(All course fees are in USD)

Course Description

The Data Science with R programming Online Course covers data exploration, data visualization, predictive analytics, and descriptive analytics techniques with the R language. You will learn about R packages, how to import and export data in R, data structures in R, various statistical concepts, cluster analysis, and forecasting.

Offered in Partnership with

Simplilearn

Course Delivery

Total applied learning: 64 hours

Benefits

• 60+ hours of blended learning
• 10 real-life industry projects
• Online virtual classes by industry experts

Skills to be Learned

• Business analytics
• R programming and its packages
• Data structures and data visualization
• Apply functions and DPLYR function
• Graphics in R for data visualization
• Hypothesis testing
• Apriori algorithm
• Kmeans and DBSCAN clustering

Award upon Successful Completion

Data Science with R Language Certification Training “Certificate of Achievement”

Awarding Organisation

Simplilearn

Learning Outcomes

• Gain a foundational understanding of business analytics
• Learn how to install R, RStudio, workspace setup, and learn about the various R packages
• Master R programming and understand how various statements are executed in R
• Gain an in-depth understanding of data structure used in R and learn to import/export data in R
• Define, understand and use the various apply functions and DPLYR functions
• Understand and use the various graphics in R for data visualization
• Gain a basic understanding of various statistical concepts
• Understand and use the hypothesis testing method to drive business decisions
• Understand and use linear and non-linear regression models, and classification
  techniques for data analysis
• Learn and use the various association rules with the Apriori algorithm
• Learn and use clustering methods including k-means, DBSCAN, and hierarchical clustering

Assessments

Project 1 – Products rating prediction for Amazon

Project 4 – Attrition Analysis for IBM

IBM, a leading US-based IT company, wants to identify the factors that influence employee attrition by building a logistics regression model that can help predict employee churn.

Who Should Enrol

We recommend this data science training particularly for:

• IT professionals
• Analytics professionals
• Software developers
• Anyone interested in R programming

Prerequisites

Learners need to possess an undergraduate degree, a high school diploma, or senior high school students.

Course Overview

Lesson 0 – Course Introduction

Lesson 1 – Introduction to Business Analytics

Lesson 2 – Introduction to R Programming

Lesson 3 – Data Structures

Lesson 4 – Data Visualization

Lesson 5 – Statistics for Data Science-I

Lesson 6 – Statistics for Data Science-II

Lesson 7 – Regression Analysis

Lesson 8 – Classification

Lesson 9 – Clustering

Lesson 10 – Association

Accessible Period of Course

1 year from date of enrolment

Customer reviews

Sasa Stevanovic

Member of the Network on Institutional Investors and Long-term Investment

Great experience with the provider, enjoyed learning, very helpful application, and staff support. Good start for mastering R, SAS, and Excel.

Amani Alawneh

The course was delivered successfully. It was very punctual, organized, and thorough. Overall, it was good.

Rodney Swann

Senior Facility Manager at CBRE

My instructor is obviously a Pro at what she does. I wish I had someone around like her to mentor me when I was younger. Some of the technical aspects of the course are a little challenging, but the concepts for doing what is being taught is becoming clear to me. I hope this will make all the difference as I delve into the coursework even more.

The course helped me to improve my skill set and gain the confidence to handle the role of an analyst. I had a break in my career due to immigration policies and had utilized the time to learn new skills, which helped me get a new job faster.

*Note: We reserve the right to revise/change any of the course content &/or instructor at our sole & absolute discretion, without prior notice to learner.