Faculty: INSTITUTE OF GRADUATE PROGRAMS The Data Science major focuses on the extraction, analysis, and interpretation of complex data to uncover insights and support decision-making. Students develop skills in statistics, machine learning, data visualization, and programming. Graduates are prepared for careers in data analysis, data engineering, machine learning, and various data-driven roles in tech, finance, healthcare, and other industries. **Learning Objectives:** - Understand the fundamentals of data science and statistical analysis. - Develop skills in data collection, cleaning, and preprocessing. - Learn techniques for applying machine learning algorithms and models. - Explore principles of data visualization and storytelling. - Analyze and interpret complex data sets and trends. - Develop critical thinking, problem-solving, and programming skills for effective data science practice. **Major Outline:** 1. **Introduction to Data Science** - Overview of key concepts, principles, and practices in data science. - Fundamentals of data collection, storage, and management. 2. **Statistics for Data Science** - Principles of statistics for data science, including descriptive statistics, probability, and inferential statistics. - Techniques for applying statistical methods to data analysis. 3. **Data Collection and Preprocessing** - Principles of data collection and preprocessing, including data cleaning, transformation, and normalization. - Techniques for preparing data for analysis. 4. **Machine Learning** - Principles of machine learning, including supervised and unsupervised learning, and deep learning. - Techniques for building, training, and evaluating machine learning models. 5. **Data Visualization** - Principles of data visualization, including visual design, chart types, and storytelling with data. - Techniques for creating effective and informative data visualizations. 6. **Programming for Data Science** - Principles of programming for data science, including Python, R, and SQL. - Techniques for writing efficient and effective code for data analysis. 7. **Big Data Technologies** - Principles of big data technologies, including distributed computing, cloud storage, and data processing frameworks. - Techniques for working with large-scale data sets using tools like Hadoop, Spark, and NoSQL databases. 8. **Ethics in Data Science** - Principles of ethics in data science, including data privacy, security, and ethical decision-making. - Techniques for ensuring ethical practices in data collection, analysis, and reporting. 9. **Internship/Practicum** - Real-world experiences in data science settings, including internships in tech companies, data analysis firms, or research institutions. - Application of learned skills in practical data science scenarios. 10. **Capstone Project in Data Science** - Comprehensive project applying skills in data collection, machine learning, or data visualization. - Delivery of a polished data science project, analysis report, or research presentation. **Assessment Methods:** - Data collection and preprocessing projects, machine learning model evaluations, data visualization reports, programming assignments, big data technology projects, ethics analyses, internship/practicum reports, capstone projects, group projects, and presentations. **Recommended Textbooks:** - "Data Science" by various authors. - "Statistics for Data Science" by various authors. - "Data Collection and Preprocessing" by various authors. - "Machine Learning" by various authors. - "Data Visualization" by various authors. - "Programming for Data Science" by various authors. - "Big Data Technologies" by various authors. - "Ethics in Data Science" by various authors. **Prerequisites:** Basic knowledge of mathematics, statistics, and an interest in data analysis and programming. **Major Duration:** Typically 4 years for a bachelor's degree, including coursework, internships, and capstone projects. For advanced practice, a Master's degree in Data Science may be pursued, which typically takes an additional 1-2 years. **Certification:** Graduates may receive a degree in Data Science and pursue further education or professional certifications, such as those offered by the Data Science Council of America (DASCA) or specialized certifications in areas like machine learning or data analytics. **Target Audience:** Aspiring data scientists, data analysts, data engineers, machine learning specialists, and professionals seeking careers in tech, finance, healthcare, and various data-driven industries. This major equips students with the analytical, programming, and problem-solving skills needed to excel in data science, supporting careers in various data-driven roles.