Introduction To R Programming
History and Overview of R, Installation, Introduction to R and RStudio, R interface, Cleaning and transforming data, Getting data in and out of R, Evaluation, R Objects, Numbers, Attributes, Vectors, Matrices/Arrays, Lists, Factors, Missing Values, Data Types, Structures and Frames, Names, ,…
Learning outcomes
At the end of the course, the students should be able to: 1. utilise the R programming language for data-driven functions and utilities that have been lauded across the computing industry; 2. explain the structures, functions, and operations that power the utilities of this Language across various application domains; 3. explain the structures, functions, and operations of the language; and 4. apply the R programming language to various data-driven use-cases in practical problem domains in the real-world.
Course contents
History and Overview of R, Installation, Introduction to R and RStudio, R interface, Cleaning and transforming data, Getting data in and out of R, Evaluation, R Objects, Numbers, Attributes, Vectors, Matrices/Arrays, Lists, Factors, Missing Values, Data Types, Structures and Frames, Names, , Displaying and plotting data, Reading lines of a Text File, Reading from a URL connection, Vectorised Operations, Dates and Times, Control Structures, Functions, Scoping Rules, Coding Standard for R, Looping, Debugging, Profiling R Code. Creating data products using R package. Lab work: Installation of R programming language and learning the practical basics. Practical programming exercises on R programming language in getting data in and out, evaluation, computation, finding missing values and reading lines of text files. Practical exercises on R coding and debugging. New Computing 107 DTS 299: SIWES I (3 Units C: PH 135) Learning Outcomes At the end of the course, the students should be able to: 1. explain how a typical Data Science firm operates; 2. expose students to the realities of the computing industry beyond the walls of the University; through an attachment with an organisation in the computing industry; and 3. apply the skills and knowledge that they have acquired in class towards solving real problems in actual working environments. Course Contents Students are attached to private and public organisations for a period of three months during the second-year session long break with a view to making them acquire practical experience and to the extent possible, develop skills in all areas of Data Science. Students are supervised during the training period and shall be expected to keep records designed for the purpose of monitoring their performance. They are also expected to submit a report on the experience gained and defend their reports.