D'Math University | Statistics & Data Science  ·  Programme #14

BSc Data Science

One of the fastest-growing undergraduate programmes in the world — combining programming, statistics, machine learning, and data engineering into a comprehensive three-year degree. Graduates are in extraordinary demand across every sector of the modern economy.

Undergraduate 3 Years Online Tech-Integrated
32
Modules Available
£45k
Average Graduate Salary
55+
Industry Partners
10,000+
Real Datasets Accessible

Programme Overview

The BSc Data Science is built on four foundational pillars: mathematics and statistics, programming and engineering, machine learning, and data ethics. Year 1 introduces Python, probability, and databases. Year 2 progresses to machine learning, big data frameworks, and visualisation. Year 3 features advanced ML, NLP electives, and a substantial industry-partnered capstone project with a real client and live dataset.

Students gain access to our curated repository of over 10,000 labelled datasets spanning healthcare, finance, climate, and social science — used in every module for hands-on learning.

What You'll Learn

  • Python for Data Science: NumPy, pandas, scikit-learn, data pipelines
  • Machine Learning: Supervised, unsupervised, and reinforcement learning
  • Database Systems: SQL, NoSQL, relational design, query optimisation
  • Big Data Technologies: Hadoop, Spark, cloud platforms (AWS/GCP)
  • Data Visualisation: matplotlib, Tableau, D3.js, storytelling with data
  • Bayesian Methods: Probabilistic modelling, uncertainty quantification
  • Ethics in Data Science: Fairness, bias, privacy, GDPR compliance
  • Linear Algebra: Vector spaces, matrix decomposition, SVD for ML
Core Curriculum
💻

Python for Data Science

End-to-end Python skills — from scripting and data cleaning with pandas to building and evaluating ML pipelines.

📊

Statistics & Probability

Distributions, estimation, hypothesis testing, and regression — the statistical backbone of every data science workflow.

🤖

Machine Learning

Linear models, decision trees, SVMs, neural networks, and ensemble methods with cross-validation and hyperparameter tuning.

🗃️

Database Systems & SQL

Relational schema design, advanced SQL queries, indexing strategies, and introduction to NoSQL (MongoDB, Cassandra).

🔢

Linear Algebra for Data Science

Matrices, eigenvalues, SVD, PCA, and tensor operations — essential mathematical tools for ML and neural networks.

🌐

Big Data Technologies

Distributed processing with Apache Spark, Hadoop ecosystem, cloud storage, and real-time streaming with Kafka.

📈

Data Visualisation

Principles of visual communication, interactive dashboards with Tableau and Power BI, and custom D3.js charts.

🌍

Ethics in Data Science

Algorithmic fairness, bias auditing, data privacy legislation (GDPR/CCPA), and responsible AI deployment frameworks.

Course Catalogue

Click any course to view its objective and learning outcomes.

DSC 101 Mathematical Foundations for Data Science +

Objective

To establish the linear algebra, calculus and probability used throughout data science.

Learning Outcomes

  • Compute matrix operations relevant to data representations.
  • Apply derivatives and gradients to optimisation.
  • Use probability rules and standard distributions.
  • Manipulate large vectors and matrices efficiently.
  • Translate ML notation into vector-matrix form.
DSC 102 Programming Foundations (Python) +

Objective

To gain fluency in Python for data manipulation, analysis and machine learning.

Learning Outcomes

  • Write idiomatic Python with collections and comprehensions.
  • Use NumPy and pandas for vectorised data work.
  • Apply control flow and functions for clean code.
  • Manage projects with packaging and virtual environments.
  • Debug and profile Python programs.
DSC 103 Linear Algebra for Data Science +

Objective

To develop the algebraic toolbox needed for ML and modern statistics.

Learning Outcomes

  • Compute eigendecomposition and SVD.
  • Apply PCA for dimensionality reduction.
  • Use matrix factorisation for recommender systems.
  • Solve least-squares problems analytically and numerically.
  • Interpret latent-space embeddings.
Interactive Activity — 2×2 Matrix Transformation
Set the entries of a 2×2 matrix. Watch how it transforms the unit square. Determinant = signed area of the transformed square.
a = 1.0 b = 0.5 c = -0.3 d = 1.0
DSC 104 Probability & Statistics for Data Science +

Objective

To master the inferential and probabilistic reasoning behind ML models.

Learning Outcomes

  • Compute Bayes' rule for posterior updates.
  • Apply maximum likelihood and MAP estimation.
  • Use bootstrap and resampling for inference.
  • Quantify model uncertainty with credible intervals.
  • Compare frequentist and Bayesian perspectives.
Interactive Activity — Distribution Plotter
Pick a distribution and adjust its parameters. Read off mean and variance directly from the plot.
Distribution: p1 = 0.0 p2 = 1.0
Interactive Activity — Central Limit Theorem Simulator
Sample n values, take their average, repeat. The histogram of averages converges to a normal distribution — CLT in action.
Source: Sample size n = 10
Total sample means: 0
DSC 201 Database Systems +

Objective

To design, query and manage data stores for analytics and ML pipelines.

Learning Outcomes

  • Model entities into normalised relational schemas.
  • Write SQL including joins and window functions.
  • Use NoSQL and graph stores where appropriate.
  • Optimise queries with indexing and partitioning.
  • Build ETL/ELT pipelines for ML training data.
Interactive Activity — Distribution Plotter
Pick a distribution and adjust its parameters. Read off mean and variance directly from the plot.
Distribution: p1 = 0.0 p2 = 1.0
DSC 202 Data Wrangling & Visualisation +

Objective

To clean, transform and visualise messy real-world datasets effectively.

Learning Outcomes

  • Identify and treat missing data and outliers.
  • Reshape data using long/wide pivots.
  • Visualise distributions, relationships and trends.
  • Build interactive dashboards.
  • Document data lineage and quality issues.
Interactive Activity — Distribution Plotter
Pick a distribution and adjust its parameters. Read off mean and variance directly from the plot.
Distribution: p1 = 0.0 p2 = 1.0
DSC 203 Machine Learning I — Supervised Learning +

Objective

To master the core supervised learning algorithms and evaluation methodology.

Learning Outcomes

  • Apply linear and logistic regression with regularisation.
  • Train decision trees, ensembles and SVMs.
  • Tune hyperparameters with cross-validation.
  • Evaluate using appropriate metrics and curves.
  • Diagnose bias-variance issues.
Interactive Activity — 2×2 Matrix Transformation
Set the entries of a 2×2 matrix. Watch how it transforms the unit square. Determinant = signed area of the transformed square.
a = 1.0 b = 0.5 c = -0.3 d = 1.0
Interactive Activity — Vector Field & Gradient Visualizer
Pick a scalar field f(x,y). Gradient arrows point in the direction of steepest ascent. Click anywhere to drop a particle that follows the gradient.
f(x,y) =
Click on the plot to drop a particle.
Interactive Activity — Gradient Descent on a 2D Loss Surface
Click anywhere on the surface to drop a starting point. Animation traces the descent path on the chosen loss function. Adjust the learning rate to see how step size affects convergence.
Loss: η = 0.10
Click on the loss surface to drop a starting point.
Interactive Activity — Linear Classifier Decision Boundary
Click to add red or blue points. Adjust weights w₁, w₂, b to position the decision line w₁x + w₂y + b = 0. Misclassified points highlight red.
w₁ = w₂ = b =
Interactive Activity — K-Means Clustering
Click on the canvas to add data points. Press "Step" to alternate between cluster assignment and centroid update. Watch the clusters converge.
k = 3
Click to add at least k points, then press Step.
DSC 204 Machine Learning II — Deep Learning +

Objective

To build, train and evaluate neural networks for vision, text and tabular tasks.

Learning Outcomes

  • Implement neural networks with PyTorch or TensorFlow.
  • Apply CNNs to image data and RNNs/Transformers to text.
  • Use transfer learning effectively.
  • Combat overfitting with dropout, augmentation and early stopping.
  • Profile and optimise training on GPUs.
Interactive Activity — 2×2 Matrix Transformation
Set the entries of a 2×2 matrix. Watch how it transforms the unit square. Determinant = signed area of the transformed square.
a = 1.0 b = 0.5 c = -0.3 d = 1.0
Interactive Activity — Vector Field & Gradient Visualizer
Pick a scalar field f(x,y). Gradient arrows point in the direction of steepest ascent. Click anywhere to drop a particle that follows the gradient.
f(x,y) =
Click on the plot to drop a particle.
Interactive Activity — Gradient Descent on a 2D Loss Surface
Click anywhere on the surface to drop a starting point. Animation traces the descent path on the chosen loss function. Adjust the learning rate to see how step size affects convergence.
Loss: η = 0.10
Click on the loss surface to drop a starting point.
Interactive Activity — Linear Classifier Decision Boundary
Click to add red or blue points. Adjust weights w₁, w₂, b to position the decision line w₁x + w₂y + b = 0. Misclassified points highlight red.
w₁ = w₂ = b =
DSC 301 Big Data Engineering +

Objective

To process, store and serve data at scale using modern cloud infrastructure.

Learning Outcomes

  • Use Spark for distributed data processing.
  • Build streaming pipelines with Kafka or Flink.
  • Deploy lakehouse architectures.
  • Apply infrastructure-as-code for reproducible environments.
  • Compare cloud platforms for cost and performance.
Interactive Activity — Distribution Plotter
Pick a distribution and adjust its parameters. Read off mean and variance directly from the plot.
Distribution: p1 = 0.0 p2 = 1.0
DSC 302 NLP & Text Analytics +

Objective

To analyse and model text data with classical and neural approaches.

Learning Outcomes

  • Apply text preprocessing and tokenisation.
  • Compute TF-IDF and word embeddings.
  • Train classifiers for sentiment, topic and intent.
  • Use transformer models for sequence tasks.
  • Evaluate NLP systems with task-appropriate metrics.
DSC 303 Data Ethics, Privacy & Fairness +

Objective

To address ethical, regulatory and fairness considerations in data science work.

Learning Outcomes

  • Audit ML systems for bias and disparate impact.
  • Apply differential privacy and federated learning.
  • Discuss interpretability and explainability methods.
  • Comply with GDPR and similar regulations.
  • Build data-ethics review processes.
Interactive Activity — Truth Table Builder
Type a logical expression using p, q, r and operators (AND, OR, NOT). The truth table generates instantly.
Operators: AND OR NOT XOR -> <->
DSC 304 Data Science Capstone +

Objective

To deliver an end-to-end data-science project from data acquisition to deployment.

Learning Outcomes

  • Frame a real-world problem as a data-science task.
  • Build, validate and deploy an analytic model.
  • Manage project timelines and stakeholder expectations.
  • Document code, data and decisions reproducibly.
  • Present results to a technical and lay audience.
Career Pathways
🤖

Data Scientist

Build predictive models and extract intelligence from large-scale datasets at technology companies, banks, and consultancies.

⚙️

Machine Learning Engineer

Develop and deploy ML models at scale, integrating statistical models into production software systems.

📊

Business Intelligence Analyst

Create dashboards and analytical reports that drive strategic decisions across sales, marketing, and operations.

🏗️

Data Engineer

Design and maintain scalable data pipelines, warehouses, and ETL systems that power analytics organisations.

📈

Product Analyst

Use data to guide product strategy, A/B test features, and understand user behaviour at tech and SaaS companies.

🔬

AI Researcher

Pursue research in applied machine learning, contributing to academic publications or corporate research labs.

Top Universities Offering This Programme
MIT Carnegie Mellon University Stanford University University College London University of Edinburgh TU Delft University of Toronto National University of Singapore University of Melbourne Chinese University of Hong Kong

Why D'Math University

01

Industry-First Curriculum

Modules co-designed with data teams at leading technology firms, ensuring skills are current and immediately deployable.

02

10,000+ Dataset Library

Unparalleled access to labelled real-world datasets from healthcare, climate science, finance, and social media.

03

Cloud Lab Environment

Every student gets a cloud computing environment (AWS-based) from Day 1, with credits for Spark and GPU-accelerated workloads.

04

Industry Capstone

Year 3 capstone projects are completed with a real client organisation, with many leading to direct job offers upon graduation.

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