Course Descriptionthis Course Is Intended To Introduce Students To Mod ✓ Solved

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. Galfarelli, M., & Rizzi, S. (2020).

A model-driven approach to automate data visualization in big data analytics. Information Visualization, 19 (1), 24-47. 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.

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. 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]. Jee, K. (2020, April 6). Data science project from scratch – part 2 (data collection) [Video]. YouTube. 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.

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. dataslice. (2020, June 21). Drag-and-drop ggplot2 graphs with the Esquisse library [Video].

YouTube. 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 . 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. 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. Assignments Discussion board What would you do to improve this course?

Paper for above instructions

Modern Techniques for Organizing, Analyzing, and Visualizing Data: A Course Overview

As we progress into an era dominated by data, the ability to visualize and interpret information effectively is becoming increasingly crucial. The course designed on modern programs and technologies enables students to develop skills for organizing, manipulating, analyzing, and visualizing data utilizing various tools, focusing on the R language as the primary foundation.

Course Content Overview

The course spans 16 weeks, each aligned with specific learning objectives and materials that reinforce data visualization and analytics principles. From the start, students will be guided through various methodologies to comprehend what data visualization entails, and the subsequent weeks will build upon these fundamentals to cover advanced concepts.

Week 1: Introduction to Data Visualization

The first week lays the groundwork with discussions on what constitutes data visualization. Students are introduced to scholarly readings, notably the multi-platform tool for robust data visualization by Allen et al. (2021) and a model-driven approach by Galfarelli & Rizzi (2020). Engaging in discussion boards enhances peer interaction, setting the tone for collaborative learning.

Week 2: Software Installation

In the second week, students familiarize themselves with the necessary software tools. Understanding machine environments is critical in data analytics, as Sinenko et al. (2019) emphasize the importance of automation in visualization processes.

Week 3: Project Planning

Weeks three through five guide students through project planning. Jee's (2020) video on starting data science projects outlines essential steps to create actionable plans. This aligns with students’ discussions, fostering a community of planning for visual projects.

Week 4: Data Understanding and Preparation

The focus shifts to understanding the nature of data in week four. Exploratory data analysis and data cleaning are critical as these processes significantly impact effective visualization (Intellipaat, 2020).

Week 5: Appropriate Visualization Techniques

In week five, discussions concentrate on determining suitable visualization methods according to data characteristics. Students will explore how the choice of visualization can influence interpretative outcomes, reinforcing practical applications through team collaborations.

Week 6: Visualization Types

Selecting the appropriate visualization for specific data becomes the focal point of week six. Various chart types are analyzed, with students learning about exploratory data analysis presented by Jee (2020). This week serves as an opportunity for students to apply their knowledge practically, as they select visual representations for their projects.

Week 7: Evaluation of Visualization Strengths and Weaknesses

Week seven prompts critical evaluations of visualization methods. By understanding strengths and weaknesses, students gain insights into optimizing visual data representation, enhancing their decision-making processes.

Week 8: Data Management Skills

In week eight, students delve deeper into the management and manipulation of data, crucial for effective visualization. This component emphasizes the integral relationship between data cleaning, modification, and visual output quality.

Week 9: Interactive and Animated Visualizations

The ninth week emphasizes the growing significance of interactive and animated visualizations. The narrative by The R Graph Gallery (n.d.) illustrates how interactivity can enhance user engagement with data.

Week 10: Midterm Assessment

A formal assessment in week ten evaluates students' understanding and application of course content thus far.

Week 11: Annotation Techniques

Understanding methods to annotate visualizations is vital. Week eleven introduces techniques that improve interpretability. Annotations play a critical role in making visualizations narrate more compelling stories, as suggested by Yanovsky (2020).

Week 12: Storytelling Through Visualization

Transformation of basic visualizations into compelling narratives occurs in week twelve. The concept of data storytelling is emphasized to enhance the audience's understanding through context-rich visualizations.

Week 13: Using Color Effectively

Week thirteen covers the psychology of color and its implications in visualizations. Students learn how color theory affects the perception of graphical data, leading to more impactful presentations.

Week 14: Integration of Aesthetic Improvements

As students finalize their projects in week fourteen, they apply aesthetic enhancements learned throughout the course to improve their visualizations' clarity and effectiveness.

Week 15: Final Assessment

Students will present comprehensive projects, demonstrating their acquired skills, culminating in an assessment that consolidates their learning journey.

Week 16: Course Reflection

The final week is dedicated to reflecting on the course. Students evaluate their progress, considering potential areas for improvement, establishing a foundation for lifelong learning in data science.

Conclusion

By the end of the course, students will emerge with the capability to contribute significantly to data science teams, deploying structured approaches to data analytic problems. They will become versed in a variety of analytic techniques and tools, ensuring they can analyze, visualize, and derive insights from big data effectively.

References

1. 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.
2. Galfarelli, M., & Rizzi, S. (2020). A model-driven approach to automate data visualization in big data analytics. Information Visualization, 19(1), 24-47.
3. 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.
4. Jee, K. (2020, April 3). Data science project from scratch – part 1 (project planning) [Video]. YouTube.
5. Intellipaat. (2020). Python vs R vs SAS | R, Python, and SAS comparison | Learn R, Python, and SAS? [Video]. YouTube.
6. Jee, K. (2020, April 6). Data science project from scratch – part 2 (data collection) [Video]. YouTube.
7. Jee, K. (2020, April 8). Data science project from scratch – part 3 (data cleaning) [Video]. YouTube.
8. Jee, K. (2020, April 10). Data science project from scratch – part 4 (exploratory data analysis) [Video]. YouTube.
9. Yanovsky, B. (2020, March 30). Data storytelling: A gentle guide (using ggplot in R). LinkedIn.
10. The R Graph Gallery. (n.d.). Interactive charts. Retrieved from [The R Graph Gallery website].
This comprehensive overview of the course on data visualization and analysis provides students the essential insights and tools necessary to excel in the dynamic field of data science.