Foundations of Data Science

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The Graduate Certificate in Foundations of Data Science provides a comprehensive foundation in data science theories, methods, and applications. Through hands-on projects and industry collaborations, students develop advanced skills in data processing, machine learning, and data visualization. The program prepares graduates for careers in data analytics, data science, and related fields, enhancing their job prospects and competitiveness in the data-driven job market.

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Degree Overview:

• Overview:
• The Graduate Certificate in Foundations of Data Science (GCFDS) provides a solid foundation in data science theories, methods, and applications using complex data from various fields.
• Objectives:
• Learn to store, prepare, visualize, analyze, model, and communicate data.
• Gain basic terminology and concepts in data science.
• Understand the components of a data science workflow and apply it to solve specific questions.
• Develop advanced knowledge in data processing and machine learning, including data cleaning, wrangling, regression, classification tasks, machine learning approaches, regression trees, deep neural networks, data visualization, and complex result communication.
• Apply machine learning algorithms and associated tools in a cloud computing environment.
• Understand limitations and ethical concerns around data collection, machine learning, and real-world problem-solving.
• Enhance competency in programming languages like Python.

Outline:

• Content:
• Introduction to data science and analytics
• Data visualization
• Data wrangling
• Introduction to data science and analytics
• Data management
• Final project
• Structure:
• 4-month full-time or 8-month part-time program
• 12.0 credit hours
• Course Schedule:
• Data Science 5010/Business Analytics 5010:
Introduction to Data Science and Analytics in Python I
• Data Science 5020/Business Analytics 5020:
Data Visualization
• Data Science 5050/Business Analytics 5050:
Data Wrangling
• Data Science 5110/Business Analytics 5110:
Introduction to Data Science and Analytics in Python II
• Data Science 5140/Business Analytics 5140:
Data Management
• Data Science 5180:
Final Project
• Individual Modules:
• Introduction to Data Science and Analytics in Python I:
Introduces data science concepts, Python programming basics, data exploration, data cleaning, data analysis, data visualization, and machine learning fundamentals.
• Data Visualization: Explores data visualization techniques, including data storytelling principles, data visualization tools, interactive data visualizations, and dashboard creation.
• Data Wrangling: Covers data munging, data aggregation, data transformation, data normalization, data restructuring, data validation, and data quality control techniques.
• Introduction to Data Science and Analytics in Python II: Advanced Python programming, data mining techniques, unsupervised machine learning algorithms, supervised machine learning algorithms, model evaluation and selection, and machine learning deployment.
• Data Management: Examines data management systems, database design, data security, data warehousing, big data management, cloud computing for data management, and data ethics.
• Final Project: A capstone project that allows students to apply their data science skills to a real-world problem, including data collection, analysis, modeling, interpretation, and communication of findings.

Assessment:

• Assessment Methods:
• Assignments
• Quizzes
• Exams
• Projects
• Presentations
• Assessment Criteria:
• Understanding of concepts and theories
• Problem-solving skills
• Analytical and critical thinking abilities
• Communication and presentation skills
• Practical application of knowledge and skills

Teaching:

• Teaching Methods:
• Lectures
• Tutorials
• Hands-on exercises
• Group projects
• Online resources
• Faculty:
• Experienced professors and industry professionals with expertise in data science, machine learning, statistics, and computer science.
• Unique Approaches:
• Focus on real-world problem-solving and industry applications.
• Hands-on learning through projects, assignments, and a capstone project.
• Collaboration with industry partners for practical experience and project opportunities.

Careers:

• Potential Career Paths:
• Business and Finance
• Health Sciences and Healthcare
• S.T.E.A.M.
(Science, Technology, Engineering, Arts, and Math)
• Data Analytics and Data Science
• Research and Education
• Consulting and Entrepreneurship
• Opportunities:
• Data Scientist
• Data Analyst
• Business Intelligence Analyst
• Statistician
• Machine Learning Engineer
• Data Visualization Specialist
• Software Engineer
• Database Administrator
• Outcomes:
• Enhanced job prospects in data-driven industries.
• Increased competitiveness in the competitive job market.
• Career advancement and leadership opportunities in data-related fields.