D'Math University | Statistics & Data Science

BSc Data Analytics

A modern, industry-aligned undergraduate programme that blends statistical foundations, computational methods, and real-world data skills. Graduates emerge fluent in statistical modelling, data visualisation, machine learning, and programming — ready for immediate impact in the data-driven economy.

Undergraduate 3 Years Online & Blended Industry-Ready
32
Total Modules
£43k
Average Graduate Salary
50+
Partner Universities
3
Year Programme

Programme Overview

Programme Overview

  • Combines statistics, computing, and business analytics in a cohesive curriculum
  • Year 1: Foundations in statistics, mathematics, and programming (Python/R)
  • Year 2: Database systems, machine learning, statistical modelling, and data ethics
  • Year 3: Big data technologies, advanced analytics, and a capstone data project
  • Hands-on learning with real datasets from industry partners
  • Strong focus on data storytelling, dashboards, and communicating insights
  • Excellent pathway to MSc in Data Science or Statistics

Entry Requirements

  • A-Levels: ABB including Mathematics
  • IB: 32 points with 5 in Higher Level Mathematics
  • SAT/ACT: strong quantitative scores for US applicants
  • CBSE/ISC: 85%+ in Mathematics at Class XII
  • No prior programming experience required — introduced in Year 1
  • English proficiency: IELTS 6.0+ or equivalent
  • Mature applicants with industry data experience considered individually

Core Curriculum

📊
Statistical Foundations
Descriptive statistics, probability distributions, confidence intervals, hypothesis testing, and regression fundamentals.
🐍
Python for Data Science
Python programming, NumPy, Pandas, Matplotlib, and Seaborn for data manipulation, analysis, and visualisation.
🗄️
Databases & SQL
Relational database design, SQL querying, data warehousing, and working with structured and semi-structured data.
🤖
Machine Learning
Supervised and unsupervised learning, decision trees, ensemble methods, neural networks, and model evaluation.
📈
Statistical Modelling
Linear and generalised linear models, time series analysis, ANOVA, and model selection techniques.
🎨
Data Visualisation
Principles of visual communication, interactive dashboards with Tableau and Power BI, and storytelling with data.
☁️
Big Data Technologies
Hadoop, Spark, cloud platforms (AWS/GCP), and processing large-scale datasets in distributed environments.
🎓
Capstone Analytics Project
A substantial independent project applying the full analytics pipeline to a real-world industry problem or dataset.

Course Catalogue

Click any course to view its objective and learning outcomes.

DAN 101 Foundations of Data Analytics +

Objective

To introduce the data analytics workflow from question formulation to communication.

Learning Outcomes

  • Frame business questions as data-answerable problems.
  • Distinguish descriptive, predictive and prescriptive analytics.
  • Outline the typical data-analytics lifecycle.
  • Apply ethical principles to data collection and use.
  • Communicate findings using effective storytelling.
DAN 102 Statistical Methods for Analytics +

Objective

To apply core statistical reasoning to analytic problems.

Learning Outcomes

  • Summarise data using location, spread and shape statistics.
  • Construct confidence intervals and conduct hypothesis tests.
  • Identify and address common biases in observational data.
  • Apply non-parametric tests where appropriate.
  • Critically read statistical claims in business reports.
DAN 103 Probability & Distributions +

Objective

To master the probability theory underlying analytic models.

Learning Outcomes

  • Apply probability rules to compound and conditional events.
  • Identify common discrete and continuous distributions.
  • Compute expected values and variances of derived quantities.
  • Use the central limit theorem in inference.
  • Simulate random samples to estimate probabilities.
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
DAN 104 Database Systems & SQL +

Objective

To design and query relational databases for analytics workloads.

Learning Outcomes

  • Model real-world entities into normalised schemas.
  • Write complex SQL including joins, subqueries and window functions.
  • Optimise queries using indexing and execution plans.
  • Use ETL pipelines to ingest data into warehouses.
  • Discuss the trade-offs of OLTP vs OLAP designs.
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
DAN 201 Data Visualisation +

Objective

To design visualisations that reveal structure and support decision-making.

Learning Outcomes

  • Apply principles of visual perception to chart design.
  • Choose appropriate chart types for different data and tasks.
  • Build interactive dashboards with industry tools.
  • Avoid common visualisation pitfalls and misleading graphics.
  • Tailor visualisations to specific audiences.
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
DAN 202 Programming for Analytics (Python/R) +

Objective

To program reproducible analytic workflows in Python and R.

Learning Outcomes

  • Manipulate tabular data with pandas, dplyr and tidyverse.
  • Write reusable functions and modules for analytic pipelines.
  • Manage projects with virtual environments and version control.
  • Apply unit tests and code reviews to analytic code.
  • Use Jupyter and R Markdown for reproducible reports.
DAN 203 Predictive Modelling +

Objective

To build supervised learning models for prediction and classification tasks.

Learning Outcomes

  • Apply linear and logistic regression to business data.
  • Use decision trees, random forests and gradient boosting.
  • Tune models with cross-validation and grid search.
  • Evaluate models using appropriate accuracy metrics.
  • Diagnose and address overfitting and data leakage.
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.
DAN 204 Business Intelligence Tools +

Objective

To deliver insight using industry BI platforms and self-service reporting.

Learning Outcomes

  • Connect BI tools to multiple data sources.
  • Build dashboards in Tableau or Power BI.
  • Apply DAX and similar formula languages.
  • Schedule data refreshes and manage permissions.
  • Discuss governance and single-source-of-truth strategies.
DAN 301 Time Series Analysis +

Objective

To analyse and forecast data observed over time.

Learning Outcomes

  • Decompose series into trend, season and residual.
  • Fit ARIMA and exponential-smoothing models.
  • Evaluate forecast accuracy with hold-out samples.
  • Apply state-space models for irregular series.
  • Quantify forecast uncertainty using prediction intervals.
Interactive Activity — Sequence Convergence
Pick a sequence and an ε. The graph shows when a_n enters the ε-band around limit L. The smallest such N is the "epsilon-N" for convergence.
a_n = ε = 0.10
Interactive Activity — Epsilon-Delta for Continuity
For f(x) = x², set the point a and tolerance ε. The activity finds the largest δ such that |x − a| < δ ⟹ |f(x) − f(a)| < ε.
a = 1.0 ε = 0.50
DAN 302 A/B Testing & Experimentation +

Objective

To design and analyse online experiments rigorously.

Learning Outcomes

  • Calculate sample sizes for desired power.
  • Avoid common pitfalls including p-hacking and peeking.
  • Apply sequential testing methods.
  • Account for novelty and network effects.
  • Translate test results into business decisions.
DAN 303 Big Data Technologies +

Objective

To work with data at scale using distributed computing frameworks.

Learning Outcomes

  • Use HDFS, Spark and cloud data warehouses.
  • Write efficient Spark transformations.
  • Compare batch and streaming architectures.
  • Apply NoSQL stores for non-relational data.
  • Discuss cost-performance trade-offs in cloud platforms.
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
DAN 304 Data Ethics & Governance +

Objective

To navigate the ethical, legal and societal implications of data work.

Learning Outcomes

  • Apply GDPR and major data-protection frameworks.
  • Identify and mitigate algorithmic bias.
  • Design data-governance policies and audit trails.
  • Discuss privacy-enhancing technologies.
  • Communicate ethical risks to stakeholders.
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 -> <->

Career Pathways

📈
Data Analyst
Transform raw business data into actionable insights using SQL, Python, and BI tools across every industry sector.
💼
Business Intelligence Analyst
Build dashboards, KPI frameworks, and reporting systems that drive strategic decisions in corporate environments.
🤖
Junior Data Scientist
Apply machine learning and statistical models to predict outcomes, classify data, and automate analytical tasks.
🏥
Healthcare Data Analyst
Analyse clinical, operational, and genomic data to improve patient outcomes and healthcare resource management.
💰
Financial Data Analyst
Support investment decisions, risk modelling, and regulatory reporting in banking and asset management firms.
🔬
Research Data Analyst
Support academic and industry research projects with data management, statistical analysis, and evidence synthesis.

Top Global Universities

University of Edinburgh Imperial College London University of Manchester Carnegie Mellon University University of Michigan University of Toronto NUS Singapore University of Melbourne ETH Zürich University College London

Why D'Math University

STEP 01
Industry-Aligned Curriculum
Programme designed with input from data science leaders — every module maps directly to skills in demand across tech, finance, and healthcare.
STEP 02
Real-World Projects
Work with live datasets from industry partners throughout the degree, building a portfolio of practical analytical work before graduation.
STEP 03
Mathematical Depth
Unlike vocational bootcamps, our programme ensures genuine statistical understanding — so graduates can adapt as tools and platforms evolve.
STEP 04
Career Support
Dedicated placement support, CV workshops, mock interviews, and connections to a global network of data-employing organisations.
Enrol in BSc Data Analytics →

Applications open year-round — join the next cohort today.