Course Module and Learning Outcomes
You are provided with highly structured and detailed course content, broken down into 11 distinct units covering core skills and knowledge.
Programming in Python, R and SQL
Data management
Measures of central tendency and variation
Data visualisation
MODULE 2: (6 ECTS CREDITS)
Statistical Inference
Statistical inference is the process of drawing inferences or conclusions from data using statistical techniques. This is at the core of data science, and a strong understanding of statistics from the beginning is the prime ingredient for a competent data scientist. In this module, you will cover sampling, statistical distribution, hypothesis testing, and variance analysis and use R code to carry out various statistical tests and draw inferences from their output.
Principles of statistical inference
Parametric tests
Non-parametric tests
Analysis of variance (ANOVA)
MODULE 3: (6 ECTS CREDITS)
Fundamentals of Predictive Modelling
Solutions to many business problems are related to successfully predicting future outcomes. This module introduces predictive modelling and provides a foundation for more advanced methods and machine learning. You’ll gain an understanding of the general approach to predictive modelling and then build simple and multiple linear regression models in Python and R and apply these in a range of contexts.
Predictive modelling principles
Linear regression models
Model validation
Python and R packages for predictive modelling
MODULE 4: (6 ECTS CREDITS)
Advanced Predictive Modelling
In this module, you are introduced to model development for categorical dependent variables. Binary dependent variables are encountered in many domains such as risk management, marketing, and clinical research, and this module covers detailed model building processes. Multinomial and ordinal logistic regression are also covered.
Logistic regression models
Survival analysis
Cox regression
Poisson regression
MODULE 5: (6 ECTS CREDITS)
Time Series Analysis
In this module, time series forecasting methods are introduced and explored. You will analyze and forecast macroeconomic variables such as GDP and inflation, as well as look at complex financial models using ARCH and GARCH, ARIMA, time series regression, exponential smoothing, and other models.
Time series concepts
Assessing stationarity
ARIMA, ARCH, GARCH modelling
Panel Data Regression
MODULE 6: (6 ECTS CREDITS)
Unsupervised Multivariate Methods
Data reduction is a key process in data science, and you will learn to apply data reduction methods such as principal component analysis, factor analysis, and multidimensional scaling. You will also learn to segment and analyze large data sets using clustering methods, another key analytical technique that brings out rich business insight if carried out skillfully.
Principal Component Analysis
Factor Analysis
Multidimensional Scaling
Cluster Analysis
MODULE 7: (6 ECTS CREDITS)
Machine Learning 1
Machine learning algorithms are new generation algorithms used in conjunction with classical predictive modelling methods. In this Machine Learning 1 module, you will understand applications of the support vector machine, K-nearest neighbours, and naive bayes algorithms for classification and regression problems using case studies from a range of industries and sectors.
Naive Bayes Method
Support Vector Machine Algorithm
K-nearest neighbours
MODULE 8: (6 ECTS CREDITS)
Machine Learning 2
The Machine Learning 2 module continues developing your machine learning knowledge, and you will cover decision tree, random forest, and neural network algorithms for regression and classification, again drawing on case studies from real-world data. You will have the opportunity to compare the performance of machine learning algorithms against classical statistical models and learn to assess which are most appropriate for specific scenarios.
Decision Tree
Random Forest
Association Rules
Neural Networks
MODULE 9: (6 ECTS CREDITS)
Text Mining and Natural Language Processing
This module looks at analyzing unstructured data such as that found in social media, newspaper articles, videos, and more. In particular, you will look at methods for text mining and natural language processing using R and Python code to produce graphical representations of unstructured data and carry out sentiment analysis.
Structured vs unstructured data
Text mining in R and Python
Sentiment analysis using R and Python
MODULE 10: (6 ECTS CREDITS)
Data Science in Practice
Presentation and communication skills
Synthesis of data science knowledge
Application to real world data and scenarios
MODULE 11: (30 ECTS CREDITS)
Applied Data Science Practicum
This Postgraduate Major Project completes the MSc Data Science and students choose a problem from a particular business or social domain. They have the option of working on a real-world problem from their own organisation and work with a mentor in conjunction with their course supervisor.
Students are required to solve a research problem that involves carrying out exploratory data analysis, hypothesis testing, research design and usie a range of classical and/or modern machine learning modelling methods to predict outcomes and provide actionable insights and recommendations. In doing so they will apply technical capabilities together with research skills and critical thinking. A key part of the project is to communicate the output of the student’s research to both technical and non-technical audiences through written, verbal and visual means.
Critical and creative thinking
Application of technical expertise
Scientific communication
Cohort-Based Learning
Join a learning community that grows together. Our cohort-based model ensures you collaborate, share experiences, and support one another throughout the program. Our dynamic, interactive approach ensures you're job-ready and confident, providing in-depth knowledge, hands-on experience, and unmatched support throughout your journey.
Flexible Schedules and Engaging Learning
Flexibility and comprehensive support
Structured schedules
interactive, hands-on learning experiences
Key Features
Flexible Schedules
Balance live, instructor-led sessions with self-paced content for an adaptable learning experience.
One-to-One Tutorials
Receive individualized guidance to address specific learning objectives or areas of interest.
Group Projects
Collaborate on authentic case studies, enhancing teamwork, problem-solving skills, and applied knowledge.
Take part in live Concept Classes to develop advanced skills and strengthen your understanding through practical coding and modeling exercises based on real-world scenarios.
Stay connected with peers and instructors through real-time updates, feedback, and collaborative tools.
Comprehensive Learning Platform
Access recorded live sessions, structured course materials, and supplementary resources at any time.
Internationally Accredited
Our Accreditation
The Data Science Institute is an accredited member of Woolf University, a recognized higher education institution in the European Union. All diploma and degree programs adhere to rigorous European Standards and Guidelines, ensuring international academic excellence and credibility.
ECTS – The Benchmark of Excellence
Our curricula are accredited through the European Credit Transfer and Accumulation System (ECTS), a recognized international standard and the world’s largest academic accreditation system. ECTS certification ensures widespread acceptance, facilitating both mobility and career development.
Why It Matters
Qualifications are internationally portable
Recognized by employers, institutions, and government agencies
Opens pathways for further academic progression and supports career development
Application and Admissions
The application process requires following the steps below, including completing an online form, receiving a conditional offer, registering with Woolf University, and finalizing admission through document verification and fee payment.
