last updated: November 28, 2023

COSC 3337: Data Science I in Fall 2023 (Dr. Eick )

Goals of the Data Science I Course

COSC 3337 Syllabus

Upon completion of this course, students
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

Instructor: Dr. Christoph F. Eick
Office hours (573 PGH)
Office Hours: TU 4:10-5p TH 8:50-10a (in MS Teams)
TA: Janet Anagli
Office Hours: MO 3-4p TU 9:30-10:30a (scheduled in MS Teams)
Email: jyanagli@CougarNet.UH.EDU
TA: Raunak Sarbajna
Office Hours: WE 1:30-2:30p TH 9:30-10:30a (scheduled in MS Teams)
class meets: TUTH 11:30a-1p in S105
Cancelled class: none yet
Lectures taught by others: Tu., October 17: Janet; Th., November 16: Raunak
All other lectures will be taught by Dr. Eick, but some lectures include labs that are taught by Janet and Raunak!

Course Materials

COSC 3337 Syllabus for Fall 2023

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 2023

Important Dates in Fall 2023

Tuesday, September 5: R-Lab taught by Raunak (last 55-60 minutes of the lecture that day)
Thursday, September 14: Using Python for Data Science & Task2 Lab (taught by Janet) taught by Janet (first 50-55 minutes of the lecture that day)
Saturday, September 23, 11:59p: Deadline to submit Task1 of ProblemSet1 focusing on Exploratory Data Analysis
Friday, September 29, 11:59p: Deadline to submit Task2 of ProblemSet1 focusing on Classification
Tuesday, October 3: Midterm1 Exam
Thursday, October 5: 25-45 minute presentation given by Raunak in preparation for the group project
Thursday, November 2: Spatial Analysis and Hotspot Discovery Lab taught by Raunak (45+ minutes)
Tuesday, November 14: Midterm2 Exam
Thursday, November 16: Deep Learning Lecture and Lab taught by Raunak in preparation of Task5
Thursday, November 23: Thanksgiving (no class!)
Thursday, November 30: Last class of the semester
Thursday, December 7, 11a: Final Exam

Course Elements and Their Tentative Weights for 2023

Problem Sets (3), Group Project and Group Homework Credit and attendance: 51%
Exams (3): 49% (14%, 15%, 20%)
Tentative weights of non-exam tasks: Problem Sets: 31-32%, Group Project: 13-15%, Group Homework Credit: 3%, Attendance: 3%.

Fall 2023 Problem Sets and Group Project

Problem Set1 (Task1: Exploratory Data Analysis for a Video Sales Dataset; Task2: Learning SVM and Decision Tree Models)

Group Project (Oct. 10-Nov. 10, 2023 (1 month), centering on analyzing solar flares; Discussions Helios Project)

Problem Set2 (consists of a clustering task which is due on Nov. 6, and an outlier detection task which is due on Nov. 20).

Problem Set3 (Task5: Autoencoders).

2023 Group Homework Credit Tasks and Presentation Dates

In this activity which will be called group homework credit, each group formed for this activity, receives a different usually homework-style problem, and they present their solution during the lecture, and share their solution in form of a Word or pptx file. The groups and e-mail addresses of the group members have been posted in the 'Group Homework Credit' channel of this section's MS Team. Below is a list of the already assigned tasks and associated groups and presentation dates; tasks will be added as we move along with the teaching of the course; tasks will be posted at least six days before a group's presenation date:

Group A and Group B Tasks (Group A will present on Sept. 12 and Group B will present on Sept. 14)
Group C and Group D Tasks (Group C will present on Sept. 19 and Group D will present on Sept. 21)
Group E and Group F Tasks (both will present on Sept. 28)
Group G Task (will present Thursday, October 12)
Group H and Group I Tasks (both will present on Thursday, October 26)
Group J Task (will present on November 7)
Group K and L Tasks (Group K will present on Nov. 7 and Group L will present on November 9)
Group M will present on November 28
Group N will present on November 30

2023 Course Exams

Mid1 Exam(Oct. 3, 11:30a, 2023): Sept 28, 2023 Review for Mid1, Review List for 2023 Midterm1 Exam, Solution Sketches October 3, 2023 Midtem1 Exam

Mid2 Exam(Nov. 14, 11:30a, 2023): Nov. 9, 2023 Review for Mid2, Review List for 2023 Midterm2 Exam, Solution Sketches 2022 Midterm2 Exam.

Final Exam(Dec. 7, 11a, 2023): Dec. 1, 2022 Review for Final Exam, Review List for 2022 Final Exam.

All Exams are in SW 101.

Nov. 21, 2023 Activities

There will be no F2F lecture on Tu., Nov. 21; instead you will watch a 30 minute video concerning the challenges of collecting and interpreting census data; finally, you will have the opportunity to receive a COSC 3337 bonus, by writing a 1-1.25 page essay about the video you watched. For more details about those activities, click the following link:
Nov. 21, 2023 Offline Tasks

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. 
There will be a short grace period of a few minutes for each submission deadline (up to the discretion of the respective Teaching Assistant); submissions that are obtained after this grace period will be considered to be late!

COSC 3337 Data Science I: Lecture Notes

I Introduction to Data Mining/Data Science (Part1: Introduction to Data Mining, Part2: Mostly Course Information, Part3: Introduction to Data Science, Preprocessing in Data Science).
II Exploratory Data Analysis (covers chapter 3 from the the First Edition of the Tan Book (download as this material is not in the second edition); more material: these slides will not be covered in 2021: Introduction to Non-Parametric Density Estimation; KDE Density Functions, Some R Data Analysis Functions I; Some R Data Analysis Functions II.
III R and Python for Data Science (only some of the listed slide sets will be covered in the lecture; Arko's Short Intro Into R (not covered, but a good "refresher" if you forgot most details of using R, because you learnt it some time ago), Scatter Plot Code, Decision Trees in R, Some useful code for Task1 ProblemSet1 (will be covered in part during the lecture), Computing Statistical Summaries In the Presense of Missing Value (NA), Functions and Loops in R, Directory containing R-code for ProblemSet3; Python: Saying Hi to Python, Python Refresher.
IV Classification (Introduction to Classification: Basic Concepts and Decision Trees, Overfitting, kNN-Classifiers and Support Vector Machines, Neural Networks, Recurrent Neural Networks (not covered in 2023), Colah's Blog: Understanding LSTMs (not covered in 2023), Ensemble Learning (not covered), Naive Bayes Classifiers&Bayes' Theorem (not covered)
V Density Estimation (Naive and Parametric Density Estimation (PDE Task (added on Nov. 6, 2023)), Non-parametric Density Estimation(slides have been added on November 3, 2023!)
VI Clustering and Similarity Assessment ( Introduction, Density-based Clustering Centering on DBSCAN, Hierarchical Clustering, Cluster Validity, R-scripts demonstrating: K-means/medoids, DBSCAN, More on PAM and using PAM/DBSCAN with dist-objects (not relevant and covered in 2022); Clustering Exercises K-Means, HC, and DBSCAN)
VII Outlier Detection
VIII Association Analysis: Brief Introduction to Association Analysis Centering on APRIORI and Sequence Mining
IX Data Storytelling
X Spatial Data Analysis: Spatial Data Analysis and Hotspot Discovery and Introduction to Spatial Data Mining
XI Introduction to Deep Learning Centering on Autoencoders (In Part 1 we are showing parts of MIT 6.S191 (MIT Deep Learning Bootcamp) videos and discuss their content (Introduction to Deep Learning (watch the first 8:20 of the video and 11:20-15:00; the remainder of the video was actually covered in the neural network part of this course), Deep Generative Learning (watch the first 22 minutes of this video; if you want to know VAEs generate "new" examples resume watching the video 31:05 for a few minutes) and maybe---if enough time---New Horizons: Diffussion Models; watching at 39:40-58:30); Part 2: Autoencoders and More on Deep Learning and Lab for Task 5 (taught by Raunak on Nov. 16, 2023)).
XII Advanced Clustering
XIII Overview of Data Preprocessing Techniques (was already discussed in the August 30 lecture)
XIV Ethical Aspects of Data Science centering Ethics Involving Census Data Collection and Interpretation (Danah Boyd Video)
XV Introduction to Data Visualization (not covered since 2021; Part1 (Most of the slides in this slideshow were created by Guoning Chen, Department of Computer Science, University of Houston), Part2 (slides were created by Alark Joshi, Department of Computer Science, University of San Francisco; Data Visualization Reading Material for DS I)


Translation number to letter grades, starting in Fall 2021:
A:100-92 A-:92-88 B+:88-84 B:84-80 B-:80-76 C+:76-71
C: 71-66 C-:66-62 D+:62-58 D:58-54 D-:54-50 F: 50-0

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.

  • In contrast to the exam grades where you receive your number grades immediately, the assignment/problemset scores, still will be curved near the end of the semester, and your curved assignment scores will be ultimately converted into a number grade using a coversion function---the conversion function incorperates the different weights of the four assignments---and, then this number grade will count about 50% towards your final course grade. In general, assignment weights were selected considering amount of work required but also difficulty was considered; moreover, group projects carry lower weights. Moreover, when looking at the detailed grade reports, be aware of the fact that number grades of 92 or higher are A's in Dr. Eick's curving. Students may discuss course material and homeworks, but must take special care to discern the difference between collaborating in order to increase understanding of course materials and collaborating on the homework / course project itself. We encourage students to help each other understand course material to clarify the meaning of homework problems or to discuss problem-solving strategies, but it is not permissible for one student to help or be helped by another student in working through assignment problems and in the course project. If, in discussing course materials and problems, students believe that their like-mindedness from such discussions could be construed as collaboration on their assignments, students must cite each other, briefly explaining the extent of their collaboration. Any assistance that is not given proper citation may be considered a violation of the Honor Code, and might result in obtaining a grade of F in the course, and in further prosecution.

    Excused Missing of Course Exams: If you miss course exams for other reasons, you might get a grade of 'F' for the exam, unless highly unusual circumstances lead to your missing of the exam!

    Attendance 2023

    Attendance counts 3% towards the course grade. Attendance will be taken starting Tuesday, August 29, 2023 throughout the remainder of the semester. Only F2F attendance counts. Attendance will be taken approx. 11:45a; that is, if you show up 30 minutes late, your attendance will not count. Therefore, 24 attendances (August(2), September (8), October (8), November (6)) will be recorded. Your number of attendances will be converted as follows into a number grade:
    23-24: 94, 22: 93, 21:92, 20:91, 19:87, 18:83, 17:79, 16:75, 15:71, 14:67, 13:63, 12:59, 11:55, 10: 51, 9:47, 0-8:43.

    Past Exam and Review Solutions

    Solution Sketches Midterm1 March 10, 2015
    Solution Sketches Midterm2 April 7, 2015
    Solution Sketches Final Exam December 10, 2018
    Solution Sketches Review1 March 1, 2016
    Solution Sketches Review1 Feb. 27, 2018
    Solution Sketches Review1 September 24+26, 2018
    Solution Sketches Midterm1 March 3, 2016
    Solution Sketches Midterm1 March 1, 2018
    Solution Sketches Midterm1 October 2, 2019
    Solution Sketches Midterm2 November 6, 2019
    Solution Sketches Final Exam December 6, 2019
    Solution Sketches Review2 April 5, 2016
    Solution Sketches Midterm2 April 7, 2016
    Solution Sketches Midterm2 April 5, 2018
    Review for Final Exam, May 3, 2016
    Solution Sketches of Review for Final Exam on April 26, 2018
    Solution Sketches Final Exam May 10, 2016
    Review2 solution sketches on November 5, 2018
    Solution Sketches Midterm Exam October 14 2021

    "Old" News COSC 3337 (Data Science I)

    Fall 2022 Group Homework Credit Tasks and Schedule

    In this activity which will be called group homework credit, each group formed for this activity, receives a different homework-style problem, and they present their solution during the lecture, and share their solution in form of a Word or pptx file. The groups and e-mail addresses of the group members have been posted in the 'File' Section of the General Channel of 3337-Class. Here is a list of the already assigned tasks and associated groups; tasks will be added as we move along with the teaching of the course:

    Group A, B and C Tasks (Group A will present during the lecture on September 13, and groups B and C will present on September 15)
    Group D Task (Group D will present during the lecture on September 22)
    Group E and F Tasks (both groups will present on September 29)
    Group G Task (to be presented on October 13)
    Group H, I and J Task (groups H and I will present on October 20, and group J will present on October 25)
    Group K Task (to be presented on November 1)
    Group L and M Task (both groups will present on Nov. 10)
    Group N Task (to be presented on Nov. 17)
    Group O Task (to be presented on Dec. 1)

    For groups see: 2022 Group Homework Credit Groups

    Fall 2022 Problem Sets and Group Project

    Problem Set1 (Task1 Specification; Task2 Specification; updated on Sept. 28)

    Problem Set2 (Task3, centering on Recurrent Neural Networks; individual task; Navid's Introduction to RNN)

    ProblemSet3 (centering on clustering; individual task)

    POIMAGIC: an Early Warning System for Streaming Spatial Events (group project October 4-November 22, 2022; Groups in 2022) <