Download IMPROVING BUSINESS WITH DATA MINING

PROFESSIONAL EDUCATION PROGRAMMES 2016
IMPROVING BUSINESS
WITH DATA MINING
7.5 University Credits
We live in a world that is data rich but information poor, creating the so
called DRIP syndrome. Therefore, and recently, there has been an
enormous interest in data mining and its applications in business.
Data mining is the discovery of patterns and hidden information in large
data sets. This course provides you with the details about most aspects
of data mining with focus on techniques and algorithms, and their
applications in business. The course also provides you with details in
model building and interpreting and validating results.
FACTS
Starting date
Block 1: 13th – 15th April 2016
Case study: Between the blocks
Block 2: 12th – 13th May 2016
Price
29 000 SEK
Register at
www.ltubusiness.se/utbildningar
CONTACT
You will learn from:
 Hands-on experience with algorithms used for data-mining
 Easy-to-use software Rapid-miner for analysis
 Real cases
”
The goal of the data mining course is that the participant should have
gained a solid understanding of the basic data mining concepts and
techniques and how they are used in a business context.
We designed this hands-on training for professionals wanting a
deeper knowledge of data mining and its practical applications
Ahmed Elragal
Associate Professor
Information systems
Maria Svanberg
Program manager
LTU Business AB
070 – 680 26 29
[email protected]
www.ltubusiness.se
PROFESSIONAL EDUCATION PROGRAMMES 2016
PROGRAM OVERVIEW
Content
Methods of Instruction
Lecturing, discussions and hands-on labs.
The concepts of data mining, its motivation, definition,
the relationships of data mining with database
systems, statistics, machine learning and information
retrieval.
Course Material
Course material will be provided in e-copy.
Data will be provided electronically.
Understanding and analyzing the knowledge discovery
process with emphasis on the iterative and interactive
nature of the KDD process.
University credits
Upon completion of the course it is possible for the
participant to apply for 7.5 university credits (ECTS).
Mine different kinds of data: relational, transactional,
object-relational, spatiotemporal, text, web.
Mine for different kind of knowledge like classification,
regression, clustering, frequent patterns, discriminant,
outliers etc.
Evaluate knowledge: interestingness or quality of
knowledge, including accuracy, utility and relevance.
Data mining applications: market basket analysis,
energy, insurance, sports and health.
Who will benefit from attending this course:
 Business Analyst
 Data Scientist
 Data Mining Specialist
 Decision Support Specialist
 Business Managers
 IT Consultants
 Senior Business Intelligence Staff
 Mining researchers
 Anyone who has interest in “torturing data till
they confess”
Find detailed information about course content on:
www.ltubusiness.se/utbildningar
Model and solve data mining problems with Rapid
Miner [member of Gartner’s magic data quadrant,
2015].
Instructor
Ahmed Elragal is an Associate Professor of Information Systems at Luleå
University of Technology (LTU). Prof. Elragal research is focused on big data
(analytics), business intelligence, and enterprise systems. Prof. Elragal has
obtained his PhD in 2001 in Decision Support Systems (DSS) from the University
of Plymouth, UK. He has over fifty research articles published at international
outlets e.g., Journal of Enterprise Information Management, International Journal
of Enterprise Information Systems, ECIS, HICSS, AMCIS, etc. He is a member of
the editorial board of I & M and IJBIR journals. He is the Associate Editor of the
International Journal of Information Systems and Project Management (IJISPM). In
2010, he has obtained KDD nuggets Teradata-funded award entitled “Best BI
Case Study”. In 2013, he has co-authored Pearson’s flagship MIS textbook “MIS:
Managing the Digital Firm - AWE” by (Laudon, Laudon, and Elragal). He has more
than fifteen years of consulting experience, serving different companies including
projects with: SAP, Teradata, Mobinil [an Orange Subsidiary], Hyperone [retailer];
mainly in the areas of big data, business intelligence and enterprise systems.