Data Science, Visualization, Visualization in Data Scienceenetic programs, decision trees, WEKA

DASC 5231 -- Visualization in Data Science

Updated January 16, 2024

Office and Addresses

Delta 171 Phone 281.283.3805
email: boetticher@uhcl.edu
Secretary: Ms. Caroline Johnson, Delta 161 281.283.3860

Class Hours (Face-to-Face or Online)

Wednesday 4:00 - 6:50, Room: Delta 237, or via Zoom (If necessary)
Zoom information may be found in the Google folder for this course.

Office Hours

Wed 12 - 4 PM, Thurs 9 - 10 AM, or by appointment. Students with appointments have priority. If the suite door is locked, then call my extension (x3805) using the phone in the hallway. A Zoom session is also possible.

Teaching Assistant

Ms. Lynette Pinto
Email: pinto@uhcl.edu

TA Hours: Monday 9 - 12 PM, 7 - 10 PM;

Tuesday 9 - 12 PM; 7 - 9 PM;

Wednesday 9 - 12 PM

If you are not willing to learn, no one can help you.

If you are determined to learn, no one can stop you. -- Zig Ziglar

Course Description

Study of principles and best practices in effective data visualization using leading tools. Focus on identifying and choosing the proper visualization methods and techniques to be used in various stages of a typical data science project.

The traditional graduate student load is 3 courses. Be prepared to commit 15 to 20 hours per week to this course!

Course Goals

By the end of the course, you will be able to:

Associate different types of data and data properties (e.g. temporal) with different graph/chart constructs.
Use cutting edge visualization tools and technologies.
Critically evaluate visualizations and suggest improvements and refinements
Tell data stories with visualizations
Conduct explanatory and exploratory data analysis using visualization.
Craft visual presentations of data for effective communication.

Prerequisites

CSCI 5833 - Data Mining

Methodology

Lecture, seminar, case studies, and interactive problem solving.

Appraisal:

Exams	80%
Quizzes and Participation	5%
Homework	15%

Grades will be based solely on criteria listed above and nothing else.

Grading Scale

93+ = A; 90 = A-; 87+ = B+; 83+ = B; 80+ = B-;

77+ = C+; 73+ = C; 70 = C-; 67+ = D+; 63+ = D; 60+ = D-; 0+= F

My motto:

Foster disciplined, altruistic passion.

Required Textbook

Wilke, Claus O. Fundamentals of data visualization: a primer on making informative and compelling figures. O'Reilly Media, 2019.

Recommended Books

1) Berinato, Scott. Good charts: The HBR guide to making smarter, more persuasive data visualizations. Harvard Business Review Press, 2016.

2) Keim, Daniel, et al. "Mastering the information age: solving problems with visual analytics." (2010).

3) Munzner, Tamara. Visualization analysis and design. CRC press, 2014.

4) Murray, Scott. Interactive data visualization for the web: an introduction to designing with D3. " O'Reilly Media, Inc.", 2017.

5) Tufte, Edward. "The visual display of quantitative information." (2001).

Reference Books

1) Bertin, Jacques. Semiology of graphics; diagrams networks maps. No. 04; QA90, B7.. 1983.

2) Cielen, Davy, Arno DB Meysman, and Mohamed Ali. Introducing data science: big data, machine learning, and more, using Python tools. Manning Publications Co.,, 2016.

3) Embarak, Dr Ossama, Embarak, and Karkal. Data analysis and visualization using python. Apress, 2018.

4) Few, Stephen. Information dashboard design: The effective visual communication of data. Vol. 2. Sebastopol, CA: O'reilly, 2006.

5) Few, Stephen. "Show me the numbers." Analytics Pres (2004).

6) Freeman, Michael, and Joel Ross. Programming Skills for Data Science: Start Writing Code to Wrangle, Analyze, and Visualize Data with R. Addison-Wesley Professional, 2018.

7) Grant, Robert. Data visualization: charts, maps, and interactive graphics. Crc Press, 2018.

8) Kabacoff, Robert. R in Action. Shelter Island, NY, USA: Manning publications, 2011.

9) Kane, Frank. Hands-on data science and python machine learning. Packt Publishing Ltd, 2017.

10) Knaflic, Cole Nussbaumer. Storytelling with data: A data visualization guide for business professionals. John Wiley & Sons, 2015.

11) Krum, Randy. Cool infographics: Effective communication with data visualization and design. John Wiley & Sons, 2013.

12) Mailund, Thomas. Beginning Data Science in R. Apress, 2017.

13) Meeks, Elijah. D3. js in Action. Shelter Island, NY: Manning, 2015.

14) Myatt, Glenn J., and Wayne P. Johnson. Making sense of data II: A practical guide to data visualization, advanced data mining methods, and applications. John Wiley & Sons, 2009.

15) Needham, Mark, and Amy E. Hodler. Graph Algorithms: Practical Examples in Apache Spark and Neo4j. O'Reilly Media, 2019.

16) Pimpler, Eric. Data Visualization and Exploration with R A Practical Guide to Using R RStudio and Tidyverse for Data Visualization Exploration and Data Science Applications.

17) Riche, Nathalie Henry, et al., eds. Data-driven storytelling. CRC Press, 2018.

18) Sosulski, Kristen. Data visualization made simple: insights into becoming visual. Routledge, 2018.

19) Thomas, James J. Illuminating the path:[the research and development agenda for visual analytics]. IEEE Computer Society, 2005.

20) Tufte, Edward R., Nora Hillman Goeler, and Richard Benson. Envisioning information. Vol. 2. Cheshire, CT: Graphics press, 1990.

21) Wilkinson, Leland. "ggplot2: elegant graphics for data analysis by WICKHAM, H." (2011): 678-679.

22) Wilkinson, Leland. "The grammar of graphics." Handbook of computational statistics. Springer, Berlin, Heidelberg, 2012. 375-414.

Other Reference Materials

KDnuggets: Data Mining, Web Mining, and Knowledge Discovery Guide: A portal for data mining information.
The Data Mine: An index of resources for data mining.
Data Mining Competitions, KDD Cup, http://www.data-mining-cup.com, Kaggle.com

Conferences, Journals, and Organizations

ACM's Special Interest Group on Knowledge Discovery and Data Mining (SIGKDD): A premier group promoting knowledge discovery R&D. Organizes one of the top conferences in the area.

Data Visualization Software

R (Windows): A programming language and software environment for statistical computing and graphics. Third-party packages support machine learners. It is available on the Google Drive.
RStudio: GUI interface for R. It is available on the Google Drive.
Python: Available for various platforms. The link is for a Windows environment.
Tableau: Tableau helps people see and understand data. Our visual analytics platform is transforming the way people use data to solve problems. See why organizations of all sizes trust Tableau to help them be more data-driven.
Infogram: Infogram is an intuitive visualization tool that empowers people and teams to create beautiful content.
Sisense: Sisense Fusion is an AI-driven embedded analytics platform that infuses intelligence at the right place and the right time, every time.
Finereport: It supports 3 types of reports. General Report: a special design to create complex reports. Aggregation Report: an innovative design to create irregular reports. Dashboard: a dedicated design to create a dashboard for multi-dimensional data analysis.
GoogleCharts: Google Charts provides a perfect way to visualize data on your website. From simple line charts to complex hierarchical tree maps, the chart gallery provides a large number of ready-to-use chart types.
Grafana: Grafana is used to compose observability dashboards with everything from Prometheus & Graphite metrics, to logs and application data to power plants and beehives.
Dundas BI: Dundas BI is an end-to-end business intelligence platform that simplifies the entire analytics process and empowers everyone to visualize and analyze data.
Adaptive Insights: As businesses adjust and adapt to changing business environments, the need for real-time planning capabilities is accelerating. Making faster, informed decisions across all areas of the business requires a continuous planning process. Workday Adaptive Planning offers customers a modern platform that ties data, people, and plans together in one version of truth, accessible in the cloud to finance, HR, sales, functional business leaders, and more. Adding Workday Adaptive Planning to Azure offers increased flexibility as customer demand for cloud-based planning grows.
Power BI: Power BI is a collection of software services, apps, and connectors that work together to turn your unrelated sources of data into coherent, visually immersive, and interactive insights. Your data may be an Excel spreadsheet, or a collection of cloud-based and on-premises hybrid data warehouses. Power BI lets you easily connect to your data sources, visualize and discover what's important, and share that with anyone or everyone you want.
WhatAGraph: Marketers use Whatagraph to track progress and make result-based decisions. Set up KPIs for each marketing channel, track expenses, and see how well you're achieving your marketing objectives.
D3 (Data Driven Documents): D3.js is a JavaScript library for manipulating documents based on data. D3 helps you bring data to life using HTML, SVG, and CSS. D3’s emphasis on web standards gives you the full capabilities of modern browsers without tying yourself to a proprietary framework, combining powerful visualization components and a data-driven approach to DOM manipulation.
REACT: React.js is an open-source JavaScript library that is used for building user interfaces specifically for single-page applications. It allows developers to create large web applications that can change data, without reloading the page. The main purpose of React is to be fast, scalable, and simple.
Excel
Google Sheets
Flourish
Datawrapper
ArcGIS StoryMaps

Schedule (Tentative)

Jan 17 – What is Visualization? Why is it important? Course overview

************************************************************************

*** All course materials are located in the Google Drive folder. ***

*** You are expected to bring a copy of the notes to all lectures. ***

*** I strongly recommend you place the notes in a 3-ring binder. ***

************************************************************************

Assign Homework 1 - Graph, Chart Analysis

Point value: 100 points

Due date: Wednesday, February 7 at 4^PM. Email to boetticher@uhcl.edu

FOR THIS WEEK (IF NOT SOONER)

· Read: Syllabus

· Read: WK00 Notes - Orientation Visualization - 20230816

· Read: WK01 Notes - What is Visualization - Why is it important (All notes in the Zip file)

· Read: Wilke - Chapter 1

FOR NEXT WEEK (IF NOT SOONER)

· Read: WK02 Notes - Taxonomy of Data

· Read: WK02 NotesB - Overview of Descriptive Statistics

· Read: Wilke - Chapter 2

Jan 24 – Taxonomy of Data

FOR NEXT WEEK (IF NOT SOONER)

· Read: WK03 Notes - Taxonomy of Graphs and Charts (All notes in the Zip file)

· Read: Wilke - Chapters 3 through 5

Jan 31 – Taxonomy of Charts and Graphs

FOR NEXT WEEK (IF NOT SOONER)

· Read: WK04 Notes - Converting Data to Graphs, Charts

Feb 07 - Mapping Data to Visualization

FOR NEXT WEEK (IF NOT SOONER)

· Read: WK05 Notes - Excel and RAWGraphs

Feb 14 - RawGraphs

Assignment 1 is due. Email to boetticher@uhcl.edu

Assign Homework 2 - Promo

Point value: 100 points

Due date: Wednesday, February 21 at 4^PM. Email to boetticher@uhcl.edu

FOR NEXT WEEK (IF NOT SOONER)

· Read: WK06 Notes - Graphing in R

Feb 21 – Data Visualization in R

Assignment 2 is due.

Assign Homework 3 - TBD

Point value: 100 points

Due date: Wednesday, March 06 at 4^PM. Email to boetticher@uhcl.edu

FOR NEXT WEEK (IF NOT SOONER)

· Read: WK07 Notes - Graphing in Python

Feb 28 – Data Visualization in Python

FOR NEXT WEEK (IF NOT SOONER)

· Read: WK08 Notes - Graphing using ChatGPT

Mar 06 – Data Visualization using ChatGPT Viz

Assignment 3 is due.

Assign Homework 4 - TBD

Point value: 100 points

Due date: Wednesday, March 27 at 4^PM. Email to boetticher@uhcl.edu

FOR NEXT WEEK (IF NOT SOONER)

· Submit: Midterm questions by Wednesday, March 8^th, 7 PM.

Use the template found on the Google Drive

Strip out any identifying information (Your name, Student ID number)

Specify whether you want me to post your questions on the Google Drive.

Mar 13 ************ Spring Break *************

Mar 20 – Midterm

FOR NEXT WEEK (IF NOT SOONER)

· Read: WK10 Notes - What makes for good visualization (Animation, Color, Composition)

Mar 27 – What makes for a good visualization? (Animation, Color, Composition)

Assignment 4 is due.

FOR NEXT WEEK (IF NOT SOONER)

· Read: WK11 Notes - Tableau

Apr 03 – Data Visualization Tools: Tableau

Assign Homework 5 - Tableau

Point value: 100 points

Due date: Wednesday, April 17 at 4^PM. Email to boetticher@uhcl.edu

******** April 9 – Last day to withdraw ********

Apr 10 – Storytelling with Data

FOR NEXT WEEK (IF NOT SOONER)

· Read: WK12 Notes - Storytelling with data

Apr 17 – Story Telling with Data: Case Studies

Assignment 5 is due.

FOR NEXT WEEK (IF NOT SOONER)

· Read: WK13 - Data Visualization - Case Studies

Apr 24 – Review

FOR NEXT WEEK (IF NOT SOONER)

· Submit: Final questions by Wednesday, December 6^th, 7 PM.

Use the template found on the Google Drive

Strip out any identifying information (Your name, Student ID number)

Specify whether you want me to post your questions on the Google Drive.

May 01 – Final Exam

Other Policies

This class has 6 simple rules:

1) Be respectful of others. (3% Penalty/Cellphone or text infraction)

2) Be very passionate about your learning and do your best.

3) Be fearless - ask lots of questions in class.

4) Don't be late on anything. (10% Penalty/Day)

5) Don't ever cheat.

6) Have fun!

Miscellaneous

Any person with a disability who requires a special accommodation should inform me and contact the Disability services office or call 281 283 2627 as soon as possible.
You are expected to come fully prepared to every class!
If there is any religious observance that may interfere with any scheduled exam, homework due date, or attending class, please notify me of the situation during the first 2 weeks of class so that adjustments can be made at that time.
Please turn off all cell phones, and pagers prior to the start of class.
I am willing to provide letters of recommendation/references only if you have attained an 'A' in one of my classes, or two 'A-' in two of my classes.
I highly recommend that you seek out your advisor and complete your Candidate Plan of Study (CPS) as soon as possible. I am normally not available for advising during the summer months.

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2700 Bay Area Boulevard
Delta Building. Office 171
Houston, Texas 77058
Voice: 281-283-3805
Fax: 281-283-3869
boetticher@uhcl.edu