Faculty: Graduate School of Science and Engineering This major focuses on the collection, processing, and interpretation of large datasets to uncover insights, make data-driven decisions, and solve complex problems. Students develop skills in statistics, data mining, machine learning, and data visualization. Graduates are prepared for careers in data analysis, business intelligence, and related fields. **Learning Objectives:** - Understand the fundamentals of data analytics and its applications. - Develop skills in statistical analysis, data mining, and machine learning. - Learn techniques for data collection, cleaning, and preprocessing. - Explore principles of data visualization and storytelling. - Analyze and interpret complex datasets to derive actionable insights. - Develop critical thinking, problem-solving, and data-driven decision-making skills. **Major Outline:** 1. **Introduction to Data Analytics** - Overview of key concepts, principles, and practices in data analytics. - Fundamentals of data types, sources, and analytics tools. 2. **Statistical Analysis** - Principles of statistical analysis, including descriptive statistics, inferential statistics, and hypothesis testing. - Techniques for applying statistical methods to analyze data. 3. **Data Mining** - Principles of data mining, including clustering, classification, and association rule mining. - Techniques for extracting patterns and knowledge from large datasets. 4. **Machine Learning** - Principles of machine learning, including supervised and unsupervised learning algorithms. - Techniques for building, training, and evaluating machine learning models. 5. **Data Visualization** - Principles of data visualization, including chart types, design principles, and storytelling with data. - Techniques for creating effective and engaging data visualizations. 6. **Data Management** - Principles of data management, including database design, data warehousing, and data governance. - Techniques for managing and organizing large datasets. 7. **Big Data Technologies** - Principles of big data technologies, including Hadoop, Spark, and cloud computing. - Techniques for processing and analyzing big data. 8. **Practicum in Data Analytics** - Real-world experiences in data analytics, including internships, projects, and hands-on data analysis tasks in industry or research settings. - Application of learned skills in practical data analytics scenarios. 9. **Capstone Project in Data Analytics** - Comprehensive project applying skills in statistical analysis, data mining, or machine learning. - Delivery of a polished data analytics project, report, or presentation. **Assessment Methods:** - Statistical analysis reports, data mining projects, machine learning models, data visualizations, data management plans, big data analyses, practicum reports, capstone projects, group projects, and presentations. **Recommended Textbooks:** - "Data Science for Business" by Foster Provost and Tom Fawcett. - "The Elements of Statistical Learning" by Trevor Hastie, Robert Tibshirani, and Jerome Friedman. - "Data Mining: Concepts and Techniques" by Jiawei Han, Micheline Kamber, and Jian Pei. - "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurélien Géron. - "Storytelling with Data" by Cole Nussbaumer Knaflic. - "Data Management" by various authors. - "Big Data Technologies" by various authors. **Prerequisites:** Basic knowledge of mathematics, statistics, and computer science. Suitable for students interested in data analysis, business intelligence, and related fields. **Major Duration:** Typically 4 years for a bachelor's degree, including coursework, projects, practicum, and internships. Additional advanced degrees or certifications may be required for specialized roles. **Certification:** Graduates may receive a degree in Data Analytics and pursue further education or professional certifications in related fields, such as a Master's in Data Science, Business Analytics, or related disciplines. Certifications from organizations like the Institute for Operations Research and the Management Sciences (INFORMS) or the Data Science Council of America (DASCA) may also be pursued. **Target Audience:** Aspiring data analysts, business intelligence specialists, data scientists, and individuals seeking careers in data analysis, business intelligence, consulting firms, tech companies, and related fields. This major equips students with the analytical, technical, and problem-solving skills needed to excel in data analytics, supporting careers in data analysis, business intelligence, and related fields.