1. DATA MINING
COURSE LEARNING OUTCOMES

2. Students are able to……
• Learn the concepts of database technology
evolutionary path which has led to the need for
data mining and its applications.
• Examine the types of the data to be mined and
present a general classification of tasks and
primitives to integrate a data mining system.
• Apply preprocessing statistical methods for any
given raw data.
• Explore DWH and OLAP , and devise efficient &
cost effective methods for maintaining DWHs.

3. Students are able to……
• Discover interesting patterns from large
amounts of data to analyze and extract
patterns to solve problems , make predictions
of outcomes.
• Comprehend the roles that data mining plays
in various fields and manipulate different data
mining techniques
• Select and apply proper data mining
algorithms to build analytical applications.

4. Students are able to……
• Evaluate systematically supervised and
unsupervised models and algorithms w.r.t
their accuracy.
• Develop practical work of DM techniques and
design hypotheses based on the analysis to
conceptualize a DM solution to a practical
problem.

6. Course learning outcomes (CLOs)
• Having successfully completed the course, student will
be able to:
• CLO 1: Evaluate and implement a wide range of
emerging and newly-adopted methodologies and
technologies to facilitate the knowledge discovery.
• CLO 2: Assess raw input data, and process it to provide
suitable input for a range of data mining algorithms.
• CLO 3: Discover and measure interesting patterns from
different kinds of databases
• CLO 4: Characterize and discriminate data
summarization forms and determine data mining
functionalities.

7. • Evaluate and select appropriate data-mining
algorithms and apply, and interpret and report
the output appropriately.
• CLO 6: Design and implement of a data-mining
application using sample, realistic data sets
and modern tools.