Credit- Degree applicable | Effective Quarter: Fall 2020 | I. Catalog Information
| CIS 64H | R Programming | 4 1/2 Unit(s) |
| Advisory: EWRT 211 and READ 211 (or LART 211), or ESL 272 and 273; CIS 22A or CIS 36A or CIS 40. Lec Hrs: 48.00
Lab Hrs: 18.00
Out of Class Hrs: 96.00
Total Student Learning Hrs: 162.00
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)
| | Design, implement and debug R programs to process data from various sources for data analysis. |
| | 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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