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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
29:404.001 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
14:10Preview5.001 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
23:13Preview10.001 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 01: Course Introduction
07:05Preview1.01 Course Introduction
05:191.02 What Will You Learn
01:46
Lesson 02: Introduction to Statistics
18:41Preview2.01 Learning Objectives
01:162.02 What Is Statistics
01:502.03 Why Statistics
02:062.04 Difference between Population and Sample
01:212.05 Different Types of Statistics
02:422.06 Importance of Statistical Concepts in Data Science
03:202.07 Application of Statistical Concepts in Business
02:112.08 Case Studies of Statistics Usage in Business
03:092.09 Recap
00:46
Lesson 03: Understanding the Data
17:29Preview3.01 Learning Objectives
01:123.02 Types of Data in Business Contexts
02:113.03 Data Categorization and Types of Data
03:133.03 Types of Data Collection
02:143.04 Types of Data
02:013.05 Structured vs. Unstructured Data
01:463.06 Sources of Data
02:173.07 Data Quality Issues
01:383.08 Recap
00:57
Lesson 04: Descriptive Statistics
32:03Preview4.01 Learning Objectives
01:264.02 Mathematical and Positional Averages
03:154.03 Measures of Central Tendancy: Part A
02:174.04 Measures of Central Tendancy: Part B
02:414.05 Measures of Dispersion
01:154.06 Range Outliers Quartiles Deviation
02:304.07 Mean Absolute Deviation (MAD) Standard Deviation Variance
02:524.08 Z Score and Empirical Rule
02:144.09 Coefficient of Variation and Its Application
02:064.10 Measures of Shapes
02:394.11 Summarizing Data
02:034.12 Recap
00:544.13 Case Study One: Descriptive Statistics
05:51
Lesson 05: Data Visualization
20:55Preview5.01 Learning Objectives
00:575.02 Data Visualization
02:155.03 Basic Charts
01:525.04 Advanced Charts
02:195.05 Interpretation of the Charts
02:575.06 Selecting the Appropriate Chart
02:255.07 Charts Do's and Dont's
02:475.08 Story Telling With Charts
01:295.09 Recap
00:505.10 Case Study Two: Data Visualization
03:04
Lesson 06: Probability
19:46Preview6.01 Learning Objectives
00:556.02 Introduction to Probability
03:106.03 Key Terms in Probability
02:256.04 Conditional Probability
02:116.05 Types of Events Independent and Dependent
02:566.06 Addition Theorem of Probability
01:586.07 Multiplication Theorem of Probability
02:086.08 Bayes Theorem
03:106.09 Recap
00:53
Lesson 07: Probability Distributions
22:29Preview7.01 Learning Objectives
00:527.02 Random Variable
02:217.03 Probability Distributions Discrete vs.Continuous: Part A
01:447.04 Probability Distributions Discrete vs.Continuous: Part B
01:457.05 Commonly Used Discrete Probability Distributions: Part A
03:187.06 Discrete Probability Distributions Poisson
03:167.07 Binomial by Poisson Theorem
01:377.08 Commonly Used Continuous Probability Distribution
03:227.09 Applicaton of Normal Distribution
02:497.10 Recap
01:25
Lesson 08: Sampling and Sampling Techniques
30:53Preview8.01 Learnning Objectives
00:518.02 Introduction to Sampling and Sampling Errors
03:058.03 Advantages and Disadvantages of Sampling
01:318.04 Probability Sampling Methods: Part A
02:328.05 Probability Sampling Methods: Part B
02:278.06 Non-Probability Sampling Methods: Part A
01:428.07 Non-Probability Sampling Methods: Part B
01:258.08 Uses of Probability Sampling and Non Probability Sampling
02:088.09 Sampling
01:088.10 Probability Distribution
02:538.11 Theorem Five Point One
00:528.12 Center Limit Theorem
02:148.13 Recap
01:078.14 Case Study Three: Sample and Sampling Technique
05:168.15 Spotlight
01:42
Lesson 09: Inferential Statistics
33:59Preview9.01 Learning Objectives
01:049.02 Hypothesis and Hypothesis Testing in Businesses
03:249.03 Null and Alternate Hypothesis
01:449.04 P Value
03:229.05 Levels of Significance
01:169.06 Type One and Two Errors
01:379.07 Z Test
02:249.08 Confidence Level Significance Level: Part A
02:529.09 Confidence Intervals: Part B
01:209.10 One Tail and Two Tail Tests
04:439.11 Notes to Remember for Null Hypothesis
01:029.12 Alternate Hypothesis
01:519.13 Recap
00:569.14 Case Study Four: Inferential Statistics
06:24Hypothesis Testing
Lesson 10: Application of Inferential Statistics
27:07Preview10.01 Learning Objectives
00:5010.02 Bivariate Analysis
02:0110.03 Selecting the Appropriate Test for EDA
02:2910.04 Parametric vs. Non-parametric Tests
01:5410.05 Test of Significance
01:3810.06 Z Test
04:1410.07 T Test
00:5410.08 Parametric Tests ANOVA
03:2610.09 Chi-Square Test
02:3110.10 Sign Test
01:5810.11 Kruskal Wallis Test
01:0410.12 Mann Whitney Wilcoxon Test
01:1810.13 Run Test for Randomness
01:5310.14 Recap
00:57
Lesson 11: Relation between Variables
18:08Preview11.01 Learning Objectives
01:0611.02 Correlation
01:5411.03 Karl Pearson's Coefficient of Correlation
02:3611.04 Karl Pearsons: Use Cases
01:3011.05 Spearmans Rank Correlation Coefficient
02:1411.06 Causation
01:4711.07 Example of Regression
02:2811.08 Coefficient of Determination
01:1211.09 Quantifying Quality
02:2911.10 Recap
00:52
Lesson 12: Application of Statistics in Business
17:25Preview12.01 Learning Objectives
00:5312.02 How to Use Statistics In Day to Day Business
03:2912.03 Example: How to Not Lie With Statistics
02:3412.04 How to Not Lie With Statistics
01:4912.05 Lying Through Visualizations
02:1512.06 Lying About Relationships
03:3112.07 Recap
01:0612.08 Spotlight
01:48
Lesson 13: Assisted Practice
11:47Assisted Practice: Problem Statement
02:10Assisted Practice: Solution
09:37
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 R programming 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 Data Science with R certification course. Its projects and assignments 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
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.
Debashis Sen
Researcher at S&P Capital IQ, BangaloreThe course material of the Data Science program was well designed for beginners. The presentations were precise and to the point. The mentors in the various sessions were helpful and kept close to the basics. The examples used mirror real life scenarios, hence are very useful. Finally, the members of the CD team were truly delightful!
Nisar Pasha
Student at Miracle educational society, VisakhapatnamI 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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