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.

Features:

• Solved programs
• PDF Exercise sheets
• Practical Tips
• Difficulty Solving Sessions

Course Prerequisites:

• Basic math and statistics
• Logical bent of mind

SYLLABUS OF R Programming ONLINE COURSE For SCHOOL, COLLEGE, AND UNIVERSITY SYLLABUS TELEGRAM (Message): https://t.me/scitechgen Email: scitechgen@outlook.com
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
Fundamental Concepts 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.
Using Data Introduction to datasets in R packages Dealing with missing data Summarizing data
Functions, Conditional Execution & Looping Writing functions and if-else statements Creating loops
Strings Strings and operations on strings
Visualization 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 Probability- counting, generating random variables, discrete and continuous probability functions
Linear Regression ANOVA Linear regression and its diagnostic checks and plots.