CSCE 411/811 - Data Modeling for Systems Development (Fall 2021)

COVID-19: Policy on Face Coverings
To protect the health and well-being of the University and the wider community, UNL has implemented a policy requiring all people, including students, faculty, and staff, to wear a face-covering that covers the mouth and nose in all indoor spaces including the classroom. Failure to comply with this policy is interpreted as a disruption of the classroom and may be a violation of UNL’s Student Code of Conduct.

Student conduct expectations: https://studentconduct.unl.edu/covid-19-related-expectations-and-non-compliance

Professor

Dr. M. R. Hasan
123E Avery Hall
hasan@unl.edu
URL: https://cse.unl.edu/~hasan/

Office Hours (in-person at Avery 123E):

Tuesday & Thursday: 12.20 PM - 2.00 PM
If the main door of Avery 123 is closed, email me or call me at (402) 472 5026. I will open it for you.

Syllabus

Lecture Time & Venue:
Tuesday & Thursday: 9.30 AM - 10.45 AM (Avery 106)

Graduate Teaching Assistant:

Rojina Deuja
rdeuja2@huskers.unl.edu

Office Hours (over Zoom):

Tuesday: 12.00 PM - 1.00 PM
Wednesday: 12.00 PM -1.00 PM

Zoom Link: https://unl.zoom.us/j/91445970641

Meeting ID: 914 4597 0641

Text Resources:

Though there are no required textbooks for this course. However, the following books are recommended.

Data Modeling:

Data Modeling Essentials (3rd Edition) by Graeme Simsion & Graham Witt
Database Design for Mere Mortals: A Hands-On Guide to Relational Database Design (3rd Edition) by Michael J. Hernandez
Data Modeling Made Simple: A Practical Guide for Business and IT Professionals (2nd Edition) by Steve Hoberman, Carol Lehn, Michael Blaha, Bill Inmon, Graeme Simsion
Database Systems: The Complete Book (2nd Edition) by Hector Garcia-Molina, Jeffrey D. Ullman, Jennifer Widom
Data Modeling for MongoDB by Steve Hoberman
Interactive Data Visualization for the Web: An Introduction to Designing with D3 (2nd Edition) by Scott Murray (O'Reilley)
Graph Databases: New Opportunities for Connected Data (2nd Edition) by Ian Robinson, Jim Webber, Emil Eifrem (O'Reilley)

Data Analysis:

Introduction to Data Mining (2nd edition) by Pang-Ning Tan, Michael Steinbach, Anuj Karpatne, Vipin Kumar
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems (2nd Edition) by Aurélien Géron (O'Reilley)
Data Science from Scratch by Joel Grus (O’Reilly) Chapter 2 & 3
Python for Data Analysis (2nd Edition) by Wes McKinney (O'Reilley)

The eBook version of any textbook published by O'Reilley can be found in the UNL library.

Schedule (subject to change):

Week	Date	Topic	Note
1	August 24 August 26	Tuesday: Course introduction - Data Modeling & Analysis Thursday: Structured Data Modeling: Normalization
2	August 31 September 2	Tuesday: - Rules of Normalization - Introduction to Conceptual (Entity-Relationship) Modeling Thursday: - Conceptual (Entity-Relationship) Modeling: Top-down Design	August 30: Last day to drop the course and receive 100% refund August 30: Assignment 1 posted
3	September 7 September 9	Tuesday: - Conceptual Modeling: Many-to-Many & Recursive Relationships - Conceptual Modeling: Subtyping 1 Thursday: - Conceptual Modeling: Subtyping 2 - Logical Modeling I
4	September 14 September 16	Tuesday: - Logical Modeling II - Physical Modeling I - Physical Modeling II Thursday: - Physical Modeling: ISAM - Physical Modeling: B+ Tree	Assignment 1 Due: September 16 (Thursday) 9.30 AM Late submission is not allowed
5	September 21 September 23	Tuesday: - Assignment 1 & Beyond - Semi-Structured Data Modeling; NoSQL - MongoDB Introduction Thursday: - MongoDB: Installation & Usage - MongoDB: Data Modeling - NoSQL: Graph Database
6	September 28 September 30	Tuesday: - NoSQL: Graph Model and Graph Database Thursday: - Neo4j
7	October 5 October 7	Tuesday: Neo4j & Cypher Thursday: - Roadmap for the rest of the semester - Data Analysis & Data Models; Clustering (K-Means)
8	October 12 October 14	Tuesday: - Getting Started With Essential Python Libraries - K-Means: Practical Issues Thursday: Midterm Exam 9.30 AM - 10:50 AM - Online via Canvas - Syllabus: From beginning to MongoDB - Open book/note/notebook - Multiple choice type question - Please bring your laptop or borrow one from the library - There will be no makeup exam	Assignment 2 Due: October 12 (Tuesday) 9.30 AM
9	October 19 October 21	Tuesday: Fall Semester Break Thursday - Assignment 3 Discussion - Clustering: K-Means & Gaussian Mixture Model or GMM	Assignment 3 posted: October 21
10	October 26 October 28	Tuesday: Clustering (Gaussian Mixture Model or GMM) & Anomaly Detection (GMM) Thursday - Assignment 3 Discussion - Clustering & Anomaly Detection (DBSCAN) - DBSCAN: Algorithm; Advantages; Disadvantages; Beyond DBSCAN
11	November 2 November 4	Tuesday: - Anomaly Detection (Local Outlier Factor) Thursday: - Anomaly Detection (Isolation Forest)
12	November 9 November 11	Tuesday: - Data modeling & data visualization; Low-Dimensional Data Visualization: using D3.js; HTML, CSS Thursday: - DOM, SVG	Assignment 3 Due: November 11 (Thursday) 9.30 AM Assignment 4 posted: November 11
13	November 16 November 18	Tuesday: - D3 Thursday: - D3: Interactive Visualization
14	November 23 November 25	Tuesday: - Market-basket Analysis, Association-Rule Mining, Support, Confidence - Frequent itemset generation: Brute-force approach Thursday: Thanksgiving holiday
15	November 30 December 2	Tuesday: - Frequent itemset generation: Efficient approach Apriori algorithm - Improve the Complexity of Data Analysis: Data Modeling for Apriori Algorithm Thursday: - Apriori algorithm: association rule mining - Build an end-to-end information system	Assignment 4 Due: December 2 (Thursday) 9.30 AM No late pass is allowed on the final assignment
16	December 7 December 9	Tuesday: - High-Dimensional Visualization: Dimensionality Reduction (PCA) Thursday: - High-Dimensional Visualization: Dimensionality Reduction (t-SNE)
17	December 15	Final Exam: December 15 (10.00 AM - 12.00 PM) - Online via Canvas - Syllabus: From beginning to Market-Basket Analysis (topics covered up to December 2) - Open book/note/notebook - Multiple choice type question - Please bring your laptop or borrow one from the library - There will be no makeup exam