Data Science with Statistics MSc, PGDip, PGCert

Program Overview

This MSc in Data Science with specialization in Statistics equips students with advanced statistical methods and computational skills to solve real-world problems using data. The program combines theoretical knowledge with practical applications, including a research project or dissertation, and offers strong industry connections for placements and collaborative research. Graduates are highly sought-after professionals in the rapidly growing data science field, with expertise in solving complex data challenges and generating valuable insights for organizations.

Program Outline

Program Description:

This MSc program blends advanced statistical methods with essential computational skills to equip students to handle significant amounts of unstructured data effectively. Combining subject-specific taught modules and a research project or dissertation, this program provides a comprehensive understanding of data science theory and its practical applications across various fields.

Key Features:

• Focus on advanced statistical concepts and data analysis techniques.
• Development of strong computational skills for handling large data sets.
• Real-world project work and a deep dive into a specific data science area in the dissertation.
• Strong industry connections with opportunities for placements and collaborative research.

Benefits:

• Become a highly sought-after professional in the rapidly growing data science field.
• Develop a competitive edge in the job market with your specialization in statistics.

Outline:

Program Structure: The program is divided into three phases:

Phase 1:

Introduction to core knowledge and skills in statistics and computer science alongside introductory Python training. Focus on data science modules in data visualization, machine learning, time series analysis, and Bayesian inference. The group project involves developing and evaluating a data science solution for a complex real-world problem in collaboration with industry members.

Phase 3:

Independent research and development project. Conduct research under the supervision of an ervaren academic within a University research lab or in collaboration with industry or your current employer. Individual supervision and support from industry partners are provided.

Course Schedule: The course starts in mid-September. Part-time students have the flexibility to complete the program over two years, allowing them to align their assessed work with their current job roles and potentially conduct the individual project within their workplaces.

Individual Modules:

Compulsory Modules:

• Engineering for AI (10 credits)
• Computing Foundations of Data Science (10 credits)
• Data Visualization (10 credits)
• Data Management and Exploratory Data Analysis (10 credits)
• Data Science in the Wild (Group Project) (10 credits)
• Machine Learning with Project (10 credits)
• Time Series Data (10 credits)
• Bayesian Methodology (10 credits)
• Statistical Learning for Data Science (10 credits)
• Project and Dissertation in Data Science (80 credits)

Optional Modules:

• Image Informatics (10 credits)
• Complex Data Visualization (10 credits)
• Deep Learning (10 credits)
• Statistical Foundations of Data Science (10 credits)

Assessment:

Assessment Methods:

• Computer assessment
• Dissertation
• Professional skills assessments
• Oral examination
• Oral presentation
• Practical lab report
• Poster
• Problem-solving exercises
• Report

Assessment Criteria: Students are assessed through a combination of individual and group projects, written examinations, and coursework assignments. The assessment criteria vary depending on the module and may include factors such as originality, critical analysis, problem-solving skills, communication skills, and technical proficiency.

Teaching:

Teaching Methods: The program is delivered by the School of Computing and the School of Mathematics, Statistics and Physics. Teaching methods include lectures, seminars, workshops, group projects, and individual supervision. The focus is on active learning, encouraging students to participate in discussions, problem-solving exercises, and hands-on projects.

Faculty: The program is taught by a team of internationally recognized experts in data science, statistics, and computer science. Faculty members have extensive research experience and strong industry connections. They are committed to providing students with a stimulating and supportive learning environment.

Unique Approaches:

• Strong emphasis on real-world applications and problem-solving skills.
• Collaboration with industry partners on projects and research.
• Flexible learning options for part-time students.
• Supervision and mentorship from experienced academics and industry professionals.
• Use of cutting-edge technology and software tools.

Careers:

Career Paths:

• Data Scientist
• Machine Learning Engineer
• Data Analyst
• Statistician
• Business Intelligence Analyst
• Research Scientist
• Consultant
• Financial Analyst
• Marketing Analyst
• Quantitative Analyst

Career Opportunities: Graduates have a wide range of career opportunities in various sectors, including:

• Technology
• Finance
• Healthcare
• Retail
• Government
• Education
• Research and Development

Outcomes:

• The program equips graduates with the skills and knowledge required to pursue successful careers in data science and related fields.
• Graduates are well-prepared to tackle complex data challenges and generate valuable insights for organizations.
• This program is suitable for students who have a strong background in mathematics, statistics, or computer science.
• Applicants are expected to have a 1st class BSc honours degree or international equivalent.
• The program is offered full-time and part-time.
• Scholarships and funding are available for eligible students.
• The University's Urban Sciences Building and the Newcastle Helix location provide state-of-the-art facilities and a vibrant research environment.
• The program offers a unique opportunity to learn from world-leading experts in data science and gain real-world experience through industry partnerships and projects.