Skip to main content Skip to navigation

Data Science: Foundations of Data Analytics

The most important aspect of computer science is problem solving, an essential skill for life.

Data Science is concerned with how to gain knowledge from the vast volumes of data generated daily in modern life, from social networks to scientific research and finance, and proposes sophisticated computing techniques for processing this deluge of information. This course addresses fundamental aspects of Data Science, e.g. analytical models to represent and understand the data, efficient algorithms to manipulate and extract relevant knowledge.

In particular, students study the design, development and analysis of software and hardware used to solve problems in a variety of business, scientific and social contexts. During this course, students will study techniques for how to go from raw data to a deeper understanding of the patterns and structures within the data, to support making predictions and decision making. Students would be expected to have some basic knowledge of linear algebra and calculus.

Attending the classes and seminars, meeting new friends and taking part in all the activities gave me an experience far beyond what I expected. The course content was so rich and gave me a good base to further my academic endeavor in the area of data science.

Frank Barigye (Uganda)

Key Facts

Level: Introductory to intermediate

Fees: Please see fees page

Teaching: 60 hours

Expected independent study: 90 hours

Optional assessment: Dependant on course

Typical credit: 3-4 credits (US) 7.5 ECTS points (EU)*
* Please check with your home institution

For more information on exams and credit, please see our Teaching and assessment page



Teaching Assistant

Course Aims

To understand the foundational skills in data analytics, including preparing and working with data; abstracting and modelling an analytic question; and using tools from statistics, learning and mining to address the question.


Data Analytics involves being about to go from raw data to a deeper understanding of the patterns and structures within the data, to support making predictions and decision making. The course will cover a number of topics, including:

  • Introduction to analytics, case studies - How analytics is used in practice. Examples of successful analytics work from companies such as Google, Facebook, Kaggle, and Netflix.
  • Basic tools: command line tools, plotting tools, programming tools - The wide variety of tools available to work with data, including unix/linux command line tools for data manipulation (sorting, counting, reformatting, aggregating, joining); tools such as gnuplot for displaying and visualizing data.
  • Statistics: Probability recap, distributions, significance tests, R - The tools from statistics for understanding distributions and probability (means, variance, tail bounds). Hypothesis testing for determining the significance of an observation, and the R system for working with statistical data.
  • Regression: linear regression, least squares, logistic regression - Predicting new data values via regression models. Simple linear regression over low dimensional data, regression for higher dimensional data via least squares optimization, logistic regression for categoric data.
  • Matrices: Linear Algebra, SVD, PCA - Matrices to represent relations between data, and necessary linear algebraic operations on matrices. Approximately representing matrices by decompositions (Singular Value Decomposition and Principal Components Analysis). Application to the Netflix prize.
  • Clustering: hierarchical, k-means, k-center - Finding clusters in data via different approaches. Choosing distance metrics. Different clustering approaches: hierarchical agglomerative clustering, k-means (Lloyd's algorithm), k-center approximations. Relative merits of each method.
  • Classification: Trees, NB, Support Vector Machines, Kernel Trick - Building models to classify new data instances. Decision tree approaches and Naive Bayes classifiers. The Support Vector Machines model and use of Kernels to produce separable data and non-linear classification boundaries. The Weka toolkit.
  • Graphs: Social Network Analysis, metrics, relational learning - Graph representations of data, with applications to social network data. Measurements of centrality and importance. Classification and prediction.
  • Recommendations in social networks; neighbor and latent-based methods.
  • Time-Series Analysis; dynamic time warping, dimensionality reduction, autoregressive moving averages

Learning Outcomes

By the end of the module, the student should be able to:

  • Understand the basic mathematical models for large data sets.
  • Understand the principles and purposes of data analytics, and articulate the different dimensions of the area.
  • Work with and manipulate a data set to extract statistics and features, coping with missing and dirty data.
  • Apply basic data mining machine learning techniques to build a classifier or regression model, and predict values for new examples.

Course Structure

For this course, there will be 4 hours of teaching on most weekdays, comprised of lectures and small group teaching. The structure will be:

  • 3 hours of lectures.
  • A 1 hour seminar in small groups.

Students will also be given time each day for independent study. Towards the end of the third week, students will be provided with time for revision.

Course Assessment

The module will be assessed via a 2-hour examination. It should be noted that the exam is not compulsory. Everyone who completes the course – whether or not they sit the exam - will receive a certificate of attendance. However, by taking the exam you will also receive a grade/mark for the course which can be helpful to you.

Course Reading List

The main text for this course is:

  • Data Mining: Concepts and Techniques. Jiawei Han, Michelle Kanber, Jian Pei. Morgan Kaufman, 2011
  • Data Manipulation with R. Phil Spector. Springer, 2008
  • Machine Learning. Thom Mitchell. McGraw Hill, 1997
  • Database Systems: An Application-oriented Approach, Introductory Version. Michael Kifer, Arthur Bernstein, Philip Lewis. Addison Wesley, 2004
  • The Works: Anatomy of a City. Kate Ascher. Penguin, 2012

Entry Requirements

This course is open to students studying any discipline at University level provided they have basic knowledge of linear algebra and calculus. We welcome individuals from all backgrounds, including students who are currently studying another subject but who want to broaden their knowledge in another discipline. Students should also meet our standard entry requirements and must be aged 18 or over by the time the Summer School commences and have a good understanding of the English language.

Please note changes to the syllabus and teaching team may be made over the coming months before exact set of topics are finalised.