Executive Program Practical Connection Assignment

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CourseDescriptionsyllabus.docx

Course Description

This course is intended to introduce students to modern programs and technologies that are useful for organizing, manipulating, analyzing, and visualizing data. We start with an overview of the R language, which will become the foundation for your work in this class. Then we’ll move on to other useful tools, including working with regular expressions, basic UNIX tools, XML, and SQL.

Course Objectives

Upon completion of this course:

1. Become a contributor on a data science team

2. Deploy a structured lifecycle approach to data analytics problems,

3. Apply appropriate analytic techniques and tools to analyzing big data

Learner Outcomes

Prepare students to have the technical knowledge and concepts and practices of Computer Information Technology

Prepare students to analyze, visualize and get insight of the data

Prepare students to think critically about the concepts and practices of Computer Information Technology

May 3 – May 9

Week 1

Objectives

By the end of the course week, you should:

· understand what constitutes data visualization

· understand the process of data visualization

Materials

Reading

Chapter 1, course textbook

Allen, M., Poggiali, D., Whitaker, K., Marshall, T. R., van Langen, J., & Kievit, R. A. (2021). Raincloud plots: A multi-platform tool for robust data visualization [version 2; peer review: 2 approved]. Wellcome Open Research, 4(63), 1-51. https://doi.org/10.12688/wellcomeopenres.15191.2

Galfarelli, M., & Rizzi, S. (2020). A model-driven approach to automate data visualization in big data analytics. Information Visualization, 19(1), 24-47. https://doi.org/10.1177/1473871619858933

Slides and video lecture for chapter 1

Assignments

UC Academic Integrity Pledge

Discussion Boards:

· Introduction yourself

· Data visualization

Unless otherwise specified, the due date is Sunday night at 11:59 PM EST of the assigned course week.

*Failing to Participate in week 1 may result in being dropped from the course.

Assigned work due Sunday, 11:59 PM EST

Discussion boards: first post due Wednesdays, 11:59 PM EST

UC Academic Integrity Pledge 

0 points

Discussion board 

10 points

May 10 – May 16

Week 2

Objectives

By the end of the course week, you should have installed the necessary software tools used in the course and familiarized yourself with the basic functionality.

Materials

Reading

Chapter 2, course textbook

Sinenko, P., Poznakhirko, T., & Obodnikov, V. (2019). Automation of visualization process for organizational and technological design solutions. In R. D. Wirahadikusumah, B. Hasiholan, & P. Kusumaningrum (Eds.), MATEC web of conferences: Vol. 270. The 2nd conference for civil engineering research networks (Article 05008). EDP Sciences. https://doi.org/10.1051/matecconf/201927005008

Slides and video lectures for chapter 2

Assignments

Install required software

Assigned work due Sunday, 11:59 PM EST

Software installation 

40 points

May 17 – May 23

Week 3

Objectives

By the end of the course week, you should:

· understand the process

· understand how to generate a plan (formulating your brief)

Materials

Reading

Chapter 3, course textbook

Watching

Jee, K. (2020, April 3). Data science project from scratch – part 1 (project planning) [Video]. YouTube. https://youtu.be/MpF9HENQjDo?list=PL2zq7klxX5ASFejJj80ob9ZAnBHdz5O1t

Slides and video lectures for chapter 3

Assignments

Discussion board:

Planning data visualization

Assigned work due Sunday, 11:59 PM EST

Discussion boards: first post due Wednesdays, 11:59 PM EST

Discussion board

10 points

May 24 – May 30

Week 4

Objectives

By the end of the course week, you should:

· understand the importance of understanding the data

· understand the different principles of managing data

· understand cleaning and exploration of data and how these actions fit in the process

Materials

Reading

Chapter 4, course textbook

Watching

Intellipaat. (2020). Python vs R vs SAS | R, Python and SAS comparison | Learn R, Python and SAS? | Intellipaat [Video]. https://www.youtube.com/watch?v=S0P4N7m9y28

Jee, K. (2020, April 6). Data science project from scratch – part 2 (data collection) [Video]. YouTube. https://youtu.be/GmW4F6MHggs

Slides and video lectures for chapter 4

Assignments

Discussion boards:

Working with data

Where do you start a visualization project?

Assigned work due Sunday, 11:59 PM EST

Discussion boards: first post due Wednesdays, 11:59 PM EST

Discussion board

10 points

Where do you start?

40 points

May 31 – June 6

Week 5

Objectives

By the end of the course week, you should understand what methods of visualization are suitable to address the plan.

Materials

Reading

Chapter 5, course textbook

Watching

Jee, K. (2020, April 8). Data science project from scratch – part 3 (data cleaning) [Video]. YouTube. https://youtu.be/fhi4dOhmW-g

Slides and video lectures for chapter 5

Assignments

Discussion board

Angles of implementation

Assigned work due Sunday, 11:59 PM EST

Discussion boards: first post due Wednesdays, 11:59 PM EST

Discussion board

10 points

June 7 – June 13

Week 6

Objectives

By the end of the course week, you should understand how to select the right type of visualization.

Materials

Reading

Chapter 6, course textbook

Watching

Jee, K. (2020, April 10). Data science project from scratch – part 4 (exploratory data analysis) [Video]. YouTube. https://youtu.be/QWgg4w1SpJ8

dataslice. (2020, June 21). Drag-and-drop ggplot2 graphs with the Esquisse library [Video]. YouTube. https://youtu.be/FWLxE-ARuO8

Slides and video lectures for chapter 6

Assignments

Discussion board

Chart types and data types

**Residency is the course week in northern Kentucky.

Assigned work due Sunday, 11:59 PM EST

Discussion boards: first post due Wednesdays, 11:59 PM EST

Discussion board

10 points 

Residency

600 points

June 14 – June 20

Week 7

Objectives

By the end of the course week, you should understand different methods to visualize data.

Assignments

Discussion board

Strengths and weaknesses of data visualization 

Clean and explore data

Assigned work due Sunday, 11:59 PM EST

Discussion boards: first post due Wednesdays, 11:59 PM EST

Discussion board

10 points

Clean and explore data

40 points

June 21 – June 27

Week 8

Objectives

By the end of the course week, you should be able to understand data management and manipulation for the visualization process.

Assignments

Visualizing data in R

Assigned work due Sunday, 11:59 PM EST

Discussion boards: first post due Wednesdays, 11:59 PM EST

Visualizing data in R 

40 points

June 28 – July 4

Week 9

Objectives

By the end of the course week, you should:

· understand effective use of interactive and animated visualizations

· understand the when it is suitable to use interactive or animated visualizations

Materials

Reading

Chapter 7, course textbook

The R Graph Gallery. (n.d.). Interactive charts. https://www.r-graph-gallery.com/interactive-charts.html

Slides and video lectures for chapters 7

Assignments

Discussion board

Interactive visualizations

Assigned work due Sunday, 11:59 PM EST

Discussion boards: first post due Wednesdays, 11:59 PM EST

Discussion board

10 points

July 5 – July 11

Week 10

Assignments

Midterm assessment

Assigned work due Sunday, 11:59 PM EST

Midterm assessment

40 points

July 12 – July 18

Week 11

Objectives

By the end of the course week, you should:

· understand the different methods to annotate visualizations

· understand effective methods to annotate visualizations

Materials

Reading

Chapter 8, course textbook

Yanovsky, B. (2020, March 30). Data storytelling: A gentle guide (using ggplot in R). LinkedIn. https://www.linkedin.com/pulse/data-storytelling-gentle-guide-using-ggplot-r-boris-yanovsky/

Slides and video lectures for chapters 8

Assignments

Discussion board

Annotating visualizations

Assigned work due Sunday, 11:59 PM EST

Discussion boards: first post due Wednesdays, 11:59 PM EST

Discussion board

10 points

July 19 – July 25

Week 12

Objectives

By the end of the course week, you should understand how to change a visualization into a story with annotations.

Assignments

Annotating visualizations

Assigned work due Sunday, 11:59 PM EST

Annotating visualizations

40 points

July 26 – August 1

Week 13

Objectives

By the end of the course week, you should:

· understand the use of color in visualizations

· understand the importance of coordinating colors within visualizations

Materials

Reading

Chapter 9, course textbook

Slides and video lectures for chapter 9

Assignments

Discussion board

Using color in visualizations

Assigned work due Sunday, 11:59 PM EST

Discussion boards: first post due Wednesdays, 11:59 PM EST

Discussion board

10 points

August 2 – August 8

Week 14

Objectives

By the end of the course week, you should understand how to combine the process, plan, and implementation, and aesthetic improvements to generate visualizations that tell a story

Materials

Reading

Chapter 10, course textbook

Slides and video lectures for chapter 10

Assignments

Discussion board

Putting it all together

Assigned work due Sunday, 11:59 PM EST

Discussion boards: first post due Wednesdays, 11:59 PM EST

Discussion board

10 points

August 9 – August 15

Week 15

Assignments

Final assessment: Putting it all together

Assigned work due Sunday, 11:59 PM EST

Final assessment

50 points

August 16 – August 19

Week 16

Objectives

By the end of the course week, you should be able to reflect on this course and understand the application of the course objectives in a real-world practical setting.

Have you worked with Python? You may find this extreme demonstration of obtuse behavior funny. 

Barouse, L. (2021, April 11). Python vs R (funny!) [Video]. YouTube.  https://youtu.be/DGrszAeMZJI

Assignments

Discussion board

What would you do to improve this course?