CS91015 Foundations of Data Analytics
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, kmeans, kcenter  Finding clusters in data via different approaches. Choosing distance metrics. Different clustering approaches: hierarchical agglomerative clustering, kmeans (Lloyd's algorithm), kcenter 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 nonlinear 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: kanonymity, 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 Applicationoriented 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 

Lectures  30 sessions of 1 hour (20%) 
Practical classes  5 sessions of 1 hour (3%) 
Private study  115 hours (77%) 
Total  150 hours 
Private study description
Private study and revision
Costs
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 C4
Weighting  Study time  

Project  35%  
Project. This assignment is worth more than 3 CATS and is not, therefore, eligible for selfcertification. 

Problem set 3  5%  
Exercise sheet 3  Eligible for selfcertification by waiving. 

Problem set 4  5%  
Exercise sheet 4  Eligible for selfcertification by waiving. 

Problem set 5  5%  
Exercise sheet 5 

Inperson Examination  50%  
CS910 examination

Assessment group R1
Weighting  Study time  

Inperson Examination  Resit  100%  
CS910 resit exam ~Platforms  AEP

Feedback on assessment
Written feedback and mark breakdown for assignments.
Courses
This module is Core for:
 Year 1 of TCSAG5PA Postgraduate Taught Data Analytics
 Year 1 of TCSAG5PB Postgraduate Taught Data Analytics (CUSP)
This module is Optional for:
 Year 2 of TIMSL990 Postgraduate Big Data and Digital Futures

TCSAG5PD Postgraduate Taught Computer Science
 Year 1 of G5PD Computer Science
 Year 1 of G5PD Computer Science
 Year 1 of TPXAF344 Postgraduate Taught Modelling of Heterogeneous Systems
 Year 2 of TPXAF345 Postgraduate Taught Modelling of Heterogeneous Systems (PGDip)
 Year 1 of TIMAL99D Postgraduate Taught Urban Analytics and Visualisation
 Year 2 of TIMAL99C Postgraduate Urban Informatics and Analytics
This module is Core option list A for:
 Year 1 of TPSSC803 Postgraduate Taught Behavioural and Data Science
This module is Core option list C for:
 Year 1 of TPSSC803 Postgraduate Taught Behavioural and Data Science
This module is Option list A for:
 Year 1 of TIMSL990 Postgraduate Big Data and Digital Futures
 Year 1 of TIMAL99C Postgraduate Urban Informatics and Analytics
This module is Option list B for:
 Year 1 of TPXAF345 Postgraduate Taught Modelling of Heterogeneous Systems (PGDip)