Executive Program Practical Connection Assignment
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CourseDescriptionsyllabus.docxCourse 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
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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 |
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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 |
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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
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Assigned work due Sunday, 11:59 PM EST Discussion boards: first post due Wednesdays, 11:59 PM EST
Discussion board 10 points |
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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?
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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 |
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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 |
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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.
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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 |
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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 |
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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
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Assigned work due Sunday, 11:59 PM EST Discussion boards: first post due Wednesdays, 11:59 PM EST
Visualizing data in R 40 points |
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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 |
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July 5 – July 11 |
Week 10
Assignments Midterm assessment
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Assigned work due Sunday, 11:59 PM EST Midterm assessment 40 points |
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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 |
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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 |
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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
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Assigned work due Sunday, 11:59 PM EST Discussion boards: first post due Wednesdays, 11:59 PM EST
Discussion board 10 points |
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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 |
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August 9 – August 15 |
Week 15 Assignments Final assessment: Putting it all together
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Assigned work due Sunday, 11:59 PM EST
Final assessment 50 points |
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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? |
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