Big Data Analytics
R Programming
This course teaches many concepts and capabilities of the R programming language. Some of the
topics include importing data, data visualization using ggplot2, built-in R datatypes & structures,
and general R syntax. Upon completion of the course students will be able to import, analyze,
and summarize large, complex data sets using R.
PREREQUISITES
Students should have a background in statistics and statistical methods and working with structured data. No prior programming experience is needed.
Upon completion of this course, the student will be able to:
- Write simple programs in R.
- Use the R profiler to make their analysis more efficient.
- Choose appropriate R data structures for their data.
- Install R libraries and add-ons.
- Import Data from various sources into R.
- Read and understand R code.
- Write reusable functions in R.
- Produce informative visualizations of their data.
1. R Libraries
a. Installation of R
b. Using RStudio
c. Inline Help in R
d. CRAN
e. Using installr
f. Examples, Demoes, and Vignettes
g. R Utilities
2. Basic Data Types
a. character
b. numeric
c. integer
d. complex
e. logical
f. Pitfalls of floats
3. Basic Data Structures
a. vector
b. Vectorized Operations
c. list
d. matrix
e. array
f. data frame
4. Data Wrangling
a. Dealing with NA/NAN Values
b. Inline Data Manipulation
5. Data Visualization
a. Boxplots
b. Map Plots
c. Histograms
d. Bar Plots
e. ggplot2
6. Data Importing
a. Table Import
b. XML Import
c. URL Import
d. Import from SQL Database
7. Data Manipulation
a. Defining Custom Functions
b. Lazy Evaluation
c. Scoping Rules
8. Grouping Data
a. ScatterPlots
b. Data Frame Manipulation
9. Network Generation and Visualization
a. Simple Graphs
b. statnet
10. Sets and Set Operations
a. Subsetting Data Structures
11. Writing Functions in R
a. R profiler
b. R Markdown
12. Iteration
a. if-else Conditionals
b. for Loops
c. while Loops
d. repeat Loops
e. apply()-like Loop Functions
13. Factors
a. Character Vectors
b. Factor Levels
c. Factor Ordering
d. Factors vs. Continuous Variables
e. Generating Factors
14. Basic Models in R
a. Linear Regression
b. Logistic Regression
15. Advanced Models in R
a. Simulation of Data
b. Statistical Model
Is there a discount available for current students?
UMBC students and alumni, as well as students who have previously taken a public training course with UMBC Training Centers are eligible for a 10% discount, capped at $250. Please provide a copy of your UMBC student ID or an unofficial transcript or the name of the UMBC Training Centers course you have completed. Online courses are excluded from this offer.
What is the cancellation and refund policy?
Student will receive a refund of paid registration fees only if UMBC Training Centers receives a notice of cancellation at least 10 business days prior to the class start date for classes or the exam date for exams.