Data Science with R

Gain expertise in machine learning algorithms using R

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      Course Overview



Registration Deadline

August 3

Start Date

August 4

End Date

September 23

Cost

$199

Overview

In this course, students will learn beginner and intermediate levels of data science with R. Students are expected to complete 16 modules of course work over 8 weeks and 2 projects that demonstrate the skills accquired. Free pre-requisite online course work is available for those interested. After completing this course, students are expected to gain expertise in machine learning algorithms such as decision trees, cluster analysis and regression.


Prerequisites

There is no specific pre-requisite for Data Science with R. However, a basic understanding of statistics and R can be beneficial. The course is offered only in English language.


Schedule

Saturday & Sunday; 8 AM - 11 AM IST

Location

The course is delievered online.

Tuition

$199







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      Course Features



      Instructor-led Sessions

   48 Hours of Online Live Instructor-led Classes
   Weekend class: 16 sessions of 3 hours each
   Weekday class: 24 sessions of 2 hours each



      Case Studies

   Our student advisors will work with you to build a portfolio
   of projects to showcase your newly mastered skills. Projects
   include a mix of real world and simulated case studies



      24 x 7 Support

   Whether it is query related to the course or otherwise,
   we got your back. Our experts are availabe 24 X 7 to
   answer your queries



      Quiz & Assignments

   Each module of the course includes interactive tutorials,
   quiz, assignments and case studies that help sharpen your
   skills and aid in building your project portfolio



      Lifetime Access

   Our learning management system will give you lifetime
   access to all course related resources such as videos,
    slides, interactive tutorials, quiz and assignments



      Certification

   You have mastered the skills, built your portfolio. Earn a
   valued certificate and get help in crafting a professional
   CV as well as resources and guidance for interviews






Get in touch with Student Advisor for more information about the course



    +91-914-806-8802







      Course Curriculum


Learning Objectives - Get an introduction to and quick tour of R, RStudio and GitHub.


Topics:

  • A brief history of R
  • Introduction to the R ecosystem
  • Installation of R
  • Introduction to RStudio
  • Installation of RStudio
  • Quick tour of RStudio
  • Quick tour of R
  • Installing R Packages
  • Getting help in R
  • Introduction to Git
  • Introduction to GitHub
  • Quick tour of GitHub

Hands-On/Demo:

  • Creating new project in RStudio
  • Installing R packages
  • Configuration of Git, GitHub & Rstudio

Learning Objectives - Learn to read/import data into R and the grammar of data manipulation.


Topics:

  • Introduction to tidyverse
  • Read data from flat/delimited files
  • Read data from excel spreadsheets
  • Frequently faced issues when reading data
  • Introduction to tibbles
  • Introduction to dplyr package
  • Handling categorical data in R
  • Introduction to ggplot2

Hands-On/Demo:

  • Read data from flat/delimited files
  • Read data from excel spreadsheets
  • Wrangle data to compute web analytics metrics
  • Visualize data using ggplot2

Learning Objectives - Learn to handle date/time, strings in R and advanced data visualization.


Topics:

  • Work with date/time in R
  • Hacking strings with stringr
  • Writing readable code with pipes
  • Intermediate data visualization with ggplot2
  • Writing functions in R
  • Introduction to ggplot2

Hands-On/Demo:

  • Process transactions data
  • Extract info from ecommerce data
  • Visualize ecommerce KPI using ggplot2

Learning Objectives Get an introduction to descriptive statistics, statistical distributions and statistical inference.


Topics:

  • Measures of central tendency
  • Measures of variation
  • Measures of location
  • Frequency distribution and cross tables
  • Explore important statistical distibutions
  • Introduction to hypothesis testing

Hands-On/Demo:

  • Exploratory data analysis of ecommerce data
  • Visualize statistical distributions
  • Introduction to A/B Testing

Learning Objectives Get an introduction to linear and logistic regression.


Topics:

  • Introduction to simple linear regression
  • Introduction to multiple linear regression
  • Residual & collinearity diagnostics
  • Measures of influence & heteroskedasticity
  • Model fit assessment and variable selection procedures
  • Introduction to logistic regression
  • Bivariate analysis
  • Variable selection procedures
  • Model validation techniques

Hands-On/Demo:

  • Predict housing prices
  • Predict loan approval
  • Direct marketing campaign effectiveness

Learning Objectives Get an introduction to supervised and unsupervised learning techniques and implementation of decision trees and cluster analysis.


Topics:

  • Introduction to classification trees
  • Introduction to decision trees
  • Decision tree algorithms
  • Creating a decision tree
  • Confusion matrix
  • Introduction to clustering techniques
  • K-means clustering
  • Hierarchical clustering

Hands-On/Demo:

  • Implement decision tree model in R
  • Implement K-means clustering in R
  • Implement hierarchical clustering in R

Learning Objectives Get an introduction to text mining and sentimental analysis.


Topics:

  • Introduction to text mining
  • Word frequencies & word clouds
  • Term frequency & inverse document frequency
  • n-grams & correlations
  • Sentiment analysis

Hands-On/Demo:

  • Implement word cloud in R
  • Sentiment analysis of twitter data

      Course Description


This course will give you an introduction to data science using R. It will teach you the skills needed for data science. You will learn how to read data into R, clean the data, structure and transform it accordingly, explore the data using visualization and finally model it using different techniques. The course also covers version control, literate programming and reproducible research.

Data science with R course is designed to offer you an introduction to data science. The course offers:

  • Introduction to data science life cycle
  • In depth knowledge of most popular machine learning techniques
  • Supervised and unsupervised learning techniques
  • Real life case studies and simulated projects to sharpen your skill sets
  • Assistance in creating a portfolio which will allow you to showcase your newly accquired skills
Data is pervasive. Data is everywhere. As companies try to make sense of the humungous amounts of data generated daily, it is incumbent upon employees to use this data to generate insights that will lead to better outcomes for organisations and consumers. Regardless of whether you work for private or government organizations, irrespective of your field of work, today you are expected to be well versed in handling structured and unstructured data. This course gives you the skill sets required to navigate a world overflowing with data and to improve your career prospects.

Data science with R course is designed to offer you an introduction to data science. This course will introduce you to:

  • Data science work flow
  • Reading data from multiple sources in mutiple formats
  • Cleaning and restructuring data using the grammar of data manipulation
  • Visualizing data using the grammar of graphics
  • Exploratory data analysis and statistical distributions
  • Implementing supervised and unsupervised macbine learning techniques
  • Version control with GitHub and literate programming

Our course is designed to cater to the needs of a wide range of audience. It is best suited for:

  • Business Analysts
  • Analytics Managers
  • Developers
  • Information Architects
  • MBA & Engineering Students
  • Pure & social science researchers
  • Academics

There is no specific pre-requisite for Data Science with R. However, a basic understanding of statistics and R can be beneficial. The course is offered only in English language. We offer several free self paced courses when you enroll for this course.


      Projects


Your system should have minimum 4GB RAM or above.

All assignments will be should be completed using RStudio IDE and shared on a GitHub repository. The step-wise installation guides for setting up the environment on various operating systems are present in the LMS. In case you come across any doubt, the 24*7 support team will promptly assist you.

Towards the end of the course, you will be working on a live project. You can choose any of the following as your project work:

  • Sentiment Analysis of Twitter Data
  • Market Basket Analysis
  • Customer Segmentation for ecommerce business
  • Bank tele marketing for product subscription
  • Loan default prediction
  • Predicting employee attrition
  • Customer churn analysis

      FAQ's


While we do not promise jobs, we work with our students in designing resumes and sharing it with our corporate clients and hiring partners. Students who dedicate time to build a portfolio of projects have found success in getting jobs.

Yes, we offer a 20% discount for students. You need to use the email provided by your university/college or after the enrolment, you can send us a copy of your identity card from your university/college email id. Post verification, we refund 20% of the course fee.

There is no need to worry about missing classes as we offer you the following choices:

  • View the recorded session of the class in the LMS
  • Attend the missed session in the next batch

The live classes are held on weekends and we expect the students to spend another 10 hours during the rest of the week to keep up with the course. Since it might not be possible to spend such time week after week for 8 weeks, we offer post course support to ensure that you can complete the projects and your learning outcome is maximized.

Once you make the payment, you get immediate access to course materials in the LMS. It will include recordings from the previous batch, installation guides and suggested readings. You also get access to whole set of free self paced courses.

You can use any of the following options for doubt clearance:

  • Reach out to the instructor after the live class
  • Reach out to student advisor any time
The instructors at Rsquared Academy are experts who have spent considerable time using R for data science. Some of our instructors have published R packages that are used world wide for teaching graduate and undergraduate statistics and in corporates.

We have limited number of participants in a live session to maintain the Quality Standards. So, unfortunately participation in a live class without enrolment is not possible. However, you can go through the sample class recording and it would give you a clear insight about how are the classes conducted, quality of instructors and the level of interaction in the class.

We have a no questions asked refund policy upto 2 weeks after the batch starts.
You can Call us at +91-914-806-8802 OR Email us at sales@rsquaredacademy.com . We shall be glad to assist you.

      Certification



certificate