Now, learn programming languages in the comfort of your home, in any part of the world. I can help you learn programming in R Programming for your university, college or school exams.
The syllabus as shown alongside is a typical syllabus for most exams. I can modify the topics covered as per your exam syllabus.
All teaching is online through live (not recorded) lectures wherein you will be able to interact with me in real-time and get your difficulties solved immediately.
You will learn theory and practicals and see how programs develop online.
- Solved programs
- PDF Exercise sheets
- Practical Tips
- Difficulty Solving Sessions
- Basic math and statistics
- Logical bent of mind
|SYLLABUS OF |
For SCHOOL, COLLEGE, AND UNIVERSITY SYLLABUS
|Fundamentals of Programming.|
What are High Level Languages and Low Level Languages?
What are Compiler and Interpreter?
Features of R Programming Language
Introduction to R console and editor, idea of packages in R
Types of data- integers, characters, numeric, logical, factor, dates, etc.
Types of objects: lists, vectors, matrices, data-frames, etc.
Creating and working with simple vectors
Merging vectors into a single data set
|Interacting with Excel|
Importing data from an excel sheet into R and exporting data from R to Excel.
Assigning column names, extracting individual rows and columns.
Basic mathematical and statistical operations- adding and subtracting across row and columns, calculating measures of central tendency and dispersion.
Introduction to datasets in R packages
Dealing with missing data
|Functions, Conditional Execution & Looping|
Writing functions and if-else statements
Strings and operations on strings
Introducing the basic plot function- scatter plots, lines, steps, box plots.
Adding parameters to the basic plot function- creating legends, adding background colors, grid lines, viewing multiple graphs in a single pane, etc.
Bar plots, histograms, pie charts.
Quintiles, Percentiles, Q-Q plots.
Probability- counting, generating random variables, discrete and continuous probability functions
Linear regression and its diagnostic checks and plots.
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