Data Science using R

Course Objectives :

1. The fundamental knowledge on basics of data science and R programming.
2. The programs in R language for understanding and visualization of data using statistical functions and plots.
3. The fundamentals of how to obtain, store, explore, and model data efficiently.
4. The fundamentals of probability and statistics for data science.

Course Outcomes (CO)

1. CO 1 - Understand basics of data science and R programming.
2. CO 2 - Understand and visualize data using statistical functions and plots.
3. CO 3 - Explain how to obtain, store, explore, and model data efficiently.
4. CO 4 - Apply probability and statistics for data science.

UNIT-I

Structured versus unstructured data, Quantitative and qualitative data, The four levels of data: Nominal level, Ordinal level, Interval level, and Ratio level, The five steps of Data Science: Ask an interesting question, obtain the data, explore the data, model the data, communicate and visualize the results, Explore the data.

UNIT-II

How to run R, R Sessions and Functions, Basic Math, Variables, Data Types, Vectors, Conclusion, Advanced Data Structures, Data Frames, Lists, Matrices, Arrays, Classes, R Programming Structures, Control Statements, Loops, - Looping Over Nonvector Sets,- If-Else, Arithmetic and Boolean Operators and values, Default Values for Argument, Return Values, Functions are Objects, Recursion.

UNIT-III

Mathematics: Vectors and matrices, Arithmetic symbols, Graphs, Logarithms/exponents, Set theory, Linear algebra. Probability: Basic definitions, Probability, Bayesian versus Frequentist, Compound events, Conditional Probability, The rules of probability, Collectively exhaustive events, Bayes theorem, Random variables

UNIT - IV

Statistics: Obtaining data, Sampling data, Measuring Statistics, The Empirical rule, Point estimates, Sampling distributions, Confidence intervals, Hypothesis tests

Textbook(s):

1. Sinan Ozdemir, “Principles of Data Science”, Packt.
2. Norman Matloff, “The Art of R Programming”, Cengage Learning.

References:

1. G. Jay Kerns, “Introduction to Probability and Statistics Using R”, First Edition.
2. Nina Zumel, John Mount, “Practical Data Science with R”, Manning Publications, 1st Edition, 2014