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Data Science with R Course Overview
The Data Science with R programming training in Lucknow 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.
R Certification Training Key Features
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- 64 hours of Applied Learning
- 10 real-life industry projects
- Dedicated mentoring session from industry experts
- Lifetime access to self-paced learning
Skills Covered
- 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
Benefits
The Big Data Analytics market is expected to reach $40.6 billion by 2023, at a growth rate of 29.7-percent. Randstad reports that pay hikes in the analytics industry are 50-percent higher than the IT industry. Learning R can help you begin a career in data science.
- Designation
- Annual Salary
- Hiring Companies
- Annual SalarySource: GlassdoorHiring CompaniesSource: Indeed
- Annual SalarySource: GlassdoorHiring CompaniesSource: Indeed
R Certification Course Curriculum
Eligibility
Pre-requisites
Course Content
Data Science with R Programming
PreviewLesson 00 - Course Introduction
01:31PreviewCourse Introduction
01:31
Lesson 01 - Introduction to Business Analytics
21:06Preview1.001 Overview
00:441.002 Business Decisions and Analytics
04:331.003 Types of Business Analytics
03:531.004 Applications of Business Analytics
08:571.005 Data Science Overview
01:291.006 Conclusion
01:30Knowledge Check
Lesson 02 - Introduction to R Programming
26:35Preview2.001 Overview
00:312.002 Importance of R
05:202.003 Data Types and Variables in R
02:142.004 Operators in R
04:392.005 Conditional Statements in R
02:452.006 Loops in R
05:072.007 R script
01:442.008 Functions in R
02:582.009 Conclusion
01:17Knowledge Check
Lesson 03 - Data Structures
50:57Preview3.001 Overview
01:043.002 Identifying Data Structures
13:143.003 Demo Identifying Data Structures
14:053.004 Assigning Values to Data Structures
04:513.005 Data Manipulation
09:233.006 Demo Assigning values and applying functions
07:463.007 Conclusion
00:34Knowledge Check
Lesson 04 - Data Visualization
29:40Preview4.001 Overview
00:294.002 Introduction to Data Visualization
03:034.003 Data Visualization using Graphics in R
18:504.004 ggplot2
05:144.005 File Formats of Graphic Outputs
01:084.006 Conclusion
00:56Knowledge Check
Lesson 05 - Statistics for Data Science-I
14:105.001 Overview
00:215.002 Introduction to Hypothesis
02:065.003 Types of Hypothesis
03:135.004 Data Sampling
02:485.005 Confidence and Significance Levels
04:335.006 Conclusion
01:09Knowledge Check
Lesson 06 - Statistics for Data Science-II
29:55Preview6.001 Overview
00:286.002 Hypothesis Test
00:476.003 Parametric Test
14:366.004 Non-Parametric Test
08:316.005 Hypothesis Tests about Population Means
02:096.006 Hypothesis Tests about Population Variance
00:456.007 Hypothesis Tests about Population Proportions
01:116.008 Conclusion
01:28Knowledge Check
Lesson 07 - Regression Analysis
45:04Preview7.001 Overview
00:267.002 Introduction to Regression Analysis
01:117.003 Types of Regression Analysis Models
01:387.004 Linear Regression
08:597.005 Demo Simple Linear Regression
07:297.006 Non-Linear Regression
03:497.007 Demo Regression Analysis with Multiple Variables
13:297.008 Cross Validation
01:487.009 Non-Linear to Linear Models
02:067.010 Principal Component Analysis
02:457.011 Factor Analysis
00:267.012 Conclusion
00:58Knowledge Check
Lesson 08 - Classification
01:05:14Preview8.001 Overview
00:318.002 Classification and Its Types
04:248.003 Logistic Regression
03:358.004 Support Vector Machines
04:268.005 Demo Support Vector Machines
11:138.006 K-Nearest Neighbours
02:348.007 Naive Bayes Classifier
02:538.008 Demo Naive Bayes Classifier
06:158.009 Decision Tree Classification
09:478.010 Demo Decision Tree Classification
06:258.011 Random Forest Classification
02:018.012 Evaluating Classifier Models
06:048.013 Demo K-Fold Cross Validation
04:098.014 Conclusion
00:57Knowledge Check
Lesson 09 - Clustering
28:10Preview9.001 Overview
00:179.002 Introduction to Clustering
02:579.003 Clustering Methods
07:479.004 Demo K-means Clustering
11:159.005 Demo Hierarchical Clustering
05:029.006 Conclusion
00:52Knowledge Check
Lesson 10 - Association
23:1310.001 Overview
00:1510.002 Association Rule
06:2010.003 Apriori Algorithm
05:1910.004 Demo Apriori Algorithm
10:3710.005 Conclusion
00:42Knowledge Check
- Free Course
Statistics Essential for Data Science
PreviewLesson 1 Introduction
02:55Preview1.1 Introduction
02:55
Lesson 2 Sample or population data
03:56Preview2.1 Sample or population data
03:56
Lesson 3 The fundamentals of descriptive statistics
21:18Preview3.1 The fundamentals of descriptive statistics
03:183.2 Levels of measurement
02:573.3 Categorical variables. Visualization techniques for categorical variables
04:063.4 Numerical variables. Using a frequency distribution table
03:243.5 Histogram charts
02:273.6 Cross tables and scatter plots
05:06
Lesson 4 Measures of central tendency, asymmetry, and variability
25:17Preview4.1 Measures of central tendency, asymmetry, and variability
04:244.2 Measuring skewness
02:434.3 Measuring how data is spread out calculating variance
05:584.4 Standard deviation and coefficient of variation
04:544.5 Calculating and understanding covariance
03:314.6 The correlation coefficient
03:47
Lesson 5 Practical example descriptive statistics
14:30Preview5.1 Practical example descriptive statistics
14:30
Lesson 6 Distributions
16:176.1 Distributions
01:026.2 What is a distribution
03:406.3 The Normal distribution
03:456.4 The standard normal distribution
02:516.5 Understanding the central limit theorem
03:406.6 Standard error
01:19
Lesson 7 Estimators and Estimates
23:36Preview7.1 Estimators and Estimates
02:367.2 Confidence intervals - an invaluable tool for decision making
06:317.3 Calculating confidence intervals within a population with a known variance
02:307.4 Student’s T distribution
03:147.5 Calculating confidence intervals within a population with an unknown variance
04:077.6 What is a margin of error and why is it important in Statistics
04:38
Lesson 8 Confidence intervals advanced topics
14:27Preview8.1 Confidence intervals advanced topics
04:478.2 Calculating confidence intervals for two means with independent samples (part One)
04:368.3 Calculating confidence intervals for two means with independent samples (part two)
03:408.4 Calculating confidence intervals for two means with independent samples (part three)
01:24
Lesson 9 Practical example inferential statistics
09:379.1 Practical example inferential statistics
09:37
Lesson 10 Hypothesis testing Introduction
12:3610.1 Hypothesis testing Introduction
04:5610.2 Establishing a rejection region and a significance level
04:2010.3 Type I error vs Type II error
03:20
Lesson 11 Hypothesis testing Let's start testing!
26:39Preview11.1 Hypothesis testing Let's start testing!
06:0711.2 What is the p-value and why is it one of the most useful tool for statisticians
03:5511.3 Test for the mean. Population variance unknown
04:2611.4 Test for the mean. Dependent samples
04:4511.5 Test for the mean. Independent samples (Part One)
03:3811.6 Test for the mean. Independent samples (Part Two)
03:48
Lesson 12 Practical example hypothesis testing
06:3112.1 Practical example hypothesis testing
06:31
Lesson 13 The fundamentals of regression analysis
18:32Preview13.1 The fundamentals of regression analysis
01:0213.2 Correlation and causation
04:0613.3 The linear regression model made easy
05:0213.4 What is the difference between correlation and regression
01:2813.5 A geometrical representation of the linear regression model
01:1813.6 A practical example - Reinforced learning
05:36
Lesson 14 Subtleties of regression analysis
23:25Preview14.1 Subtleties of regression analysis
02:0414.2 What is Rsquared and how does it help us
05:0014.3 The ordinary least squares setting and its practical applications
02:0814.4 Studying regression tables
04:3414.5 The multiple linear regression model
02:4214.6 Adjusted R-squared
04:5714.7 What does the F-statistic show us and why we need to understand it
02:00
Lesson 15 Assumptions for linear regression analysis
19:16Preview15.1 Assumptions for linear regression analysis
02:1115.2 Linearity
01:4015.3 No endogeneity
03:4315.4 Normality and homoscedasticity
05:0915.5 No autocorrelation
03:1115.6 No multicollinearity
03:22
Lesson 16 Dealing with categorical data
05:2016.1 Dealing with categorical data
05:20
Lesson 17 Practical example regression analysis
14:4217.1 Practical example regression analysis
14:42
Industry Project
- Project 1
Products rating prediction for Amazon
Help Amazon, a US-based e-commerce company, improve its recommendation engine by predicting ratings for the non-rated products and adding them to recommendations accordingly.
- Project 2
Demand Forecasting for Walmart
Predict accurate sales for 45 Walmart stores, considering the impact of promotional markdown events. Check if macroeconomic factors have an impact on sales.
- Project 3
Improving customer experience for Comcast
Provide Comcast, a US-based global telecom company, key recommendations to improve customer experience by identifying and improving problem areas that lower customer satisfaction.
- 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.
Data Science with R Course Advisor
Ronald van Loon
Top 10 Big Data and Data Science Influencer, Director - AdversitementNamed by Onalytica as one of the three most influential people in Big Data, Ronald is also an author of a number of leading Big Data and Data Science websites, including Datafloq, Data Science Central, and The Guardian. He also regularly speaks at renowned events.
Data Science with R Exam & Certification
Who provides the certification?
After successful completion of the Data Science with R certification training in Lucknow, you will be awarded the course completion certificate from Simplilearn.
What do I need to do to unlock my Simplilearn certificate?
Online Classroom:- Attend one complete batch of Data Science with R certification training in Lucknow
- Complete 1 project
Online Self-Learning:- Complete 85% of the course.
- Complete 1 project
How long is the Data Science with R certificate from Simplilearn valid for?
The Data Science with R certificate from Simplilearn has lifelong validity.
How long does it take to complete the course in Lucknow?
It will take about 40 hours to complete the R programming online course successfully.
How many attempts do I have to pass the Data Science with R certification exam?
You have a maximum of three attempts to pass the Data Science with R certification exam. Simplilearn provides guidance and support for learners to help them pass the exam.
If I pass the Data Science with R certification exam, when and how do I receive my certificate?
Upon successful completion of the Data Science with R certification course in Lucknow and passing the exam, you will receive the certificate through our Learning Management System which you can download or share via email or Linkedin.
R Certification Training Reviews
Anubhav Ingole
Project Management Officer (PMO)/Business Analyst, NagpurMy instructor, Rajneesh, made the class very interactive. He explained each topic with real-life examples and analogies. I sincerely thank him for the effort he is putting into making a difference.
Sheetal Nagpal
DelhiMy trainer was very engaging and knowledgeable. I liked her way of teaching - sharing notes and providing hands-on practicals within the R training sessions. She also shared real-life examples, and a SWOT analysis during the stat test, which I thought was a fantastic idea. Project mentoring cleared all our doubts related to the project.
Amol B
BangaloreSimplilearn has designed the R programming course in a systematic manner. It has its own UI to code the programs. In fact, the algorithm and its applications have been done in the most logical way. Thanks, Simplilearn
Rohit Kumar
Consultant, DelhiI really loved the way Shubham elaborates the concepts, how he starts from the basics and then gradually picks up the pace.
Lavanya Krishnan
RePM consultant, BangaloreMy instructor Shilesh gave me a lot of hands-on training and made us use the R-platform in ways that were practical and useful. It was indeed a good course.
Puneeta C.
Student at Rajasthan Technical University, BangaloreSimplilearn is the best platform to provide Certification Courses on Data Scientist, and it's Projects and Assignments. They are amazing. Keep Learning. Thank You
Sabyasachi Guharoy
Solution Architect - Testing at Capgemini Technology Services India Pvt., BangaloreI enrolled in Simplilearn for an Online Self Learning course on Data Science with R Programming. The LMS interface is very user-friendly and the course material is lucid and easy to understand. I have enjoyed my learning experience with Simplilearn
Amol B
Associate Manager at Firepro Systems, BangaloreSimplilearn is the awesome learning platform. The courses are very well designed and the live classes have personal attention in terms resolving the doubts. Thanks Simplilearn.
Ashish Ranjan
Data Scientist at Accenture, PuneSimplilearn is a good platform for starting the data science knowledge. Data Science with R course has helped me to get a rise from a Business Analyst to Data Scientist.
Ajeya Kumar
Associate Director at IHS Markit, BangaloreThe trainer is excellent. Real-time experiences shared during training are very helpful. Overall I am very happy with the training.
Manish Beniwal
Advisor Reporting - Global Mobility at Rio Tinto, BangaloreI am a Data Analyst with 7 years of work experience, but I didn't have the chance to work with Statistics like I am in this course. It's a good course even for beginners. Overall, R training is very good. Thank you Simplilearn.
Nisar Pasha
Student at Miracle educational society, VishakhapatnamI have enrolled in Data science with R from Simplilearn. It was a great experience. Their lab access was really helpful. If you are looking for a bright future in your career, go ahead and enroll yourself in online certification training courses for professionals from Simplilearn.
Sachin Shelar
Project Lead at Capgemini, MumbaiI took the Data Science with R training from Simplilearn, and it's amazing. It certainly added value to my career. The trainer conducted the sessions very nicely and made the learning easy and interesting. Great job Simplilearn!
Ruchi Gupta
ILP Institute, BangaloreI loved the training material and the simulations tests provided by simplilearn. I loved the Flexipass, where you have the opportunity to attend same topic in multiple batches for better understanding."
Samir Swarup
Associate Software Architect at UST Global, BangaloreI have enrolled in Data Science with R Programming course at Simplilearn. I had a very good experience with them. Their response time is very fast and extremely helpful in resolving my issues. Highly recommended.
Why Online Bootcamp
- Develop skills for real career growthCutting-edge curriculum designed in guidance with industry and academia to develop job-ready skills
- Learn from experts active in their field, not out-of-touch trainersLeading practitioners who bring current best practices and case studies to sessions that fit into your work schedule.
- Learn by working on real-world problemsCapstone projects involving real world data sets with virtual labs for hands-on learning
- Structured guidance ensuring learning never stops24x7 Learning support from mentors and a community of like-minded peers to resolve any conceptual doubts
R Certification Training FAQs
What is R programming?
R is a programming language and free software developed in 1993, made up of a collection of libraries architectured especially for data science. As a tool, R is considered to be clear and accessible.
Why should I learn R programming?
Data Science is one of the popular career domains among professionals that offers high earning potential. It mostly comprises statistics and R is the bridging language of this domain and is widely used for data analysis. By learning R programming, you can enter the world of business analytics and data visualization. It is a must-have skill for all those aspiring to become a Data Scientist.
How do beginners learn R online?
Anyone who is looking to get started in IT or willing to further their IT career should consider learning R. We at Simplilearn have compiled an extensive content for Data Science beginners, along with supporting blogs and YouTube videos to help you understand the Data Science basics and importance of R in the dynamic field of data science.
Should I learn R or Python programming for a Data Science career?
R and Python are the top languages that professionals learn to start a career in Data Science. Both languages are powerful and have their own pros and cons. So, depending on which language is used for data science projects in your organization and what can help you in the long run, you can make a choice.
Simplilearn also provides Data Science with Python course which builds a strong foundation in data science and imparts all the valuable skills that employers look for in a data scientist.
Can I learn Data Science with R online?
Learning methodologies have evolved tremendously with the influx of new technology. These changes have increased the ease and efficiency of learning on your terms. Simplilearn's Data Science with R Certification training provides live classes and round-the-clock access to study materials with the help of our learning management system. Our extensive collection of blogs, tutorials, and YouTube videos will help you brush up on the concepts. Even when your class ends, we provide a 24/7 support system to help you with any questions, concerns, or difficulties you may have.
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