Data Science with R Programming Training in San Francisco Bay Area

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R Certification Course Overview

The Data Science with R programming certification training 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

100% Money Back Guarantee

No questions asked refund*

At Simplilearn, we value the trust of our patrons immensely. But, if you feel that this R certification course does not meet your expectations, we offer a 7-day money-back guarantee. Just send us a refund request via email within 7 days of purchase and we will refund 100% of your payment, no questions asked!

64 hours of Applied Learning

Dedicated mentoring session from industry experts

10 real-life industry projects

Lifetime access to self-paced learning

64 hours of Applied Learning

10 real-life industry projects

Dedicated mentoring session from industry experts

Lifetime access to self-paced learning

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

Data structures and data visualization

Graphics in R for data visualization

Apriori algorithm

R programming and its packages

Apply functions and DPLYR function

Hypothesis testing

kmeans and DBSCAN clustering

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

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

Take the first step to your goals

Lifetime access to self-paced e learning content

Benefits

The Data Science with R Programming Course in San Francisco prepares you for a career in Big Data Analytics, a market growing 29.7% yearly to $40.6 billion by 2023, with analysts’ pay raises 50% higher than in IT functions. Data Science with R Programming training in San Francisco can accelerate your career.

Designation
Annual Salary
Hiring Companies

Data Analyst
Annual Salary
$43KMin
$62KAverage
$95KMax
Source: Glassdoor
Hiring Companies
Source: Indeed
Data Scientist
Annual Salary
$83KMin
$113KAverage
$154KMax
Source: Glassdoor
Hiring Companies
Source: Indeed

Training Options

Self Paced Learning

Lifetime access to high-quality self-paced eLearning content curated by industry experts
7 hands-on R projects to perfect the skills learned
Simulation test papers for self-assessment
Lab access to practice live during sessions
24x7 learner assistance and support

$799

Corporate Training

Upskill or reskill your teams

Flexible pricing & billing options
Private cohorts available
Training progress dashboards
Skills assessment & benchmarking
Platform integration capabilities
Dedicated customer success manager

R Certification Course Curriculum

Eligibility

Data Science with R Programming training in San Francisco is great for aspiring data scientists wishing to build a career in Data Science, including IT professionals or software developers who want to make a career switch into Data Analytics. Data Science with R Programming training in San Francisco is also ideal for professionals working in data and business analysis and experienced professionals interested in harnessing the benefits of Data Science in their fields.

Pre-requisites

There are no prerequisites for this Data Science with R Programming training in San Francisco. If you are just a beginner in the Data Science field, the Data Science with R Programming course in San Francisco is one of the best courses to begin your career journey.

Course Content

Data Science with R Programming
Preview
- Lesson 00 - Course Introduction
  08:36Preview
  - Course Introduction
    01:31
  - Accessing Practice Lab
    07:05
- Lesson 01 - Introduction to Business Analytics
  21:06Preview
  - 1.001 Overview
    00:44
  - 1.002 Business Decisions and Analytics
    04:33
  - 1.003 Types of Business Analytics
    03:53
  - 1.004 Applications of Business Analytics
    08:57
  - 1.005 Data Science Overview
    01:29
  - 1.006 Conclusion
    01:30
  - Knowledge Check
- Lesson 02 - Introduction to R Programming
  26:35Preview
  - 2.001 Overview
    00:31
  - 2.002 Importance of R
    05:20
  - 2.003 Data Types and Variables in R
    02:14
  - 2.004 Operators in R
    04:39
  - 2.005 Conditional Statements in R
    02:45
  - 2.006 Loops in R
    05:07
  - 2.007 R script
    01:44
  - 2.008 Functions in R
    02:58
  - 2.009 Conclusion
    01:17
  - Knowledge Check
- Lesson 03 - Data Structures
  50:57Preview
  - 3.001 Overview
    01:04
  - 3.002 Identifying Data Structures
    13:14
  - 3.003 Demo Identifying Data Structures
    14:05
  - 3.004 Assigning Values to Data Structures
    04:51
  - 3.005 Data Manipulation
    09:23
  - 3.006 Demo Assigning values and applying functions
    07:46
  - 3.007 Conclusion
    00:34
  - Knowledge Check
- Lesson 04 - Data Visualization
  26:25
  - 4.001 Overview
    00:29
  - 4.002 Introduction to Data Visualization
    03:03
  - 4.003 Data Visualization using Graphics in R
    15:35
  - 4.004 ggplot2
    05:14
  - 4.005 File Formats of Graphic Outputs
    01:08
  - 4.006 Conclusion
    00:56
  - Knowledge Check
- Lesson 05 - Statistics for Data Science-I
  14:10Preview
  - 5.001 Overview
    00:21
  - 5.002 Introduction to Hypothesis
    02:06
  - 5.003 Types of Hypothesis
    03:13
  - 5.004 Data Sampling
    02:48
  - 5.005 Confidence and Significance Levels
    04:33
  - 5.006 Conclusion
    01:09
  - Knowledge Check
- Lesson 06 - Statistics for Data Science-II
  29:55Preview
  - 6.001 Overview
    00:28
  - 6.002 Hypothesis Test
    00:47
  - 6.003 Parametric Test
    14:36
  - 6.004 Non-Parametric Test
    08:31
  - 6.005 Hypothesis Tests about Population Means
    02:09
  - 6.006 Hypothesis Tests about Population Variance
    00:45
  - 6.007 Hypothesis Tests about Population Proportions
    01:11
  - 6.008 Conclusion
    01:28
  - Knowledge Check
- Lesson 07 - Regression Analysis
  45:04Preview
  - 7.001 Overview
    00:26
  - 7.002 Introduction to Regression Analysis
    01:11
  - 7.003 Types of Regression Analysis Models
    01:38
  - 7.004 Linear Regression
    08:59
  - 7.005 Demo Simple Linear Regression
    07:29
  - 7.006 Non-Linear Regression
    03:49
  - 7.007 Demo Regression Analysis with Multiple Variables
    13:29
  - 7.008 Cross Validation
    01:48
  - 7.009 Non-Linear to Linear Models
    02:06
  - 7.010 Principal Component Analysis
    02:45
  - 7.011 Factor Analysis
    00:26
  - 7.012 Conclusion
    00:58
  - Knowledge Check
- Lesson 08 - Classification
  01:05:14Preview
  - 8.001 Overview
    00:31
  - 8.002 Classification and Its Types
    04:24
  - 8.003 Logistic Regression
    03:35
  - 8.004 Support Vector Machines
    04:26
  - 8.005 Demo Support Vector Machines
    11:13
  - 8.006 K-Nearest Neighbours
    02:34
  - 8.007 Naive Bayes Classifier
    02:53
  - 8.008 Demo Naive Bayes Classifier
    06:15
  - 8.009 Decision Tree Classification
    09:47
  - 8.010 Demo Decision Tree Classification
    06:25
  - 8.011 Random Forest Classification
    02:01
  - 8.012 Evaluating Classifier Models
    06:04
  - 8.013 Demo K-Fold Cross Validation
    04:09
  - 8.014 Conclusion
    00:57
  - Knowledge Check
- Lesson 09 - Clustering
  28:10Preview
  - 9.001 Overview
    00:17
  - 9.002 Introduction to Clustering
    02:57
  - 9.003 Clustering Methods
    07:47
  - 9.004 Demo K-means Clustering
    11:15
  - 9.005 Demo Hierarchical Clustering
    05:02
  - 9.006 Conclusion
    00:52
  - Knowledge Check
- Lesson 10 - Association
  23:13Preview
  - 10.001 Overview
    00:15
  - 10.002 Association Rule
    06:20
  - 10.003 Apriori Algorithm
    05:19
  - 10.004 Demo Apriori Algorithm
    10:37
  - 10.005 Conclusion
    00:42
  - Knowledge Check

Free Course
Math Refresher
Preview
- Lesson 01: Course Introduction
  06:23Preview
  - 1.01 About Simplilearn
    00:28
  - 1.02 Introduction to Mathematics
    01:18
  - 1.03 Types of Mathematics
    02:39
  - 1.04 Applications of Math in Data Industry
    01:17
  - 1.05 Learning Path
    00:25
  - 1.06 Course Components
    00:16
- Lesson 02: Probability and Statistics
  32:38Preview
  - 2.01 Learning Objectives
    00:29
  - 2.02 Basics of Statistics and Probability
    03:08
  - 2.03 Introduction to Descriptive Statistics
    02:12
  - 2.04 Measures of Central Tendencies
    04:50
  - 2.05 Measures of Asymmetry
    02:24
  - 2.06 Measures of Variability
    04:55
  - 2.07 Measures of Relationship
    05:22
  - 2.08 Introduction to Probability
    08:36
  - 2.09 Key Takeaways
    00:42
  - 2.10 Knowledge check
- Lesson 03: Coordinate Geometry
  06:31
  - 3.01 Learning Objectives
    00:35
  - 3.02 Introduction to Coordinate Geometry
    03:16
  - 3.03 Coordinate Geometry Formulas
    01:51
  - 3.04 Key Takeaways
    00:49
  - 3.05 Knowledge Check
- Lesson 04: Linear Algebra
  29:53Preview
  - 4.01 Learning Objectives
    00:29
  - 4.02 Introduction to Linear Algebra
    03:21
  - 4.03 Forms of Linear Equation
    05:21
  - 4.04 Solving a Linear Equation
    05:21
  - 4.05 Introduction to Matrices
    02:05
  - 4.06 Matrix Operations
    07:07
  - 4.07 Introduction to Vectors
    01:00
  - 4.08 Types and Properties of Vectors
    01:52
  - 4.09 Vector Operations
    02:39
  - 4.10 Key Takeaways
    00:38
  - 4.11 Knowledge Check
- Lesson 05: Eigenvalues Eigenvectors and Eigendecomposition
  08:56Preview
  - 5.01 Learning Objectives
    00:29
  - 5.02 Eigenvalues
    01:19
  - 5.03 Eigenvectors
    04:09
  - 5.04 Eigendecomposition
    02:21
  - 5.05 Key Takeaways
    00:38
  - 5.06 Knowledge Check
- Lesson 06: Introduction to Calculus
  09:47Preview
  - 6.01 Learning Objectives
    00:30
  - 6.02 Basics of Calculus
    01:20
  - 6.03 Differential Calculus
    03:01
  - 6.04 Differential Formulas
    01:01
  - 6.05 Integral Calculus
    02:33
  - 6.06 Integration Formulas
    00:47
  - 6.07 Key Takeaways
    00:35
  - 6.08 Knowledge Check

Free Course
Statistics Essential for Data Science
Preview
- Lesson 01: Course Introduction
  07:05Preview
  - 1.01 Course Introduction
    05:19
  - 1.02 What Will You Learn
    01:46
- Lesson 02: Introduction to Statistics
  25:49Preview
  - 2.01 Learning Objectives
    01:16
  - 2.02 What Is Statistics
    01:50
  - 2.03 Why Statistics
    02:06
  - 2.04 Difference between Population and Sample
    01:20
  - 2.05 Different Types of Statistics
    02:42
  - 2.06 Importance of Statistical Concepts in Data Science
    03:20
  - 2.07 Application of Statistical Concepts in Business
    02:11
  - 2.08 Case Studies of Statistics Usage in Business
    03:09
  - 2.09 Applications of Statistics in Business: Time Series Forecasting
    03:50
  - 2.10 Applications of Statistics in Business Sales Forecasting
    03:19
  - 2.11 Recap
    00:46
- Lesson 03: Understanding the Data
  17:29Preview
  - 3.01 Learning Objectives
    01:12
  - 3.02 Types of Data in Business Contexts
    02:11
  - 3.03 Data Categorization and Types of Data
    03:13
  - 3.03 Types of Data Collection
    02:14
  - 3.04 Types of Data
    02:01
  - 3.05 Structured vs. Unstructured Data
    01:46
  - 3.06 Sources of Data
    02:17
  - 3.07 Data Quality Issues
    01:38
  - 3.08 Recap
    00:57
- Lesson 04: Descriptive Statistics
  34:51Preview
  - 4.01 Learning Objectives
    01:26
  - 4.02 Descriptive Statistics
    02:03
  - 4.03 Mathematical and Positional Averages
    03:15
  - 4.04 Measures of Central Tendancy: Part A
    02:17
  - 4.05 Measures of Central Tendancy: Part B
    02:41
  - 4.06 Measures of Dispersion
    01:15
  - 4.07 Range Outliers Quartiles Deviation
    02:30
  - 4.08 Mean Absolute Deviation (MAD) Standard Deviation Variance
    03:37
  - 4.09 Z Score and Empirical Rule
    02:14
  - 4.10 Coefficient of Variation and Its Application
    02:06
  - 4.11 Measures of Shape
    02:39
  - 4.12 Summarizing Data
    02:03
  - 4.13 Recap
    00:54
  - 4.14 Case Study One: Descriptive Statistics
    05:51
- Lesson 05: Data Visualization
  23:36Preview
  - 5.01 Learning Objectives
    00:57
  - 5.02 Data Visualization
    02:15
  - 5.03 Basic Charts
    01:52
  - 5.04 Advanced Charts
    02:19
  - 5.05 Interpretation of the Charts
    02:57
  - 5.06 Selecting the Appropriate Chart
    02:25
  - 5.07 Charts Do's and Dont's
    02:47
  - 5.08 Story Telling With Charts
    01:29
  - 5.09 Data Visualization: Example
    02:41
  - 5.10 Recap
    00:50
  - 5.11 Case Study Two: Data Visualization
    03:04
- Lesson 06: Probability
  21:51Preview
  - 6.01 Learning Objectives
    00:55
  - 6.02 Introduction to Probability
    03:10
  - 6.03 Probability Example
    02:02
  - 6.04 Key Terms in Probability
    02:25
  - 6.05 Conditional Probability
    02:11
  - 6.06 Types of Events: Independent and Dependent
    02:59
  - 6.07 Addition Theorem of Probability
    01:58
  - 6.08 Multiplication Theorem of Probability
    02:08
  - 6.09 Bayes Theorem
    03:10
  - 6.10 Recap
    00:53
- Lesson 07: Probability Distributions
  24:45Preview
  - 7.01 Learning Objectives
    00:52
  - 7.02 Probability Distribution
    01:25
  - 7.03 Random Variable
    02:21
  - 7.04 Probability Distributions Discrete vs.Continuous: Part A
    01:44
  - 7.05 Probability Distributions Discrete vs.Continuous: Part B
    01:45
  - 7.06 Commonly Used Discrete Probability Distributions: Part A
    03:18
  - 7.07 Discrete Probability Distributions: Poisson
    03:16
  - 7.08 Binomial by Poisson Theorem
    02:28
  - 7.09 Commonly Used Continuous Probability Distribution
    03:22
  - 7.10 Application of Normal Distribution
    02:49
  - 7.11 Recap
    01:25
- Lesson 08: Sampling and Sampling Techniques
  36:45Preview
  - 8.01 Learnning Objectives
    00:51
  - 8.02 Introduction to Sampling and Sampling Errors
    03:05
  - 8.03 Advantages and Disadvantages of Sampling
    01:31
  - 8.04 Probability Sampling Methods: Part A
    02:32
  - 8.05 Probability Sampling Methods: Part B
    02:27
  - 8.06 Non-Probability Sampling Methods: Part A
    01:42
  - 8.07 Non-Probability Sampling Methods: Part B
    01:25
  - 8.08 Uses of Probability Sampling and Non-Probability Sampling
    02:08
  - 8.09 Sampling
    01:08
  - 8.10 Probability Distribution
    02:53
  - 8.11 Theorem Five Point One
    00:52
  - 8.12 Center Limit Theorem
    02:14
  - 8.13 Sampling Stratified: Sampling Example
    04:35
  - 8.14 Probability Sampling: Example
    01:17
  - 8.15 Recap
    01:07
  - 8.16 Case Study Three: Sample and Sampling Techniques
    05:16
  - 8.17 Spotlight
    01:42
- Lesson 09: Inferential Statistics
  37:08Preview
  - 9.01 Learning Objectives
    01:04
  - 9.02 Inferential Statistics
    03:09
  - 9.03 Hypothesis and Hypothesis Testing in Businesses
    03:24
  - 9.04 Null and Alternate Hypothesis
    01:44
  - 9.05 P Value
    03:22
  - 9.06 Levels of Significance
    01:16
  - 9.07 Type One and Two Errors
    01:37
  - 9.08 Z Test
    02:24
  - 9.09 Confidence Intervals and Percentage Significance Level: Part A
    02:52
  - 9.10 Confidence Intervals: Part B
    01:20
  - 9.11 One Tail and Two Tail Tests
    04:43
  - 9.12 Notes to Remember for Null Hypothesis
    01:02
  - 9.13 Alternate Hypothesis
    01:51
  - 9.14 Recap
    00:56
  - 9.15 Case Study 4: Inferential Statistics
    06:24
  - Hypothesis Testing
- Lesson 10: Application of Inferential Statistics
  27:20Preview
  - 10.01 Learning Objectives
    00:50
  - 10.02 Bivariate Analysis
    02:01
  - 10.03 Selecting the Appropriate Test for EDA
    02:29
  - 10.04 Parametric vs. Non-Parametric Tests
    01:54
  - 10.05 Test of Significance
    01:38
  - 10.06 Z Test
    04:27
  - 10.07 T Test
    00:54
  - 10.08 Parametric Tests ANOVA
    03:26
  - 10.09 Chi-Square Test
    02:31
  - 10.10 Sign Test
    01:58
  - 10.11 Kruskal Wallis Test
    01:04
  - 10.12 Mann Whitney Wilcoxon Test
    01:18
  - 10.13 Run Test for Randomness
    01:53
  - 10.14 Recap
    00:57
- Lesson 11: Relation between Variables
  20:07Preview
  - 11.01 Learning Objectives
    01:06
  - 11.02 Correlation
    01:54
  - 11.03 Karl Pearson's Coefficient of Correlation
    02:36
  - 11.04 Karl Pearsons: Use Cases
    01:30
  - 11.05 Correlation Example
    01:59
  - 11.06 Spearmans Rank Correlation Coefficient
    02:14
  - 11.07 Causation
    01:47
  - 11.08 Example of Regression
    02:28
  - 11.09 Coefficient of Determination
    01:12
  - 11.10 Quantifying Quality
    02:29
  - 11.11 Recap
    00:52
- Lesson 12: Application of Statistics in Business
  17:25Preview
  - 12.01 Learning Objectives
    00:53
  - 12.02 How to Use Statistics In Day to Day Business
    03:29
  - 12.03 Example: How to Not Lie With Statistics
    02:34
  - 12.04 How to Not Lie With Statistics
    01:49
  - 12.05 Lying Through Visualizations
    02:15
  - 12.06 Lying About Relationships
    03:31
  - 12.07 Recap
    01:06
  - 12.08 Spotlight
    01:48
- Lesson 13: Assisted Practice
  11:47Preview
  - Assisted Practice: Problem Statement
    02:10
  - Assisted Practice: Solution
    09:37

Programme Syllabus

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.

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Data Science with R Exam & Certification

Who provides the certification?
After you successfully complete the Data Science with R Programming course in San Francisco, you will be awarded a course completion certificate from Simplilearn.
What do I need to do to unlock my Simplilearn certificate?
Online Classroom:
- You must attend one complete batch of Data Science with R Programming training in San Francisco.
- You must complete one project
Online Self-Learning:
- You must complete 85% of the Data Science with R Programming training in San Francisco.
- You must finish one project
How long does it take to complete the Data Science with R Programming training in San Francisco?
It takes approximately 40 hours to successfully complete the Data Science with R Programming training in San Francisco.
How long is the Data Science with R certificate from Simplilearn valid for?
The validity of the Data Science with R Programming training in San Francisco certificate from Simplilearn never expires.
How many attempts do I have to pass the Data Science with R certification exam?
You have a total maximum of three attempts to pass the Data Science with R certification exam. However, through the Data Science with R Programming course in San Francisco, Simplilearn provides you with guidance and support to help you pass the exam.
If I pass the Data Science with R certification exam, when and how do I receive my certificate?
When you successfully complete the Data Science with R Programming training in San Francisco and pass the exam, you will get the certificate via our Learning Management System. You can download the certificate or share it through email or Linkedin.
If I fail the Data Science with R certification exam how soon can I retake it?
You can retry it immediately.
Do you provide any practice tests as part of Data Science with R Programming training in San Francisco?
Yes, we do! We provide one practice test as part of Data Science with R Programming training in San Francisco to help prepare you for the actual certification exam. Try these free R Programming practice questions to help you better understand the kind of tests that are part of the curriculum.

R Certification Course Reviews

Savish Dan
New York City
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.
Yune Leou-On
Market Research and Monetization | Peanut Labs, Houston
I have taken Simplilearn's Data Science course & will now be taking their CAPM program. This has helped me professionally and academically, & I recommend them to anyone.
Rodney Swann
Senior Facility Manager at CBRE, Houston
Excellent instructor with the ability to provide real world experience and insights. Emphasis on the tools along with practical and useful insights. It is not an easy course for those without programming experience, but it does take away some of the mystery and confusion associated with using these tools.
Saad Madaha
Programmer Analyst III - Cardiology Information Systems at New York-Presbyterian Hospital, New York City
Level of granularity. Tutor knowledge. Class size. Tutor's confidence, subject knowledge, and high level of commitment to student understanding of the material. Tutor assisted students who had issues with SAS installation. Great Tutor-Student interaction.
Rodney Swann
Senior Facility Manager at CBRE, Houston
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.
Sasa Stevanovic
Member of the Network on Institutional Investors and Long-term Investment, Houston
Great experience with the provider, enjoyed learning, very helpful application, and staff support. Good start for mastering R, SAS, and Excel.
Ishanie Niyogi
Associate at ICF, Atlanta
The trainer was extremely knowledgeable about the course content and provided in-depth explanations for all questions that were asked. Thanks to the Simplilearn team for all the support provided during the training process.
Anubhav Ingole
Project Management Officer (PMO)/Business Analyst, Nagpur
My 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
Delhi
My trainer was very engaging and knowledgeable. I liked her way of teaching - sharing notes and providing hands-on practical's within the 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
Bangalore
Simplilearn has designed the 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, Delhi
I 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, Bangalore
My 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.
Farhan Nizar
Lead Technical Analyst at NBAD, Abu Dhabi
Course is designed in compliance with the data scientist job market, especially the Analytics concepts covered in this module will really help to correlate with the business scenarios and fulfill business needs. I am thankful to Simplilearn and trainers.
Amani Alawneh
Head of Project Management, Geneve
The course was delivered successfully. It was very punctual, organized, and thorough. Overall, it was good.
Marwa Abdalla
Technical Support Engineer at MDS TS, Abu Dhabi
Simplilearn's Data Science certification training was a good experience. The trainer is great and the content of the course is valuable. Thank you Simplilearn.

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Why Join this Program

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

Why should I learn Data Science with R from Simplilearn?
- This Data Science course in San Francisco forms an ideal package for aspiring data analysts aspiring to build a successful career in analytics/data science. By the end of the Data Science training, participants will acquire a 360-degree overview of business analytics and R by mastering concepts like data exploration, data visualization, predictive analytics, etc
- According to marketsandmarkets.com, the advanced analytics market will be worth $29.53 Billion by 2019
- Wired.com points to a report by Glassdoor that the average salary of a data scientist is $118,709
- Randstad reports that pay hikes in the analytics industry are 50% higher than the IT industry
What is the salary of a data scientist in San Francisco?
The candidate with data science with R programming training San Francisco earns a minimum annual payment of $120,186. Professionals can identify business-changing trends utilizing several approaches such as descriptive statistics, aggregation of data, and data gathering to evaluate results from numerous data sources.
What are the major companies hiring for data scientists in San Francisco?
The top flourishing companies that hire candidates with data science with R programming training San Francisco include Google, Facebook, FocusKPI, Avila Trading, and Airtable as data scientists.
What are the major industries in San Francisco?
IT & Software, Social & Digital Media, Life Sciences & Biotech, Environmental & Cleantech, and International Business are just a few of the noteworthy industries that make up San Francisco's primary industrial industry. As a result, candidates with data science with R programming training San Francisco are extremely popular.
How to become a data scientist in San Francisco?
The data scientist must collaborate closely with your company to uncover challenges and provide data-driven solutions for better decision-making. Create methods and experiments to integrate, organize, analyze, and extract data to provide customized reports to colleagues.
How to find a data science course in San Francisco?
Professionals will become an expert in analytics utilizing R programming language after taking Simplilearn's Data Science course in the San Francisco with R training sessions. This certification course allows you to apply your Data Science abilities to a range of businesses, allowing you to assist them in analyzing data and making smarter business decisions.
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.
Are the training and course material effective in preparing me for the Data Science with R certification exam?
Yes, Simplilearn’s training and course materials guarantee success with the Data Science with R certification exam.
What is online classroom training?
Online classroom training for Data science with R certification course is conducted via online live streaming of each class. The classes are conducted by a Data Science certified trainer with more than 15 years of work and training experience.
What are the system requirements?
You will need to download R from the CRAN website and RStudio for your operating system. These are both open source and the installation guidelines are presented in the R course curriculum.
Who are our instructors and how are they selected?
All of our highly qualified R course trainers are industry Data Science experts with at least 10-12 years of relevant teaching experience. Each of these R programming certificate course trainers has gone through a rigorous selection process that includes profile screening, technical evaluation, and a training demo before they are certified to train for us. We also ensure that only those trainers with a high alumni rating remain on our faculty.
What training formats are used for this R course?
We offer this Data Science with R training in the following formats:

Live Virtual Classroom or Online Classroom: With online classroom training, you have the option to attend the R course remotely from your desktop via video conferencing. This format reduces productivity challenges and decreases your time spent away from work or home.

Online Self-Learning: In this mode, you’ll receive lecture videos that you can view at your own pace.
What if I miss a class?
We record the R training sessions and provide them to participants after the session is conducted. If you miss a class, you can view the recording before the next class session.
How do I enroll for this Data Science with R certification training?
You can enroll in this Data Science with R certification training on our website and make an online payment using any of the following options:
- Visa Credit or Debit Card
- MasterCard
- American Express
- Diner’s Club
- PayPal
Once payment is received you will automatically receive a payment receipt and access information via email.
I’d like to learn more about this Data Science with R course. Whom should I contact?
Contact us using the form on the right of any page on the Simplilearn website, or select the Live Chat link. Our customer service representatives can provide you with more details.
What is Global Teaching Assistance?
Our teaching assistants are a dedicated team of subject matter experts here to help you get certified in R programming in your first attempt. They engage students proactively to ensure the course path is being followed and help you enrich your learning experience, from class onboarding to project mentoring and job assistance. Teaching Assistance is available during business hours.
What is covered under the 24/7 Support promise?
We offer 24/7 support through email, chat, and calls. We also have a dedicated team that provides on-demand assistance through our community forum. What’s more, you will have lifetime access to the community forum, even after completion of your R training with us.
*Disclaimer
*The projects have been built leveraging real publicly available data-sets of the mentioned organizations.
What is the recommended learning path after completing Data Science with R Programming certification course?
You can either enroll in our Data Scientist Course or if you are looking to get a University certificate, you can enroll in the Data Science Certification.

Data Science with R Programming Training in San Francisco Bay Area

San Francisco is truly one-of-a-kind and stands out among nearly all big cities, but only because of its topography and culture. Then there's San Francisco, which is the fourth most populated city in California, with 873,965 people as of 2020. San Francisco is the United States' 12th-greatest metropolitan statistical region, with 4.7 million people, and the 4th largest in terms of economic production, with $592 billion in GDP in 2019. San Francisco is known by the abbreviations SF, San Fran, The City, and Frisco.

Multicultural clustering, a network of global connectedness, and a confluence of technical innovation are all characteristics of the city. As a result, cities like San Francisco are thought to be complicated, requiring a high degree of expertise as well as huge numbers of low-wage labor.

San Francisco is one of the most visited tourist spots in the United States. There are also some notable architectural landmarks to see. San Francisco provides something for everyone, whether you're seeking nature, popular entertainment, or cultural and educational activities.

Acknowledgement
PMP, PMI, PMBOK, CAPM, PgMP, PfMP, ACP, PBA, RMP, SP, OPM3 and the PMI ATP seal are the registered marks of the Project Management Institute, Inc.

This course has been upgraded to

Data Scientist

Masterclasses from IBM experts
Dedicated live sessions by faculty of industry experts
Industry-recognized Data Scientist Master’s certificate from Simplilearn

Data Science with R Programming Training in San Francisco Bay Area

R Certification Course Overview

R Certification Training Key Features

Skills Covered

Take the first step to your goals

Benefits

Training Options

Self Paced Learning

Corporate Training

R Certification Course Curriculum

Eligibility

Pre-requisites

Course Content

Data Science with R Programming

Lesson 00 - Course Introduction

Course Introduction

Accessing Practice Lab

Lesson 01 - Introduction to Business Analytics

1.001 Overview

1.002 Business Decisions and Analytics

1.003 Types of Business Analytics

1.004 Applications of Business Analytics

1.005 Data Science Overview

1.006 Conclusion

Knowledge Check

Lesson 02 - Introduction to R Programming

2.001 Overview

2.002 Importance of R

2.003 Data Types and Variables in R

2.004 Operators in R

2.005 Conditional Statements in R

2.006 Loops in R

2.007 R script

2.008 Functions in R

2.009 Conclusion

Knowledge Check

Lesson 03 - Data Structures

3.001 Overview

3.002 Identifying Data Structures

3.003 Demo Identifying Data Structures

3.004 Assigning Values to Data Structures

3.005 Data Manipulation

3.006 Demo Assigning values and applying functions

3.007 Conclusion

Knowledge Check

Lesson 04 - Data Visualization

4.001 Overview

4.002 Introduction to Data Visualization

4.003 Data Visualization using Graphics in R

4.004 ggplot2

4.005 File Formats of Graphic Outputs

4.006 Conclusion

Knowledge Check

Lesson 05 - Statistics for Data Science-I

5.001 Overview

5.002 Introduction to Hypothesis

5.003 Types of Hypothesis

5.004 Data Sampling

5.005 Confidence and Significance Levels

5.006 Conclusion

Knowledge Check

Lesson 06 - Statistics for Data Science-II

6.001 Overview

6.002 Hypothesis Test

6.003 Parametric Test

6.004 Non-Parametric Test

6.005 Hypothesis Tests about Population Means

6.006 Hypothesis Tests about Population Variance

6.007 Hypothesis Tests about Population Proportions

6.008 Conclusion

Knowledge Check

Lesson 07 - Regression Analysis

7.001 Overview

7.002 Introduction to Regression Analysis

7.003 Types of Regression Analysis Models

7.004 Linear Regression

7.005 Demo Simple Linear Regression

7.006 Non-Linear Regression

7.007 Demo Regression Analysis with Multiple Variables

7.008 Cross Validation

2.04 Measures of Central Tendencies

2.06 Measures of Variability

2.07 Measures of Relationship

3.02 Introduction to Coordinate Geometry

3.03 Coordinate Geometry Formulas