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| Credit- Degree applicable | | Effective Quarter: Fall 2020 | I. Catalog Information
| CIS 64H | R Programming | 4.5 Unit(s) |
| | Requisites: Advisory: EWRT 211 and READ 211 (or LART 211), or ESL 272 and 273; CIS 22A or CIS 36A or CIS 40. Hours: Lec Hrs: 48.00
Lab Hrs: 18.00
Out of Class Hrs: 96.00
Total Student Learning Hrs: 162.00
Description: This course is an introduction to the R programming language and its utility in big data analytics. Topics covered include data objects, data cleansing, merging and sorting, statistical analysis of data, data graphics and visualization, and working with R-Studio. |
| Student Learning Outcome Statements (SLO)
| | | • Student Learning Outcome: Design, implement and debug R programs to process data from various sources for data analysis. |
| | | • Student Learning Outcome: Use R-graphics to display and visualize data. |
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II. Course Objectives | B. | Exhibit understanding of R data objects. |
| C. | Illustrate basic data transformation concepts. |
| D. | Demonstrate extracting data from various sources. |
| E. | Perform data manipulations to enable analysis. |
| F. | Analyze data to derive patterns and hypotheses. |
| G. | Design data visualizations to demonstrate analyses. |
III. Essential Student Materials IV. Essential College Facilities | | Access to a computer lab with RStudio |
V. Expanded Description: Content and Form | 2. | Introduction to R and RStudio |
| 3. | Installing and using R packages |
| 4. | Working with R workspaces |
| B. | Exhibit understanding of R data objects. |
| 5. | Local data import/export |
| C. | Illustrate basic data transformation concepts. |
| 2. | Character and String Manipulation |
| D. | Demonstrate extracting data from various sources. |
| 3. | Connecting to external data sources |
| 4. | Data in single and distributed environments |
| E. | Perform data manipulations to enable analysis. |
| F. | Analyze data to derive patterns and hypotheses. |
| 1. | Data architecture patterns |
| G. | Design data visualizations to demonstrate analyses. |
| 1. | Core concepts of data graphics and visualization |
| 3. | Customizing graphics with 'ggplot2' |
VI. Assignments | A. | Reading: Required reading from the textbook and class notes |
| B. | Programs: 7-10 programming homework assignments. |
| C. | Group Project: Data exploration and visualization of assigned datasets. |
VII. Methods of Instruction | | Lecture and visual aids
Discussion of assigned reading
Discussion and problem solving performed in class
Collaborative learning and small group exercises
Collaborative projects
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VIII. Methods of Evaluating Objectives | A. | One or two midterm examinations requiring some programming, concepts clarification and exhibiting mastery of R programming constructs presented in the course. |
| B. | A final examination requiring concepts clarification and exhibiting mastery of data exploration, analysis and visualization principles. |
| C. | Evaluation of programming assignments and group project, based on correctness, documentation, code quality, and test plan executions. |
IX. Texts and Supporting References | A. | Examples of Primary Texts and References |
| 1. | Wickham, Hadley and Grolemund, Garrett: R for Data Science: Import, Tidy, Transform, Visualize, and Model Data 1st Edition. O'Reilly. ISBN-13: 978-1491910399, 2017. |
| 2. | Campbell, Matthew: Learn RStudio IDE: Quick, Effective, and Productive Data Science 1st Edition. Apress. ISBN-13: 978-1484245101, 2019. |
| B. | Examples of Supporting Texts and References |
| 1. | Matloff, Norman: The Art of R Programming: A Tour of Statistical Software Design 1st Edition. William-Pollock. ISBN-13: 978-1593273842, 2011. |
| 2. | Teetor, Paul: R Cookbook: Proven Recipes for Data Analysis, Statistics, and Graphics 1st Edition. O'Reilly. ISBN-13: 978-0596809157, 2011. |
X. Lab Topics | A. | Data types and data structures |
| B. | Flow control and looping |
| C. | Writing and calling functions |
| D. | Split/apply/combine pattern |
| E. | Working with character data and regular expressions |
| F. | Regular expressions and web scraping |
| G. | Reshaping data and database access |
| J. | Data and predictive analysis |
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