Course Duration – 3 weeks
Batch 1: Tue, Wed, Thu (8pm – 10pm)
Batch 2: Fri, Sat, Sun (8pm -10pm)
Import, transform and Visualize data using R programming – A Powerful open-source programming language for Data Analysis and Statistical Computing.
The era of data analysis using programming language has arrived. Business use data as their single most asset to remain competitive. They are looking for professionals who can give them insights by churning large and complex data. And they are ready to PAY BIG BUCKS for your services!
How learning R Programming will benefit me?
There are around 2 million R users globally. Learning R programming will allow you to join a specialist developer community. By acquiring R programming skills, you can differentiate your Analytics skills and stand out from a crowd. As per The PYPL PopularitY of Programming Language Index (index is created by analyzing how often language tutorials are searched on Google.) R ranks in top 10 most searched programming languages on Google. Source: https://pypl.github.io/PYPL.html
Data Analyst with R programming skills can demand average salary of $80K per Anum. In India, Analyst with R programming skills gets a average salary of around 7 Lakhs per Annum. Refer below salary statistics for R developer – Source: Naukri.com
This is fundamental R programming course. After completing this course you can able to:
automate most of the mundane data analysis tasks such as importing, filtering, selecting and visualising the data.
Increase the productivity by 10X since most of the mundane tasks will be automated leaving you more time in data analysis and insights
Build a solid foundation to learn more advance R packages such as Tidyverse and Tidymodels used to carry out Data Analysis and Machine Learning. If you plan to shift your career from Data Analyst role to Data Scientist role, this course will act as springboard for exciting career in Machine Learning.
Is it really worth the effort to learn a programming language when Excel or Power BI can handle the majority of my data analysis tasks?
I have written a detail blog on this topic. You can refer it here. Few important reasons to learn programming are:
Automation: R can automate mundane tasks such as importing, filtering and sorting the data for analysis. It is said that more than 80% of data analyst time goes into cleaning and filtering the data. R can handle data manipulation tasks with ease, thereby increasing the productivity atlest by 10X. So that, analyst can spend more time in analyzing the data rather than fiddling with mundane data cleaning tasks.
R can easily handle more than million data observations with ease, whereas Excel becomes unstable while working with large data sets.
In R, you write scripts – a notepad like file in which all analysis steps are written in easy to read syntax which allows you to track all the analysis steps without concerned about any hidden functions or modifications happening in the background. Such transparency in the analysis is not possible in Excel.
R also has an edge over point and click BI softwares such as Power BI/Tableau:
Unlike Power B/ Tableau, R is free and open source software saving millions of dollars in licensing fees.
R is known for its extensive data visualization capability. Power BI/Tableau offer limited customizations to its graphs and dashboards. While R has endless customization options for data visualization.
What is in this course?
This course is divided into six modules:
Module 1 - R Logistics
Install R
Brief orientation of R interface
Install RStudio – Integrated Development Environment
Brief orientation of RStudio interface
Understand R Help document system
Understand R package system
Module 2 - Data structures
R objects and its classes
R vectors and its manipulation
R matrices and its manipulation
R Data frames and its manipulation
R factors and its manipulation
R lists and its manipulation
Module 3 - Import Data
Importing CSV file in R using RStudio interface
Importing CSV file in R using read.csv function
Importing delimited files in R
Importing Excel file in R using RStudio interface
Importing Excel file in R
Introduction to Readxl package
Module 4 - Flow Control
FOR loop construct
WHILE loop construct
If-else condition statement
break statement
next statement
Module 5 - Vectorised Functions
Writing custom functions in R
apply function
lapply function
sapply function
tapply function
Module 6 - Data Visualisation
Bar Graphs
Box-plots
Line Graphs
Scatter Plots
Heat Maps
Histograms
Handling Time Series Data
Salient Features:
18 hours of live sessions
Downloadable PDF session notes
Access to Session Recordings
100+ exercises and mini-assignments to check your understanding
300+ lines of code you can adapt for your own projects
R scripts with all of the code
Downloadable datasets so you can follow along at home
Certificate of completion at the end of the course
Free access to future course updates
Follow-up calls post course completion to check learning effectiveness.
Bonus Material (PDF Download):
Introduction to R Markdown:
R markdown is a simple and easy to use plain text language used to combine your R code, results from your data analysis (including plots and tables) and written commentary into a single nicely formatted and reproducible document (like a report, publication, thesis chapter or a web page). R Markdown is super useful when you want to present your analysis results to wider audience using simple text narratives, charts and tables. This downloadable PDF document will give you birds eye view of this very important package in R language.
Introduction to tidyverse Package for Data Manipulation:
tidyverse is a collection of different packages used to carry out Data Analysis tasks. tidyverse collection of packages mark the important milestone in R programming. These packages share common design philosophy, thereby seamlessly working together to carry out analysis tasks. In this downloadable PDF document, we will cover the basic understanding of various tidyverse packages. Please check my upcoming course on tidyverse wherein I will go deep in each of these packages.
String Manipulation with R
String manipulation basically refers to the process of handling and analyzing strings. It involves various operations concerned with modification and parsing of strings to use and change its data. R offers a series of in-built functions to manipulate the contents of a string. In this downloadable PDF, we will study different functions concerned with the manipulation of strings in R.
This is unlike ANY other R Programming course
The course is combination of pre-recorded videos (recreation of my You-tube videos that generated more than 5K views in short span of time), reading material (downloadable PDF files of course notes) and live zoom sessions with me.
In the live sessions, you will be given the Exercises, quizzes and real-world data-sets to work on the concepts you have learned. The learning happens through discussions and hands-on application of concepts on real world data sets. These live sessions are the major component of the course, making you active learner. As per Edgar Dale, the American educator who developed the cone of Experience (https://en.wikipedia.org/wiki/Edgar_Dale):
We remember
10% of what we see
50% of what we see and hear
70% of what we discuss with others
80% of what we personally experience
What is the learning outcome?
At the end of the course, you will be able to:
Install R and RStudio on your machine
Use various features of RStudio interface
Create and save R scripts
Import CSV and Excel files in R environment
Know R data types and its various data structures
Manipulate R data structures
Use control flow statements, and loops
Create custom functions and use vectorized functions
Generate basic visualizations to understand the data
Handle time series data.
Not Convinced?
Seeing is believing! You can enroll to my FREE on-demand course on Introduction to R Programming. This course is created out of my Youtube video series on R programming. These videos generated more than 5K views in short span of time. This course contributes to 25% of the content that I will offer you in my paid course.
Here is a sneak peak into course notes and session exercise document.
Who is this course for?
This course is designed for absolute beginners who want to learn R. Data Analysts, Students oranyone working with the data will benefit from this course. The course will also build solid foundation in R for your data science career.
What are course pre-requisites?
None. This course assumes that you have no previous experience of either R or RStudio and no background in programming or using command line driven software. Thus this course will be a useful introduction to anyone who wants to learn how to use R.
What is not included in the course?
This course is designed for absolute beginners. Thus advance concepts of R programming language such as parallel computing, internal R architecture, error handling, and Object oriented programming concepts are not discussed.
Moreover, this course is not intended for software development in R. The course will also not delve into details of any particular R package. We may discuss simple statistical concepts, but statistics and machine learning topics are outside the scope of this course.
Language of Instructions:
English/ Hindi/ Marathi
Course Fee:
INR 3,500 /-
(You can pay the course fees after attending the first live session. The bank details/QR codes shall be shared with you after first live session)
There’s zero risk, because I offer a 30-day money back guarantee. What have you got to lose?
Enroll for the Course
Meet the Instructor
