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STAT 362 R for Data Science Units: 3.00
Introduction to R, data creation and manipulation, data import and export, scripts and functions, control flow, debugging and profiling, data visualization, statistical inference, Monte Carlo methods, decision trees, support vector machines, neural network, numerical methods.
Learning Hours: 118 (36 Lecture, 12 Group Learning, 70 Private Study)
Requirements: Prerequisite (STAT 252/3.0 or STAT 263/3.0 or STAT 268/3.0 or STAT 351/3.0*) and (MATH 110/6.0 or MATH 111/6.0* or MATH 120/6.0 or MATH 121/6.0 or MATH 124/3.0 or MATH 126/6.0 or [MATH 112/3.0 and MATH 212/3.0]) or permission of the Department.
Offering Faculty: Faculty of Arts and Science
Course Learning Outcomes:
- Apply appropriate methods for statistical analysis and interpret the output.
- Apply common machine learning algorithms with real-world applications.
- Import and tidy data for further analysis.
- Visualize data and perform exploratory data analysis.
- Understand the basics of numerical and Monte Carlo methods.
- Understand the fundamental concepts in programming and R.