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

• The Group A GHC presentations takes place during the lecture on Sept. 10! Group B and C will present on Sept. 17 and 19!
• Dr. Eick and the course TAs will have regular office hours starting We., August 21 ending on Tuesday, Novebember 26! Office hours will be scheduled as MS Teams meetings in the MS Team associated with this course.
• There will be a lab about R Basics and Task1, taught by Raunak, on Thursday, August 29; please bring your laptop to the class!
• Paper which analyzes the Old Faithful temporal eruption patters (donated by Aleia Sen)
• We will start taking F2F attendance on Thursday, August 29; attendance will count 2% towards your overall course grade!

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

Course Elements and Their Tentative Weights for 2023

Fall 2024 Problem Set Tasks

Fall 2023 Problem Sets and Group Project

2024 Group Homework Credit Groyos, Tasks and Presentation Dates

2023 Group Homework Credit Tasks and Presentation Dates

2023 Course Exams

2023 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.