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Higher Education Technical Challenges Hub: Module Specification

ICT05M1 Data Analysis

pdf version of module specification

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pdf version of module specification








Module name:

Data analysis

Scope and form:

The module will provide training in data analysis in the sense of evaluating data using analytical and logical reasoning to examine each component of the data provided to provide new knowledge and information. This module will provide face to face teaching and discussions as well as individual assessments.

Duration (weeks; Hours/week):

15 weeks, (2 hours course, 3 hours lab work, 5 hours individual work)/week

Type of assessment:

Compulsory exercises and lab work during the teaching period. Written or oral exam.

Qualified Prerequisites:

Fundamentals in Computer Science.

General module objectives:

The objective of the module is that the candidates to develop a thorough understanding of data analysis, process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information suggesting conclusions, and supporting decision-making. Data analysis has multiple presentations and approaches, encompassing diverse techniques under a variety of names, in different business, science, and social science domains. In this module we will introduce a number of advanced and multivariate data analysis methods and lead candidates through data analysis background, rationale, practical application and interpretation, using step by step explanations. Knowledge of these will help to understand, evaluate and draw informed conclusions from the complex analyses of other researchers.

Topics and short description:

Methods of data analysis; Data mining; Business Intelligence; Text analytics; Data visualization; Databases for large scale data; Big data; NoSQL; Statistical applications; Dealing with missing data; Data integration; Machine learning basics

Learning outcomes:




An overview of data analysis and its practical applications.

Use of data analysis techniques for problem solving.

Discriminate among the different analysis techniques for different purposes

Understand the differences and the application of the different data mining techniques

Capability to understand and to select the right data mining technique for practical applications

Detect the possible data mining approaches and use them adequate

Statistical applications, text analysis applications, possible methods, tools and approaches

Demonstrate ability to extract and critically interpret appropriate information from a data and text analysis output

Critical reasoning in choosing appropriate tools and successfully complete a detailed research results report

Business intelligence overview

Capability to covers data analysis that relies heavily on aggregation, focusing on business information

Able to understand business intelligence techniques to make adequate presentations

Fundamentals different machine learning techniques for knowledge acquisition

Capability to analyze and evaluate the best options for using every machine learning technique depending on the context

Able to understand basics of machine learning and be able to design and implement systems to take advantage of it

Creation and study of visual representation of data

Capability to abstract information in some schematic form, including attributes or variables for the units of information

To communicate information clearly and efficiently to users via statistical graphs, tables, and selected charts

Big data and databases for large scale data including storage and retrieval of data

Demonstrate ability to deal with large or complex data processing in which traditional applications are inadequate.

Able to deal with non-traditional data processing

Recommended literature:

Jason Kolb, Jeremy Kolb: The Big Data Revolution, 2013; Anil Maheshwari: Data Analytics Made Accessible, 2014
Craig K. Enders: Applied missing data Analysis, The Guilford Press, 2010
Ian H. Witten, Eibe Frank, Mark A. Hall: Data Mining: Practical Machine Learning Tools and Techniques, The Morgan Kaufmann, 2011
Data Mining: Concepts and Techniques, The Morgan Kaufmann, 2012
Ramesh Sharda, Dursun Delen, Efraim Turban: Business Intelligence and Analytics: Systems for Decision Support, 2014
Nathan Marz, James Warren: Big Data Principles and best practice of scalable realtime data systems, Manning 2015