using machine learning algorithms to address business problems (e.g., classification and regression tasks).
• Evaluate and compare model performance using appropriate metrics and validation techniques to ensure robustness and prevent overfitting.
• Apply clustering and other unsupervised learning methods to discover meaningful segments or patterns in complex datasets.
• Interpret the outcomes of machine learning models and translate them into actionable business insights and recommendations.

Analytics Strategy, Governance, and Ethics

By the end of this course, students will:

• Formulate strategies for integrating analytics into business processes and decision-making, ensuring that analytics efforts align with organizational goals and deliver value.

• Demonstrate understanding of data governance principles by designing policies for data privacy, security, and quality management within an organization.
• Identify and evaluate ethical issues in analytics (such as bias in algorithms or misuse of data), and propose solutions that uphold legal standards and ethical best practices.

• Develop effective communication and leadership approaches to promote data-driven insights to stakeholders, facilitating informed strategic decisions and a strong analytics-driven organizational culture.

Specialization Tracks and Elective Courses 

After completing the core curriculum, students at Texas Business School tailor their learning by choosing a specialization track, each comprising at least four elective courses (12 credits) focused on a specific industry or advanced analytical skill set. The available tracks—Financial Analytics, Marketing and Retail Analytics, Healthcare Analytics, and Advanced Analytics and AI—are designed to reflect high-demand sectors and equip students with applied expertise. Each track features practical case studies and hands-on projects using real-world datasets, such as consumer data in marketing or patient data in healthcare, allowing students to gain relevant, domain-specific experience while deepening their analytical capabilities.