COSC 3337: Data Science I in Fall 2022 (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, 
        Neural Networks and Regression 
6.	Introduction to Clustering and Similarity Assessment 
7.      A Brief Exposure to Deep Learning 
8.      A Brief Introduction to Association Analysis Centering on the Apriori Algorithm
9.      Outlier and Anomaly Detection
10.      Data Storytelling 

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 2022

• I enjoyed teaching the course. The letter grade distribution this semester was as follows:

  		 
  	D+	3
  	D	2
  	C+	13
  	C-	3
  	C	6
  	B+	5
  	B-	16
  	B	18
  	A-	9
  	A	7
          W       9
  	Grand Total	91

Important Dates in Fall 2022

Course Elements and Their Tentative Weights for 2022

Fall 2022 Problem Sets and Group Project

2022 Course Exams

Nov. 22, 2022 Activities

Fall 2022 Group Homework Credit Tasks and Schedule

2022 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

"Old" News COSC 3337 (Data Science I) Fall 2022

• Midterm2 has been graded; although about 30-40 students "did well" in the exam, it is our assessment, that 25+ students have been "ill prepared" for the exam and overall the results were not good. We will go solutions of some Midterm2 problems during the lecture on Nov. 29. As there will be no course activities Dec. 7-12, I strongly suggest you use this time window to get "well prepared" for the course final exam, so that the final exam results will be better than those for Midterm2!
• At the moment you mostly see information from the Fall 2021 teaching of the course; this information and teaching material will be replaced as we move along with teaching this course. The same remark applies for the teaching material, problem set and group project information and chat in MS Team 3337-Class.
• MS Teams will be used for teaching the online course; however, we will be using classical paper exams which will be given in UH class rooms. Navid and Raunak will be the TAs for this course; you find their MS Teams officehours and e-mail above.
• The lectures in the Sept.5 week will continue to discuss EDA; there will be two 40 minute labs on using R/Python for Data Science. Please attend these labs as they prepare you for Task1 and Task2 of ProblemSet1. The next topic we will discuss in the lecture is classification!
• We will be using a MS Team called "3337-Class" for the teaching of the course; please, go ahead and register for this team using the passcode '617ouxx'. You will not automatically be added to 3337-Class! We will use MS Team 3337-Class for a lot of things in the course: for online lectures, likely for problem set and group project submissions, for posting grades, and as a chat venue. You need to be a member of the team 3337-Class to take the course!
• When looking for the most recent version of a course documents, always download it from the course website! Reason for doing that: as there is no security in MS Teams---documents can be modified by any team members; consequently, documents you find in MS Teams might be outdated or modified by your class mates.
• Please post you solution files/slides for the Group Homework Credit tasks in the respective channel in MS Teams! Use you group name as the name of the file that you will post.
• A first very preliminary draft of the group project has been posted below: you can either form you own group of 4 students (request with other group sizes will be rejected) and e-mail the group information to Navid no later than Thursday, Sept. 29, 11:59 or Navid will assign you to a group. Group compositions will be posted in MS Teams by the end of the day of October 4. You will need to complete the Group Project by Saturday, November 12!
• A first very preliminary draft of the group project has been posted below: you can either form you own group of 4 students (request with other group sizes will be rejected) and e-mail the group information to Navid no later than Thursday, Sept. 29, 11:59 or Navid will assign you to a group. Group compositions will be posted in MS Teams by the end of the day of October 4. You will need to complete the Group Project by Saturday, November 12!
• The first course exam has been scheduled for Tuesday, October 4, 11:30a-12:45p in MH 170; we will use "classical paper exams"; no multiple choice questions. A review list for the exam has been posted below. Midterm1 will be open notes and books (no computers will be allowed, but calculators are okay). There will be also a 35 minute review for Midterm1 during the Sept. 29 lecture.
• An updated specification of Task2 has been upload (see link to download it below). Moreover, you get an extra day to complete it: Task2 is now due Oct. 1, 2022 at 11:59p---there will be no further extensions!

Past Exam and Review Solutions

Fall 2021 Problem Sets and Group Project

Grading

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.