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CS910 Foundations of Data Analytics

CS910-15 Foundations of Data Analytics

Academic year
Computer Science
Taught Postgraduate Level
Module leader
Florin Ciucu
Credit value
Module duration
10 weeks
Study location
University of Warwick main campus, Coventry
Introductory description

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.

Module aims

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

Outline syllabus

This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.

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 topic, 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. Suggestions for the course project.
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; advanced programming tools such as Perl and Python for powerful data manipulation.
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.
Database: Data quality, data cleaning, Relational data, SQL, NoSQL - Problems found in realistic data: errors, missing values, lack of consistency, and techniques for addressing them. The relational data model, and the SQL language for expressing queries. The NoSQL movement, and the systems evolving around it.
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 algerbraic 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.
Data Structures: Bloom Filters, Sketches, Summaries - Data structures to scale analytics to big data and data streams. The Bloom filter to represent large set values. Sketch data structures for more complex data analysis, and other summary data structures.
Data Sharing: Privacy, Anonymization, Risks - The ethics and risks of sharing data on individuals. Technologies for anonymizing data: k-anonymity, and differential privacy.
Graphs: Social Network Analysis, metrics, relational learning - Graph representations of data, with applications to social network data. Measurements of centrality and importance. Recommendations in social networks, and inference via relational learning.

Learning outcomes

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

  • 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.
  • Identify issues with scaling analytics to large data sets, and use appropriate techniques (NoSQL systems, data structures) to scale up the computation.
  • Appreciate the need for privacy, identify privacy risks in releasing information, and design techniques to mediate these risks.
  • Understand the principles and purposes of data analytics, and articulate the different dimensions of the area.
Indicative reading list

Recommended Text:
Data Mining: Concepts and Techniques. Jiawei Han, Michelle Kanber, Jian Pei. Morgan Kaufman, 2011
Additional Reading:
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

Subject specific skills

Working with data;
abstracting and modelling;

Transferable skills

Communication skills;
Problem solving.

Study time

Type Required Optional
Lectures 30 sessions of 1 hour (20%) 3 sessions of 1 hour
Practical classes 5 sessions of 1 hour (3%)
Private study 115 hours (77%)
Total 150 hours
Private study description

Private study and revision


No further costs have been identified for this module.

You do not need to pass all assessment components to pass the module.

Students can register for this module without taking any assessment.

Assessment group C5
Weighting Study time
Project 35%

Project. This assignment is worth more than 3 CATS and is not, therefore, eligible for self-certification.

Problem set 3 5%

Exercise sheet 3

Problem set 4 5%

Exercise sheet 4

Problem set 5 5%

Exercise sheet 5

In-person Examination 50%

CS910 examination

  • Answerbook Pink (12 page)
  • Students may use a calculator
Assessment group R2
Weighting Study time
In-person Examination - Resit 100%

CS910 resit exam

  • Answerbook Pink (12 page)
  • Students may use a calculator
Feedback on assessment

Written feedback and mark breakdown for assignments.

Past exam papers for CS910


This module is Core for:

  • Year 1 of TCSA-G5PA Postgraduate Taught Data Analytics
  • Year 1 of TCSA-G5PB Postgraduate Taught Data Analytics (CUSP)

This module is Optional for:

  • Year 2 of TIMS-L990 Postgraduate Big Data and Digital Futures
  • Year 1 of TESA-H641 Postgraduate Taught Communications and Information Engineering
  • TCSA-G5PD Postgraduate Taught Computer Science
    • Year 1 of G5PD Computer Science
    • Year 1 of G5PD Computer Science
  • TIMA-L995 Postgraduate Taught Data Visualisation
    • Year 1 of L995 Data Visualisation
    • Year 2 of L995 Data Visualisation
  • Year 1 of TPXA-F344 Postgraduate Taught Modelling of Heterogeneous Systems
  • Year 2 of TPXA-F345 Postgraduate Taught Modelling of Heterogeneous Systems (PGDip)
  • Year 1 of TIMA-L99D Postgraduate Taught Urban Analytics and Visualisation

This module is Core option list A for:

  • Year 1 of TPSS-C803 Postgraduate Taught Behavioural and Data Science

This module is Core option list C for:

  • Year 1 of TPSS-C803 Postgraduate Taught Behavioural and Data Science

This module is Option list A for:

  • Year 1 of TIMS-L990 Postgraduate Big Data and Digital Futures

This module is Option list B for:

  • Year 1 of TPXA-F345 Postgraduate Taught Modelling of Heterogeneous Systems (PGDip)

Further Information

Term 1


Online Material