Scheme of work - Data Mining
HTU Credit
Academic Term Fall 2020 3
Value
Programme Title Higher Nationals in Computing Lecture Duration 2
HTU Course Title Data Mining Lab Duration 2
HTU Course No. 30202232 Classroom future tech
BTEC Unit Title Data Mining BTEC Credit Value 15
BTEC Unit No, 10 GLH 60
BTEC Unit Code H/615/1653 ULH 90
BTEC Unit Level 5 TQT 150
Class Schedule:
Name Section No Sunday Monday Tuesday Wednesday Thursday
Moayyad 10:00- 10:00- 10:00-
1
Yaghi 11:30 am 11:30 am 11:00
Note:
Learners should spend lesson time and non-supervised time working on assignments.
Course Description:
This unit introduces students to a range of tools, techniques and technologies for
acquiring data and processing this into meaningful information that can be used to
support business functions and processes
Learning Outcomes (LO) Assessment
1
LO1 Discuss the historical and theoretical
foundation of data mining, its scope, techniques,
and processes.
LO2 Investigate a range of data mining
techniques to discover patterns and
relationships in large data sets .
LO3 Illustrate how a data mining algorithm
performs text mining to identify relationships
within text.
L04 Evaluate a range of graph data mining
techniques that recognise patterns and
relationships in graph-based technologies.
Learning
Sessions Session Activities
Outcome(s)
Sample activities:
LO1 · Group discussion on data mining and the
Session 1 Introduction to origin of data mining.
data mining · Group activity: Find examples where data
mining is applied in the industry.
Sample activities:
· Group discussion on the theoretical and
historical aspects of data mining.
Session 2 · Group discussion on the fundamentals of
LO1
data mining
Theoretical
· Group activity: identify the building blocks
backgound of data
mining for data mining
Learning
Sessions Session Activities
Outcome(s)
Sample activities:
LO2
· Group Discussion on the different scopes
Session 3 Topic: Scope of
of data mining : Regression, classification,
data mining
clustering
Sample activities:
· Group discussion on Regression, multiple
LO2
regression polynomial regression
Topic: Data mining
Session 4 · Group discussion on the evaluation of a
Algorithms I :
regression model.
Regression
· Activity: implement a basic regression
model
Sample activities:
· Introduce students to sklearn library and
python.
LO2
Session 5 · Implement a regression model using
Activities
python and sklearn.
· Examine data manipulation techniques
using pandas and lumpy
Sample activities:
· Group discussion on classification, discrete
LO2 vs continuous variables.
Topic: Data mining · Evaluation of classification models:
Session 6
Algorithms II : accuracy, precision, recall
Classification · Activity : examine multiple classification
algorithms and identify what are pros and
cons for each model
Learning
Sessions Session Activities
Outcome(s)
Sample activities:
· Students to Implement a Classification
LO2
Session 7 model using python.
Activities
· Introduce the concept of discretization of
a continuous variables.
Sample activities:
· Group Discussion on the different
between supervised and unsupervised
LO2
learning.
Topic: Data mining
Session 8 · Activity: students to identify problems that
Algorithms II :
requires clustering vs classification.
Clustering
· Introduction to multiple clustering
algorithms , ex: k-means , Mixutre
models.
Sample activities:
· Students to implement a Clustering model
LO2
Session 9 using python sklearn
Activities
· Students to examine multiple models for
the same dataset and plot results.
Sample activities:
LO2 · Introduce students to Dimensionality
Topic: Data mining reduction: PCA
Session 10
Algorithms II : · A group discussion on the need for
Clustering dimensionality reduction and why we
need.
Learning
Sessions Session Activities
Outcome(s)
Sample activities:
LO3 · Group discussion on text representation.
Session 11
Topic: Text Mining · Discuss text representation methods : bag
of words, N-gram, word-embedding
LO3 Sample activities:
Topic: Introduction · Group discussion on Natural Language
Session 12 to Natural Processing.
Language · Discuss document preparation techniques
Processing and document similarities.
Sample activities:
· Students to implement word
representation.
LO3
Session 13 · Activity: students to load corpus and
Activities
calculate similarities.
· Introduce students to NLTK library and
introduce stemming techniques.
Learning
Sessions Session Activities
Outcome(s)
Sample activities:
LO3
· Applying classification algorithms on text.
Topic: Text
Session 14 · Examine different text classification use
Classification and
cases: sentiment analysis, classification of
clustering
content and content tagging.
Sample activities:
· Students to implement text classification
LO3
Session 15 using python.
Topic: Activities
· A discussion on the evaluation and
implementation considerations of text.
LO4
Topic: Introduction Sample activities:
Session 16 to unstructured · A group discussion on the basics of graph
data and graph- theory and it’s applications in real world.
based technologies
Sample activities:
LO4 · Group discussion on graph pattern
Session 17 Topic Graph mining, graph clustering and classification
algorithms · Group Activity identify which algorithm to
use in various situation.
Sample activities:
LO4 · Group discussion on content mining
Content Mining, techniques.
Session 18
Structure mining · Students to examine structure mining and
and usage mining examine the effectiveness of graph based
structure mining algorithms.
Learning
Sessions Session Activities
Outcome(s)
Sample activities:
· Students to implement various graph
LO4
Session 19 classification algorithms.
Topic: Activities
· Students to examine final result of graph
classification and interpret it.
Sample activities:
LO4
· A group discussion on graph classification
Session 20 Topic: Graph
· A discussion on the usages of graph
Classification
classification.
