COSC 3337: Data Science I in Fall 2024 (Dr. Eick )

Goals of the Data Science I Course

1.	will know what the goals and objectives of data science are and how to conduct a data science project.
2.	will have a sound knowledge of basic statistics and basic machine learning concepts.
3.	will have sound knowledge about exploratory data analysis
4.	will have knowledge of popular classification techniques, such as decision trees, support vector machines, ensembles and neural networks.
5.	will have some basic knowledge about how to construct distance functions.
6.	will have detailed knowledge of popular clustering algorithms, such as K-means, DBSCAN, and hierarchical clustering and cluster evaluation.
7.      will have some basic knowledge about anomaly and outlier detection.
8.      will get some basic knowledge about association analysis.
9.	will get hands-on exposure in  the course assignments how  to apply data analysis techniques  to real world data sets. 
You will also obtain valuable experience in creating data visualizations, how to select parameters of data analysis 
tools, how to interpret and evaluate data analysis results, and data storytelling. 
10.	will get some practical experience with respect to popular 
data analysis and visualization environments, such as R or Python Data Science frameworks, and their popular libraries. 

Course Content

1.	Introduction to Data Analysis, Data Science and Data Mining  
2.      Preprocessing 
3.	Exploratory Data Analysis: How to Visualize and Compute Basic Statistics for Datasets and How to Interpret the Findings 
4.      Brief Introduction to R and Python Tools for Data Science 
5.	Introduction to Supervised Learning: Basic Concepts, Decision Trees, Instance-based Learning, 
        Support Vector Machines and Neural Networks
6.      Density Estimation 
7.      Outlier and Anomaly Detection
8.	Introduction to Clustering and Similarity Assessment 
9.      Data Storytelling 
10.     Introduction to Association Analysis Centering on the Apriori Algorithm (short) 
11.     Introduction to Deep Learning Centering on Autoencoders 
12.     Ethical Issues of Data Science (short)
13.     Advanced Clustering 
14.     Spatial Data Analysis and Spatial Data Mining 

Basic Course Information

Course Materials

Recommended Text:

P.-N. Tang, M. Steinback, and V. Kumar: Introduction to Data Mining,

Addison Wesley, 2018.

Link to Book HomePage

Other Material:

Jiawei Han and Micheline Kamber, Data Mining: Concepts and Techniques

Morgan Kaufman Publishers, Third Edition, 2011.

Link to Data Mining Book Home Page

NIST/SEMATECH e-Handbook of Statistical Methods (good onlne source covering exploratory data analysis, statistics, modelling and prediction)

News COSC 3337 (Data Science I) Fall 2024

• Dr. Eick's last office hour in the Fall 2024 semester will be Th., November 21, 8:50-10a; moreover he will have an extra office hour on Wednesday, November 20, 4:10-5p!
• There will be another poll on Nov. 21 around noon to obtain student feedback concerning the teaching of COSC 3337!
• Due to your instructor's originally, unexpected conference travel in early December 2024, the COSC 3337 grades have to completed by December 4. Therefore, the final exam, originally scheduled for Dec. 9, 2024, will be replaced by a third course exam which will take place on the last day of classes: Tuesday, November 26, 11:30a in SEC 103!
• If you have something important to discuss with Dr. Eick or the course TAs, please use e-mail and not MS Teams!!

Important Dates in Fall 2024 (preliminary; will be finalized by Sept. 4, 2024)

Course Elements and Their Tentative Weights for 2024

Fall 2024 Problem Set Tasks

2024 Group Homework Credit Groups, Tasks and Presentation Dates

Course Exams

2024 Late Submission Policy

Tasks are due at the time specified; however, 
a. tasks that are submitted one day late receive a 12% penalty; multiply task score with 0.88
b. tasks that are submitted two days late receive a 30% penalty; multiply task score with 0.7
c. task that are more than 2 days late will receive a score of 0. 

COSC 3337 Data Science I: Lecture Notes

Grading

Only machine written solutions are accepted (the only exception to this point are figures and complex formulas) in the assignments/problem sets. Be aware of the fact that our only source of information is what you have turned in. If we are not capable to understand your solution, you will receive a low score. Moreover, students should not throw away returned assignments or tests.