Print Course syllabus
Course syllabus - Predictive Data Analytics
Scope
2.5 credits
Course code
DVA478
Valid from
Autumn semester 2019
Education level
Second cycle
Progressive Specialisation
A1N (Second cycle, has only first-cycle course/s as entry requirements).
Main area(s)
Computer Science
School
School of Innovation, Design and Engineering
Ratified
2019-01-24
Literature lists
Course literature is preliminary up to 8 weeks before course start. Course literature can be valid over several semesters.
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Reference Literature
Module 1
Fundamentals of machine learning for predictive data analytics : algorithms, worked examples, and case studies
Cambridge, Mass. : The MIT Press, c2015. - xxii, 595 s.
ISBN: 9780262029445 LIBRIS-ID: 19310764
Predictive analytics : the power to predict who will click, buy, lie, or die
Revised and Updated Edition. : Hoboken : Wiley, 2016 - 1 online resource (320 s.)
ISBN: 9781119153658 LIBRIS-ID: 20062989
URL: Link
Predictive analytics, data mining and big data : myths, misconceptions and methods
2014 - xii, 248 pages
ISBN: 9781137379276 LIBRIS-ID: 19989592
Reference Literature
Module 2
Fundamentals of machine learning for predictive data analytics : algorithms, worked examples, and case studies
Cambridge, Mass. : The MIT Press, c2015. - xxii, 595 s.
ISBN: 9780262029445 LIBRIS-ID: 19310764
Storytelling with Data : a data visualization guide for business professionals
Hoboken, New Jersey : Wiley, 2015 - 267 s.
ISBN: 9781119002253 LIBRIS-ID: 18155083
Reference Literature
Module 3
Predictive analytics, data mining and big data : myths, misconceptions and methods
2014 - xii, 248 pages
ISBN: 9781137379276 LIBRIS-ID: 19989592
Applied Predictive Modeling
New York, NY : Springer New York, 2013 - XIII, 600 p. 203 illus., 153 illus. in color.
ISBN: 9781461468493 LIBRIS-ID: 14557710
Objectives
he course aims to give insights in fundamental concepts of machine learning for predictive analytics to provide actionable, i.e., better and more informed decisions in, forecasting. It covers the key concepts to extract useful information and knowledge from data sets to construct predictive modeling.
Learning outcomes
After the course, the steudents shall be able to:
1. describe the basics of machine learning for predictive analytics
2. demonstrate the ability to explore data and produce datasets suitable for analytical modeling.
3. show the ability to select suitable machine learning algorithms to solve a given problem for predictive data analytics.
Course content
Introduction: an overview of Predictive data analytics and Machine learning for predictive analytics.
Data Exploration and Visualization: presents case studies from industrial application domains and discusses key technical issues related to how we can gain insights enabling to see trends and patterns in industrial data.
Predictive modeling: consists of issues in construction of predictive modeling, i.e., model data and determine Machine learning algorithms for predicative analytics and techniques for model evaluation.
Specific requirements
90 credits of which at least 60 credits in Computer Science or equivalent, including 15 credits in programming as well as 2,5 credits in basic probability theory and 2,5 credits in linear algebra, or equivalent. In addition, Swedish course B/Swedish course 3 and English course A/English course 6 are required. For courses given entirely in English exemption is made from the requirement in Swedish course B/Swedish course 3.
Examination
Written assignment (INL1), 1,0 credit, (examines the learning objective 1 and 2), marks Fail (U) or Pass (G)
Project (PRO1), 1,5 credits, (examines learning agreement 3), marks Fail (U) or Pass (G)
A student who has a certificate from MDU regarding a disability has the opportunity to submit a request for supportive measures during written examinations or other forms of examination, in accordance with the Rules and Regulations for Examinations at First-cycle and Second-cycle Level at Mälardalen University (2020/1655). It is the examiner who takes decisions on any supportive measures, based on what kind of certificate is issued, and in that case which measures are to be applied.
Suspicions of attempting to deceive in examinations (cheating) are reported to the Vice-Chancellor, in accordance with the Higher Education Ordinance, and are examined by the University’s Disciplinary Board. If the Disciplinary Board considers the student to be guilty of a disciplinary offence, the Board will take a decision on disciplinary action, which will be a warning or suspension.
Grade
Pass, Fail