MSA

This course provides an introduction to fundamental concepts, techniques, and tools used in data analytics. Students will explore data collection, preprocessing, exploratory data analysis, basic statistical methods, and foundational analytics techniques. Emphasis is placed on understanding data-driven decision-making processes, analyzing real-world datasets, and effectively communicating analytical findings.

This course covers essential statistical methods and techniques used in data science. Topics include probability theory, statistical inference, hypothesis testing, regression analysis, and experimental design. Students will gain hands-on experience applying statistical methods to analyze and interpret data, supporting informed business decisions.

This course introduces programming concepts and techniques in Python and R for data analytics. Students will learn programming fundamentals, data manipulation, analysis, and visualization using industry-standard tools and libraries.

This course examines ethical, legal, and societal impacts of data analytics. Students discuss privacy, security, compliance, ethical standards, and responsibilities.

Students explore database concepts, relational databases, and Structured Query Language (SQL). Topics include database design, normalization, data management, and query optimization. Practical skills for managing, querying, and analyzing large datasets are emphasized.

Students gain foundational knowledge in machine learning algorithms, techniques, and tools. Topics include supervised and unsupervised learning, model evaluation, and practical applications.

This course focuses on the principles, architecture, and implementation of data warehousing and Extract, Transform, Load (ETL) processes. Students learn about designing and managing data warehouses, data integration techniques, and performance optimization strategies.

This course introduces the fundamental principles of big data and data science. Topics include data generation, storage, and processing using distributed computing frameworks. Students explore the data science lifecycle, from data collection to analysis and visualization, and gain familiarity with big data technologies and real-world applications.

This course emphasizes effective data visualization and communication. Students learn techniques for visualizing complex data, creating interactive dashboards, and communicating insights clearly to business stakeholders.

This course provides an in-depth exploration of advanced machine learning techniques, including deep learning, neural networks, ensemble methods, and model optimization strategies. Students will implement advanced algorithms, evaluate performance rigorously, and apply these models to complex, real-world datasets.

This course covers essential frameworks and best practices for data governance, ethics, and regulatory compliance in analytics. Students examine issues such as data privacy, security, ethical data use, and legal compliance.

This course introduces students to techniques and applications of natural language processing (NLP). Topics include text preprocessing, sentiment analysis, language modeling, text classification, and machine translation. Practical applications of NLP in business and analytics contexts are emphasized.

Students explore advanced methodologies and emerging topics in artificial intelligence (AI), including cutting-edge AI tools, ethical AI implementation, explainability, and scalable AI system design. Practical approaches for deploying robust AI systems in business environments are emphasized.

This course develops essential communication, presentation, and consulting skills required for analytics professionals. Students learn to effectively convey complex analytical insights and recommendations to stakeholders.

This advanced course covers statistical techniques for analyzing complex business datasets. Students learn predictive analytics, multivariate analysis, time-series modeling, and experimental design, applying these methods to strategic business decisions.

Students examine advanced techniques in generative artificial intelligence and reinforcement learning. Topics include generative adversarial networks (GANs), reinforcement learning algorithms, policy optimization, and their practical applications in business and analytics.