Data Science - MSc

Program Overview

The MSc Data Science program from Lancaster University Leipzig is an intensive 12-month program that equips students with the knowledge and skills needed for a successful career in data science. The program offers a solid foundation in data science principles, including data mining, statistics, and programming, preparing graduates for roles as data analysts, data scientists, and more. The program also features an industry placement option as part of the dissertation project, providing students with valuable real-world experience. The program is taught in English and leads to a degree certificate awarded by Lancaster University, UK.

Program Outline

Degree Overview:

The MSc Data Science program at Lancaster University Leipzig is a 12-month Master's degree offered in Germany. The program aims to equip students with cutting-edge knowledge and skills for successful careers in data science within an international context. The objective is to develop future data analysts capable of analyzing and solving problems, making informed decisions considering strategic contexts, technology's role, and management within and between organizations. The program provides a strong foundation in data science, data mining, statistics, and related programming. It's designed for graduates seeking careers in data science and statistics, enabling them to work as consultants or employees across various industries. The program includes a 12-week industry placement option as part of the dissertation project, allowing students to apply their skills to real-world scenarios and gain valuable professional experience. The core modules are: Students learn to analyze performance trade-offs and complexities in designing practical solutions for data representation and processing.

• Data Science Fundamentals: Explores the data science role within organizations, research methodologies (hypothesis formulation, research strategies), data processing, preparation, integration, and communication of findings within organizations.
It also covers how data science problems are tackled in industrial settings. It covers data processing techniques, visualization, statistical data analysis, problem-solving, and graphical application development using R and Python.
• Statistical Foundations: Introduces statistical modeling for population inference from sample data.
It covers statistical terminology, comparing statistical and machine learning approaches, sampling uncertainty, statistical inference, model fitting, linear regression, and generalized linear models using R.
• Statistical Learning: Covers statistical learning, including big data, missing data, biased samples, likelihood, cross-validation, regression problems for large datasets (Lasso and Elastic Net), classification methods (logistic models, regression trees, random forests, bagging, boosting, neural networks), and unsupervised learning (K-means, PAM, CLARA, mixture models, latent class analysis).
Optional Modules: It covers social web applications, social network theory and analysis, user-generated content, crowd-sourced data, and network analysis.
• Building Big Data Systems: Explores the architecture, techniques, and technologies of Big Data systems, focusing on principles and applying them using state-of-the-art technology.
It includes case studies and industrial speaker presentations.
• Intelligent Data Analysis and Visualisation: Covers data mining, statistical/machine learning, and intelligent data analysis methods, encompassing the entire data analysis process from project objective formulation to statistical modeling and performance assessment.
It focuses on classification and uses R.
• Optimisation and Heuristics: Applies optimization techniques to business problems, introducing problem formulations and algorithmic methods for decision-making.
• MSc Data Science Dissertation: A self-study module involving a placement (June-July), culminating in a 20,000-30,000-word dissertation submitted in early September.
The topic varies depending on student interest and availability of research staff and industry partners.

Assessment:

Assessment methods include individual or group coursework, research projects, and examinations. Independent study is also expected to supplement taught material.

Teaching:

Teaching is delivered through a combination of small group lectures and group-based tutorials. All teaching is conducted in English.

Careers:

Graduates can pursue careers as data analysts, data scientists, data engineers, data managers, computer system analysts, or statisticians. The program prepares students for roles in various industries, not limited to IT giants, as virtually every company handles large datasets. Leipzig's Life Science, Biotechnology, and IT sectors offer specific opportunities.

Other:

The program offers January intakes via Pre-Master's entry only. The degree certificate is awarded by Lancaster University, UK. The program is a 1-academic year program. The program is located in Leipzig, Germany, and taught in English.

International: €24,500 EU/UK: €16,250