(18CS147) data modelLing LAB
Year
|
Semester
|
Hours/Week
|
C
|
Marks
|
L
|
T
|
P/D
|
CIE
|
SEE
|
Total
|
IV
|
I
|
-
|
-
|
2
|
1
|
30
|
70
|
100
|
Pre-requisite
|
Nil
|
COURSE OUTCOMES
At the end of the course the student will be able to
Acquire the concepts of data processing.
understand the different data mining techniques
perform data mining tasks with relevant tools
Apply statistical tools to analyse data and understand their physical meanings and implications
Apply simulation methods and understand their role in analysing large data set.
Week 1:
Modelling conceptual and logical data model components-Entities Attributes Relationship and Constraints.
Creating a logical data model by defining entities, attributes, primary key, relationship types-super and subtypes.
Week 2:
Modelling physical data model: Creating tables, column, data types, primary key constraints, foreign key constraints, unique and non unique index.
Week 3:
Forward and reverse engineering: Generate scripts from a data model; create a data model from database.
Week 4:
Alternate data modelling using ERD’s, Process modelling-Flow charts, swimlane and UML case diagrams.
Week 5:
Generating a process model for given scenario.
Week 6:
Case Study: Data model generation for given business use case.
Week 7:
Data Exploration and Analysis: Loading the data, data processing, model generation, prediction and model evaluation.
Week 8:
Model Analytical Databases-Data modeling and architecture tools(ER Studio)
Designing a data warehouse, defining dimension tables and fact tables and measures.
Week9:
Performing OLAP on Analytical Databases –Roll up,drill down,slice and dice
Week 10:
Learning ETL tools.
Week 11:
Data Integration using Talend.
Software Required:
Mysql Workbench,ERstudio(Trail),Talend.Orange.
Learning Resources:
https://www.credera.com/insights/data-modeling-explained-in-10-minutes-or-less
https://www.erwin.com/solutions/data-modeling/
https://www.tutorialspoint.com/power_bi/power_bi_data_modeling.htm
(18CS130) Python PROGRAMMING
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