College: Graduate School of Natural and Applied Sciences

This major focuses on the collection, storage, analysis, and management of large and complex datasets to extract insights and support data-driven decision-making. Students acquire skills in data mining, machine learning, data visualization, database management, and data governance, preparing for careers in data science, analytics, and related fields.

Learning Goals:

• Understand the principles and techniques of big data.
• Develop skills in data mining and machine learning.
• Learn data visualization and reporting techniques.
• Explore database and data warehouse management.
• Analyze data governance and ethical considerations.
• Develop critical thinking, problem-solving, and analytical skills.

Core Curriculum:

1. Introduction to Big Data Analytics and Management - Overview of big data, its significance, and industry trends.
2. Data Mining and Machine Learning - Techniques for extracting insights and patterns from large datasets using machine learning algorithms.
3. Data Visualization - Methods for visualizing data to effectively communicate insights.
4. Database and Data Warehouse Management - Principles of designing, implementing, and managing databases and data warehouses.
5. Big Data Technologies - Understanding and utilizing big data technologies such as Hadoop, Spark, and NoSQL databases.
6. Statistical Analysis for Big Data - Statistical methods and models for analyzing big data.
7. Data Governance and Ethics - Principles of data governance, compliance, and ethical considerations in big data management.
8. Cloud Computing for Big Data - Leveraging cloud platforms to store, process, and analyze big data.
9. Practical/Applied Training - Hands-on experience in big data analytics and management, such as in data science teams, analytics firms, or tech companies.
10. Capstone Project - A comprehensive project applying big data analysis and management skills, such as developing a data mining model, creating a data visualization dashboard, or implementing a big data solution.

Assessment Methods:

• Projects in data mining and machine learning, data visualization reports, database and data warehouse management plans, big data technology implementations, statistical analysis studies, data governance and ethics essays, cloud computing projects, training reports, capstone projects, group projects, and presentations.

Recommended Textbooks:

• "Introduction to Big Data Analytics and Management" by various authors.
• "Data Mining and Machine Learning" by various authors.
• "Data Visualization" by various authors.
• "Database and Data Warehouse Management" by various authors.
• "Big Data Technologies" by various authors.
• "Statistical Analysis for Big Data" by various authors.
• "Data Governance and Ethics" by various authors.
• "Cloud Computing for Big Data" by various authors.

Prerequisites:

Basic knowledge of statistics and programming, along with an interest in data analysis and management.

Duration of the Program:

Typically 4 years for a bachelor's degree or 2 years for a master's degree in big data analysis and management.

Certification:

Graduates can earn certifications from professional organizations such as the Certified Analytics Professional (CAP) from INFORMS, Cloudera Certified Professional (CCP) Data Engineer, or equivalent big data certifications.

Target Audience:

Aspiring data scientists, data analysts, big data engineers, database managers, and professionals seeking careers in data science teams, analytics firms, tech companies, consulting firms, and related fields. This major equips students with analytical and technical skills necessary to excel in big data analytics and management, supporting careers in various data-dependent roles.