Faculty: Institute of Graduate Programs

The data science specialization focuses on extracting, analyzing, and interpreting 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-dependent roles in technology, finance, healthcare, and other industries.

Learning Objectives:

• Understand the fundamentals of data science and statistical analysis.
• Develop skills in data collection, cleaning, and pre-processing.
• 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.

Main Curriculum:

1. Introduction to Data Science
   • Overview of key concepts, principles, and practices in data science.
   • Basics of data collection, storage, and management.
2. Statistics for Data Science
   • Principles of statistics for data science, including descriptive, probabilistic, and inferential statistics.
   • Techniques for applying statistical methods to data analysis.
3. Data Collection and Pre-Processing
   • Principles of data collection and pre-processing, including data cleaning, transformation, and normalization.
   • Techniques for preparing data for analysis.
4. Machine Learning
   • Principles of machine learning, including supervised, unsupervised, 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 effective and efficient 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 datasets using tools such as 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. Practical/Applied Training
   • Hands-on experiences in data science settings, including internships in tech companies, data analytics firms, or research institutions.
   • Applying acquired skills in practical data science scenarios.
10. Data Science Capstone Project
    • A comprehensive project applying skills in data collection, machine learning, or data visualization.
    • Delivering a well-crafted data science project or analytical report or a research presentation.

Assessment Methods:

• Data collection and pre-processing projects, machine learning model evaluations, data visualization reports, programming assignments, big data technology projects, ethical analyses, practical training reports, capstone projects, group projects, and presentations.

Recommended Textbooks:

• "Data Science" by various authors.
• "Statistics for Data Science" by various authors.
• "Data Collection and Pre-Processing" 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.

Duration of Specialization:

Typically 4 years to obtain a bachelor's degree, including coursework, internships, and capstone projects. For advanced practice, a master's degree in data science can be pursued, usually taking an additional one to two years.

Certification:

Graduates can earn a degree in data science and pursue additional education or professional certifications, such as those offered by the Data Science Council of America (DASCA) or specialty 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 technology, finance, healthcare, and various data-driven industries. This specialization equips students with analytical, programming, and problem-solving skills necessary to excel in data science, supporting careers in various data-centered roles.