Faculty: Graduate School of Natural and Applied Sciences This major focuses on the collection, storage, analysis, and management of large and complex datasets to derive insights and support data-driven decision-making. Students gain skills in data mining, machine learning, data visualization, database management, and data governance, preparing for careers in data science, analytics, and related fields. **Learning Objectives:** - Understand big data principles and technologies. - Develop skills in data mining and machine learning. - Learn data visualization and reporting techniques. - Explore database management and data warehousing. - Analyze data governance and ethical considerations. - Develop critical thinking, problem-solving, and analytical skills. **Major Outline:** 1. **Introduction to Big Data Analytics and Management** - Overview of big data, its importance, 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 communicate insights effectively. 4. **Database Management and Data Warehousing** - Principles of database design, implementation, and management of data warehouses. 5. **Big Data Technologies** - Understanding and using 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 for big data storage, processing, and analysis. 9. **Internship/Practicum** - Real-world experiences in big data analytics and management settings, such as data science teams, analytics firms, or technology companies. 10. **Capstone Project** - Comprehensive project applying big data analytics and management skills, such as developing a data mining model, creating a data visualization dashboard, or implementing a big data solution. **Assessment Methods:** - Data mining and machine learning projects, data visualization reports, database management and data warehousing plans, big data technology implementations, statistical analysis studies, data governance and ethics essays, cloud computing projects, internship 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 Management and Data Warehousing" 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, programming, and an interest in data analytics and management. **Major Duration:** Typically 4 years for a bachelor's degree or 2 years for a master's degree in Big Data Analytics and Management. **Certification:** Graduates may pursue 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 administrators, and professionals seeking careers in data science teams, analytics firms, technology companies, consulting firms, and related fields. This major equips students with the analytical and technical skills needed to excel in big data analytics and management, supporting careers in various data-driven roles.