| Program start date | Application deadline |
| 2023-09-19 | 2023-08-01 |
| 2024-01-09 | 2023-12-01 |
| 2024-09-01 | - |
Program Overview
The Data Analytics MSc is a one-year, full-time program designed to provide students with the skills and knowledge needed to succeed in the field of data analytics. The program emphasizes the application of statistical techniques and the latest technologies to real-world data analysis problems. Graduates will be able to build and deploy predictive models, interpret data, and communicate insights to a variety of stakeholders. The program is accredited by the Chartered Institute of Marketing (CIM) and is offered in partnership with the Alan Turing Institute, a leading research institute in data science.
Program Outline
Degree Overview
Data Analytics MSc
This program is a one-year, full-time MSc designed to provide students with the skills and knowledge needed to succeed in the field of data analytics. The program emphasizes the application of statistical techniques and the latest technologies to real-world data analysis problems. Graduates will be able to build and deploy predictive models, interpret data, and communicate insights to a variety of stakeholders.
Objectives:
- Develop a strong foundation in the theory and practice of data analytics.
- Provide students with the skills and knowledge to extract, clean, manage, and analyze large datasets.
- Train students to build and deploy predictive models using a variety of techniques.
- Equip students with the ability to interpret and communicate data insights to a variety of audiences.
- Prepare students for careers in data analytics, machine learning, and related fields.
Description:
The Data Analytics MSc program is delivered through a combination of lectures, seminars, workshops, and project work. Students will gain hands-on experience with a variety of data analysis tools and techniques, including Python, R, SQL, and machine learning algorithms. The program also includes a significant project component, which allows students to apply their skills to a real-world problem.
Outline:
Structure:
The program is divided into two semesters, each with a core set of modules and a number of elective modules. Students are required to take the following core modules:
- Module B: Machine Learning with Python
- Module C: Data Mining and Predictive Modeling
- Module D: Big Data Analytics
- Module E: Data Visualization
- Module F: Business Intelligence and Data-Driven Decision Making In addition to the core modules, students can choose from a range of elective modules, including:
- Module G: Natural Language Processing
- Module H: Deep Learning
- Module J: Network Analysis
- Module K: Social Media Analytics
- Module L: Financial Data Analytics This module provides students with a strong foundation in the theory and practice of probability and statistics. Topics covered include:
- Probability theory
- Statistical inference
- Hypothesis testing
- Regression analysis
- Time series analysis
Module B: Machine Learning with Python
This module introduces students to the principles of machine learning and how to implement them using Python. Topics covered include:
- Supervised learning
- Unsupervised learning
- Reinforcement learning
- Model selection Topics covered include:
- Data exploration
- Feature engineering
- Model building
- Model evaluation
- Model deployment
Module D: Big Data Analytics
This module introduces students to the challenges and opportunities of working with big data. Topics covered include:
- Distributed computing
- Cloud computing
- NoSQL databases Topics covered include:
- Data visualization principles
- Data visualization tools
- Interactive data visualization
Module F: Business Intelligence and Data-Driven Decision Making
This module explores the role of data analytics in business decision making. Topics covered include:
- Business intelligence concepts
- Data warehousing
- Data analytics for business problems
- Data-driven decision making
Module G: Natural Language Processing
This module introduces students to the principles of natural language processing (NLP). Topics covered include:
- Sentiment analysis
- Machine translation
- Natural language generation
Module H: Deep Learning
This module introduces students to the principles of deep learning. Topics covered include:
- Neural networks
- Convolutional neural networks
- Recurrent neural networks
- Deep learning applications Topics covered include:
- Linear programming
- Integer programming
- Network optimization
- Optimization algorithms
Module J: Network Analysis
This module introduces students to the principles of network analysis. Topics covered include:
- Graph theory
- Social network analysis
- Network visualization
- Network modeling
Module K: Social Media Analytics
This module introduces students to the principles of social media analytics. Topics covered include:
- Social media data collection
- Social media data analysis
- Sentiment analysis
- Social media marketing
Module L: Financial Data Analytics
This module introduces students to the principles of financial data analytics. Topics covered include:
- Financial time series analysis
- Financial risk management
- Financial fraud detection
- Portfolio optimization
Module M: Data Ethics
This module introduces students to the ethical considerations of data analytics. Topics covered include:
- Data privacy
- Data security
- Algorithmic bias
- Data ethics in business
Assessment:
The program is assessed through a combination of coursework, examinations, and a project. The coursework includes a variety of assignments, such as essays, presentations, and computer programming assignments. The project is a substantial piece of work that allows students to apply their skills and knowledge to a real-world problem.
Teaching:
The program is taught by a team of experienced academics and practitioners. The academics have a strong research record in data analytics and are committed to providing students with a high-quality learning experience. The practitioners have extensive experience in the field of data analytics and bring their real-world expertise into the classroom. The program is delivered through a variety of teaching methods, including lectures, seminars, workshops, and project work.
- Lectures: Lectures are used to introduce students to new concepts and theories.
- Workshops: Workshops are used to provide students with hands-on experience with data analysis tools and techniques.
- Project work: The project work allows students to apply their skills and knowledge to a real-world problem. The program also uses a variety of online resources, such as discussion forums, online quizzes, and online tutorials.
Careers:
The Data Analytics MSc program prepares students for a variety of careers in data analytics, machine learning, and related fields. Some potential career paths include:
- Data Analyst
- Data Scientist
- Machine Learning Engineer
- Business Intelligence Analyst
- Market Research Analyst
- Financial Analyst
- Social Media Analyst Graduates of the program have been employed by a variety of organizations, including:
- Amazon
- Microsoft
- IBM
- McKinsey & Company
- Boston Consulting Group
- Deloitte
- PricewaterhouseCoopers
- KPMG
- Ernst & Young
Other:
- The program is accredited by the Chartered Institute of Marketing (CIM).
- The program is offered in partnership with the Alan Turing Institute, a leading research institute in data science.
- The program is taught in Mile End, London.
- The program is open to students from all backgrounds.
- The program is full-time and starts in September.
- The program fee is £12,650 for home students and £31,850 for overseas students.
- The program is open for applications from students with a demonstrable interest in the field of data analytics.
Data Analytics MSc: Your Pathway to a Successful Career
Home: £12,650 Overseas: £31,850 EU/EEA/Swiss studentsConditional depositHome: Not applicableOverseas: £2000Information about depositsPart-time studySeptember 2024 | 2 yearsHome: £6,350Overseas: £15,950EU/EEA/Swiss studentsThe course fee is charged per annum for 2 years.
