Data Science with R Programming Training in Manila, Philippines

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R Certification Course Overview

The Data Science with R Programming Course in Manila covers data visualization & exploration and predictive & descriptive analytics using the R language. Data Science with R Programming training in Manila covers all about R packages, data structures in R data import and export in R, 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


The Data Science with R Programming Course in Manila readies 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 Manila can accelerate your career.

  • Designation
  • Annual Salary
  • Hiring Companies
  • Annual Salary
    Source: Glassdoor
    Hiring Companies
    Amazon hiring for Data Analyst professionals in Manila
    JPMorgan Chase hiring for Data Analyst professionals in Manila
    Genpact hiring for Data Analyst professionals in Manila
    VMware hiring for Data Analyst professionals in Manila
    LarsenAndTurbo hiring for Data Analyst professionals in Manila
    Citi hiring for Data Analyst professionals in Manila
    Accenture hiring for Data Analyst professionals in Manila
    Source: Indeed
  • Annual Salary
    Source: Glassdoor
    Hiring Companies
    Accenture hiring for Data Scientist professionals in Manila
    Oracle hiring for Data Scientist professionals in Manila
    Microsoft hiring for Data Scientist professionals in Manila
    Walmart hiring for Data Scientist professionals in Manila
    Amazon hiring for Data Scientist professionals in Manila
    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


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


Data Science with R Programming training in Manila is great for aspiring data scientists who wish to build a Data Science career, including IT professionals or software developers who wishes to make a career switch into Data Science with R Programming training in Manila is also a perfect one for professionals working in data and business analysis and experienced professionals interested in getting the most of Data Science in their fields.
Read More


There are no prerequisites for this Data Science with R Programming training in Manila. If you are just a beginner in the Data Science field, the Data Science with R Programming course in Manila is one of the best courses to commence your journey.
Read More

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
      • 7.009 Non-Linear to Linear Models
      • 7.010 Principal Component Analysis
      • 7.011 Factor Analysis
      • 7.012 Conclusion
      • Knowledge Check
    • Lesson 08 - Classification

      • 8.001 Overview
      • 8.002 Classification and Its Types
      • 8.003 Logistic Regression
      • 8.004 Support Vector Machines
      • 8.005 Demo Support Vector Machines
      • 8.006 K-Nearest Neighbours
      • 8.007 Naive Bayes Classifier
      • 8.008 Demo Naive Bayes Classifier
      • 8.009 Decision Tree Classification
      • 8.010 Demo Decision Tree Classification
      • 8.011 Random Forest Classification
      • 8.012 Evaluating Classifier Models
      • 8.013 Demo K-Fold Cross Validation
      • 8.014 Conclusion
      • Knowledge Check
    • Lesson 09 - Clustering

      • 9.001 Overview
      • 9.002 Introduction to Clustering
      • 9.003 Clustering Methods
      • 9.004 Demo K-means Clustering
      • 9.005 Demo Hierarchical Clustering
      • 9.006 Conclusion
      • Knowledge Check
    • Lesson 10 - Association

      • 10.001 Overview
      • 10.002 Association Rule
      • 10.003 Apriori Algorithm
      • 10.004 Demo Apriori Algorithm
      • 10.005 Conclusion
      • Knowledge Check
  • Free Course
  • Math Refresher

    • Lesson 01: Course Introduction

      • 1.01 About Simplilearn
      • 1.02 Introduction to Mathematics
      • 1.03 Types of Mathematics
      • 1.04 Applications of Math in Data Industry
      • 1.05 Learning Path
      • 1.06 Course Components
    • Lesson 02: Probability and Statistics

      • 2.01 Learning Objectives
      • 2.02 Basics of Statistics and Probability
      • 2.03 Introduction to Descriptive Statistics
      • 2.04 Measures of Central Tendencies​
      • 2.05 Measures of Asymmetry
      • 2.06 Measures of Variability​
      • 2.07 Measures of Relationship​
      • 2.08 Introduction to Probability
      • 2.09 Key Takeaways
      • 2.10 Knowledge check
    • Lesson 03: Coordinate Geometry

      • 3.01 Learning Objectives
      • 3.02 Introduction to Coordinate Geometry​
      • 3.03 Coordinate Geometry Formulas​
      • 3.04 Key Takeaways
      • 3.05 Knowledge Check
    • Lesson 04: Linear Algebra

      • 4.01 Learning Objectives
      • 4.02 Introduction to Linear Algebra
      • 4.03 Forms of Linear Equation
      • 4.04 Solving a Linear Equation
      • 4.05 Introduction to Matrices
      • 4.06 Matrix Operations
      • 4.07 Introduction to Vectors
      • 4.08 Types and Properties of Vectors
      • 4.09 Vector Operations
      • 4.10 Key Takeaways
      • 4.11 Knowledge Check
    • Lesson 05: Eigenvalues Eigenvectors and Eigendecomposition

      • 5.01 Learning Objectives
      • 5.02 Eigenvalues
      • 5.03 Eigenvectors
      • 5.04 Eigendecomposition
      • 5.05 Key Takeaways
      • 5.06 Knowledge Check
    • Lesson 06: Introduction to Calculus

      • 6.01 Learning Objectives
      • 6.02 Basics of Calculus
      • 6.03 Differential Calculus
      • 6.04 Differential Formulas
      • 6.05 Integral Calculus
      • 6.06 Integration Formulas
      • 6.07 Key Takeaways
      • 6.08 Knowledge Check
  • Free Course
  • Statistics Essential for Data Science

    • Lesson 01: Course Introduction

      • 1.01 Course Introduction
      • 1.02 What Will You Learn
    • Lesson 02: Introduction to Statistics

      • 2.01 Learning Objectives
      • 2.02 What Is Statistics
      • 2.03 Why Statistics
      • 2.04 Difference between Population and Sample
      • 2.05 Different Types of Statistics
      • 2.06 Importance of Statistical Concepts in Data Science
      • 2.07 Application of Statistical Concepts in Business
      • 2.08 Case Studies of Statistics Usage in Business
      • 2.09 Applications of Statistics in Business: Time Series Forecasting
      • 2.10 Applications of Statistics in Business Sales Forecasting
      • 2.11 Recap
    • Lesson 03: Understanding the Data

      • 3.01 Learning Objectives
      • 3.02 Types of Data in Business Contexts
      • 3.03 Data Categorization and Types of Data
      • 3.03 Types of Data Collection
      • 3.04 Types of Data
      • 3.05 Structured vs. Unstructured Data
      • 3.06 Sources of Data
      • 3.07 Data Quality Issues
      • 3.08 Recap
    • Lesson 04: Descriptive Statistics

      • 4.01 Learning Objectives
      • 4.02 Descriptive Statistics
      • 4.03 Mathematical and Positional Averages
      • 4.04 Measures of Central Tendancy: Part A
      • 4.05 Measures of Central Tendancy: Part B
      • 4.06 Measures of Dispersion
      • 4.07 Range Outliers Quartiles Deviation
      • 4.08 Mean Absolute Deviation (MAD) Standard Deviation Variance
      • 4.09 Z Score and Empirical Rule
      • 4.10 Coefficient of Variation and Its Application
      • 4.11 Measures of Shape
      • 4.12 Summarizing Data
      • 4.13 Recap
      • 4.14 Case Study One: Descriptive Statistics
    • Lesson 05: Data Visualization

      • 5.01 Learning Objectives
      • 5.02 Data Visualization
      • 5.03 Basic Charts
      • 5.04 Advanced Charts
      • 5.05 Interpretation of the Charts
      • 5.06 Selecting the Appropriate Chart
      • 5.07 Charts Do's and Dont's
      • 5.08 Story Telling With Charts
      • 5.09 Data Visualization: Example
      • 5.10 Recap
      • 5.11 Case Study Two: Data Visualization
    • Lesson 06: Probability

      • 6.01 Learning Objectives
      • 6.02 Introduction to Probability
      • 6.03 Probability Example
      • 6.04 Key Terms in Probability
      • 6.05 Conditional Probability
      • 6.06 Types of Events: Independent and Dependent
      • 6.07 Addition Theorem of Probability
      • 6.08 Multiplication Theorem of Probability
      • 6.09 Bayes Theorem
      • 6.10 Recap
    • Lesson 07: Probability Distributions

      • 7.01 Learning Objectives
      • 7.02 Probability Distribution
      • 7.03 Random Variable
      • 7.04 Probability Distributions Discrete vs.Continuous: Part A
      • 7.05 Probability Distributions Discrete vs.Continuous: Part B
      • 7.06 Commonly Used Discrete Probability Distributions: Part A
      • 7.07 Discrete Probability Distributions: Poisson
      • 7.08 Binomial by Poisson Theorem
      • 7.09 Commonly Used Continuous Probability Distribution
      • 7.10 Application of Normal Distribution
      • 7.11 Recap
    • Lesson 08: Sampling and Sampling Techniques

      • 8.01 Learnning Objectives
      • 8.02 Introduction to Sampling and Sampling Errors
      • 8.03 Advantages and Disadvantages of Sampling
      • 8.04 Probability Sampling Methods: Part A
      • 8.05 Probability Sampling Methods: Part B
      • 8.06 Non-Probability Sampling Methods: Part A
      • 8.07 Non-Probability Sampling Methods: Part B
      • 8.08 Uses of Probability Sampling and Non-Probability Sampling
      • 8.09 Sampling
      • 8.10 Probability Distribution
      • 8.11 Theorem Five Point One
      • 8.12 Center Limit Theorem
      • 8.13 Sampling Stratified: Sampling Example
      • 8.14 Probability Sampling: Example
      • 8.15 Recap
      • 8.16 Case Study Three: Sample and Sampling Techniques
      • 8.17 Spotlight
    • Lesson 09: Inferential Statistics

      • 9.01 Learning Objectives
      • 9.02 Inferential Statistics
      • 9.03 Hypothesis and Hypothesis Testing in Businesses
      • 9.04 Null and Alternate Hypothesis
      • 9.05 P Value
      • 9.06 Levels of Significance
      • 9.07 Type One and Two Errors
      • 9.08 Z Test
      • 9.09 Confidence Intervals and Percentage Significance Level: Part A
      • 9.10 Confidence Intervals: Part B
      • 9.11 One Tail and Two Tail Tests
      • 9.12 Notes to Remember for Null Hypothesis
      • 9.13 Alternate Hypothesis
      • 9.14 Recap
      • 9.15 Case Study 4: Inferential Statistics
      • Hypothesis Testing
    • Lesson 10: Application of Inferential Statistics

      • 10.01 Learning Objectives
      • 10.02 Bivariate Analysis
      • 10.03 Selecting the Appropriate Test for EDA
      • 10.04 Parametric vs. Non-Parametric Tests
      • 10.05 Test of Significance
      • 10.06 Z Test
      • 10.07 T Test
      • 10.08 Parametric Tests ANOVA
      • 10.09 Chi-Square Test
      • 10.10 Sign Test
      • 10.11 Kruskal Wallis Test
      • 10.12 Mann Whitney Wilcoxon Test
      • 10.13 Run Test for Randomness
      • 10.14 Recap
    • Lesson 11: Relation between Variables

      • 11.01 Learning Objectives
      • 11.02 Correlation
      • 11.03 Karl Pearson's Coefficient of Correlation
      • 11.04 Karl Pearsons: Use Cases
      • 11.05 Correlation Example
      • 11.06 Spearmans Rank Correlation Coefficient
      • 11.07 Causation
      • 11.08 Example of Regression
      • 11.09 Coefficient of Determination
      • 11.10 Quantifying Quality
      • 11.11 Recap
    • Lesson 12: Application of Statistics in Business

      • 12.01 Learning Objectives
      • 12.02 How to Use Statistics In Day to Day Business
      • 12.03 Example: How to Not Lie With Statistics
      • 12.04 How to Not Lie With Statistics
      • 12.05 Lying Through Visualizations
      • 12.06 Lying About Relationships
      • 12.07 Recap
      • 12.08 Spotlight
    • Lesson 13: Assisted Practice

      • Assisted Practice: Problem Statement
      • Assisted Practice: Solution

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.

    Products rating prediction for Amazon
  • 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.

    Demand Forecasting for Walmart
  • 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.

    Improving customer experience for Comcast
  • 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.

    Attrition Analysis for IBM

Data Science with R Exam & Certification

Data Science with R Programming Training in Manila, Philippines
  • Who provides the certification?

    After you complete the Data Science with R Programming course in Manila, you will be given 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 Manila.
    • You must complete one project
    Online Self-Learning:
    • You must complete 85% of the Data Science with R Programming training in Manila.
    • You must finish one project

  • How long does it take to complete the Data Science with R Programming training in Manila?

    It takes approximately 40 hours to successfully complete the Data Science with R Programming training in Manila.

  • How long is the Data Science with R certificate from Simplilearn valid for?

    The validity of the Data Science with R Programming training in Manila 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 Manila, Simplilearn will help you with the right 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?

    Once you successfully go through the Data Science with R Programming training in Manila and crack the exam, you'll recieve 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 Manila?

    Yes, we do! We also provide a practice test as part of Data Science with R Programming training in Manila to help prepare you for the final 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

    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

    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.

  • Saad Madaha

    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

    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.

  • Rodney Swann

    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

    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

    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

    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

    Sheetal Nagpal


    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

    Amol B


    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

    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

    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

    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

    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

    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.


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.

  • 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.

  • Why should I learn Data Science with R from Simplilearn?

    • This Data Science course in Manila, Philippines 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 certification 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, the advanced analytics market will be worth $29.53 Billion by 2019
    • 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 are the course objectives?

    The Data Science course in the Philippines has been designed to give you in-depth knowledge of the various data analytics techniques that can be performed using R. The Data Science training is packed with real-life projects and case studies.

    • Mastering R language: The Data Science certification provides an in-depth understanding of the R language, R-studio, and R packages. You will learn the various types of applications functions including DPYR, gain an understanding of data structure in R, and perform data visualizations using the various graphics available in R.
    • Mastering advanced statistical concepts: The Data Science online certification also includes various statistical concepts such as linear and logistic regression, cluster analysis and forecasting. You will also learn hypothesis testing.

  • What will you learn in this Data Science course in Manila, Philippines?

    This Data Science course in Manila, Philippines will enable you to:

    • Gain a foundational understanding of business analytics
    • Install R, R-studio, and workspace setup, and learn about the various R packages
    • Master R programming and understand how various statements are executed in Rcour
    • 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 DPYR functions
    • Understand and use the various graphics in R for data visualization
    • Gain a basic understanding of various statistical concepts
    • Understand and use hypothesis testing method to drive business decisions
    • Understand and use linear, non-linear regression models, and classification techniques for data analysis
    • Learn and use the various association rules and Apriori algorithm
    • Learn and use clustering methods including K-means, DBSCAN, and hierarchical clustering

  • Who should take this online Data Science certification course in Manila, Philippines?

    There is an increasing demand for skilled data scientists across all industries in Manila, making this Data Science course in the Philippines well-suited for participants at all levels of experience. We recommend this Data Science certification training particularly for the following professionals:

    • IT professionals looking for a career switch into data science and analytics
    • Software developers looking for a career switch into data science and analytics
    • Professionals working in data and business analytics
    • Graduates looking to build a career in analytics and data science
    • Anyone with a genuine interest in the data science field
    • Experienced professionals who would like to harness data science in their fields

    Prerequisites: There are no prerequisites for this data science online training. If you are new in the field of data science, this is the best course to start with.

  • What projects will you work on during this Data Science training in Manila, Philippines?

    The Data Science training in Manila, Philippines includes ten real-life, industry-based projects. Successful evaluation of one of the following six projects is a part of the certification eligibility criteria.


    Project 1: Products rating prediction for Amazon

    Amazon, one of the leading US-based e-commerce companies, recommends products within the same category to customers based on their activity and reviews on other similar products. Amazon would like to improve this recommendation engine by predicting ratings for the non-rated products and add them to recommendations accordingly.

    Domain: E-commerce


    Project 2: Demand Forecasting for Walmart

    Predict accurate sales for 45 stores of Walmart, one of the US-based leading retail stores, considering the impact of promotional markdown events. Check if macroeconomic factors like CPI, unemployment rate, etc. have an impact on sales.

    Domain: Retail


    Project 3: Improving customer experience for Comcast

    Comcast, one of the US-based global telecommunication companies wants to improve customer experience by identifying and acting on problem areas that lower customer satisfaction if any. The company is also looking for key recommendations that can be implemented to deliver the best customer experience.

    Domain: Telecom


    Project 4: Attrition Analysis for IBM

    IBM, one of the leading US-based IT companies, would like to identify the factors that influence the attrition of employees. Based on the parameters identified, the company would also like to build a logistics regression model that can help predict if an employee will churn or not.

    Domain: Workforce Analytics


    Project 5:
    A nationwide survey of hospital costs conducted by the US Agency for Healthcare consists of hospital records of inpatient samples. The given data is restricted to the state of Wisconsin and relates to patients in the age group 0-17 years. The agency wants to analyze the data to research on health care costs and their utilization.

    Domain: Healthcare 

    Project 6:
    The data gives the details of third party motor insurance claims in Sweden for the year 1977. In Sweden, all motor insurance companies apply identical risk arguments to classify customers, and thus their portfolios and their claims statistics can be combined. The data were compiled by a Swedish Committee on the Analysis of Risk Premium in Motor Insurance. The Committee was asked to look into the problem of analyzing the real influence on the claims of the risk arguments and to compare this structure with the actual tariff.

    Domain: Insurance

    Project 7:
     A high-end fashion retail store is looking to expand its products. It wants to understand the market and find the current trends in the industry. It has a database of all products with attributes, such as style, material, season, and the sales of the products over a period of two months. 

    Domain: Retail

    Project 8:
    The web analytics team of is interested to understand the web activities of the site, which are the sources used to access the website. They have a database that states the keywords of time in the page, source group, bounces, exits, unique page views, and visits.

    Domain: Internet


    Project 9: 

    An education department in the US needs to analyze the factors that influence the admission of a student into a college. Analyze the historical data and determine the key drivers. 

    Domain: Education


    Project 10:

    A UK-based online retail store has captured the sales data for different products for a period of one year (Nov 2016 to Dec 2017). The organization sells gifts primarily on the online platform. The customers who make a purchase consume directly for themselves. There are small businesses that buy in bulk and sell to other customers through the retail outlet channel. Find significant customers for the business who make high purchases of their favorite products.

    Domain: E-commerce

    The course also includes 4 more projects for you to practice.

    Project 11:
    Details of listener preferences are recorded online. This data is not only used for recommending music that the listener is likely to enjoy but also to drive a focused marketing strategy that sends out advertisements for music that a listener may wish to buy. Using the demographic data, predict the music preferences of the user for targeted advertising.

    Domain: Music Industry


    Project 12:
    You’ll predict whether someone will default or not default on a loan based on user demographic data. You’ll perform logistic regression by considering the loan’s features and the characteristics of the borrower as explanatory variables.

    Domain: Finance 


    Project 13:
    Analyze the monthly, seasonally-adjusted unemployment rates for U.S. employment data of all 50 states, covering the period from January 1976 through August 2010. The requirement is to cluster the states into groups that are alike using a feature vector.

    Domain: Unemployment 

    Project 14:
    Flight delays are frequently experienced when flying from the Washington DC area to the New York City area. By using logistical regression, you’ll identify flights that are likely to be delayed. The provided dataset helps with a number of variables including airports and flight times.

    Domain: Airline

  • What is the market trend for Data Science in the Philippines?

    According to Payscale, a data scientist with an average working experience of about 5 years has the potential to earn about Philippine Peso 884K per annum, while CAs with the same level of experience earn about Philippine Peso 600K and engineers earn Philippine Peso 450K. If this salary trend is anything to go by, then the demand for data professionals has never been higher. 

    In 2017, a report by Analytics India Magazine highlighted the rising trend of data-oriented jobs. According to this report, the number of Data Science jobs in the Philippines almost doubled in 2017, and over 50,000 positions are yet to be filled. With regard to Philippines cities, Manila holds the title of having the highest number of analytics jobs in the Philippines. In 2017, over 25% of all analytics jobs originated in Manila. According to Gartner, the self-learning (ML-powered) intelligent systems will continue to reign supreme in the technology marathon through 2020.

  • What are the top companies offering Data Scientist jobs in Manila?

    Several companies in Manila are on the lookout for R Certified Data Scientists. According to, some of the top companies looking out for Data Science professionals in Manila are Appcentric Solutions Inc, DataSpark Pte Ltd, Emapta Versatile Services Inc, Ninja Van, IBM Solutions Delivery INC, SG Interactive, Inc. and many more.

  • What is the average salary for R Certified Data Scientist in Manila?

    According to Payscale, Data Science professionals in Manila can earn an average of Philippine peso 884K per year. However, a certified data science professional with experience can earn up Philippine peso 1M per annum.

  • Is this Data Science certification course in Manila suitable for freshers?

    Yes, the Data Science certification course in Manila is suitable for freshers, and this course makes you an expert in data analytics using the R programming language.

  • What is the price of the Data Science certification course in Manila?

    The price of the Data Science certification course in Manila is $799.

  • In which areas of Manila is the Data Science certification course conducted?

    No matter which area of Manila you are in, be it Makati City, Taguig City, Muntinlupa City anywhere. You can access our Data Science course online sitting at home or office.

  • Do you provide this Data Science certification training in Manila with placement?

    No, currently, we do not provide any placement guarantee with the Data Science course.

  • Why do I need to choose Simplilearn to learn Data Science in Manila?

    Simplilearn provides instructor-led training, lifetime access to self-paced learning, training from industry experts, and real-life industry projects with multiple video lessons.

  • What is the salary of a data scientist in Manila?

    The candidate with data science with R programming training Manila earns an average lumpsum amount of ?750,121. A data scientist's position includes computer science, analytics, and arithmetic. They evaluate the outcomes of data analysis, processing, and modelling to produce actionable strategies for businesses and other organizations.

  • What are the major companies hiring for data scientists in Manila?

    Kantar Philippines, GLOBE TELECOM, Manulife, Accenture, Nielsen are top flourishing companies that hire candidates with data science with R programming training Manila for prestigious roles like data analysts and data scientists.

  • What are the major industries in Manila?

    Manila is a major centre for business, finance, commerce, shipping, tourism, real estate investment, internet and traditional media, promotion, legal support, accounting, insurance, entertainment, architecture, and art. Candidates with data science with R programming training Manila are highly preferred by the companies for the jobs.

  • How to become a data scientist in Manila?

    The primary responsibility of a data scientist is to organize and analyze huge amounts of data using custom-designed statistical technology so that stakeholders can make intelligent business choices. So Data Visualization and Intellectual Curiosity must be the two main skills required for a candidate.

  • How to find a data science course in Manila?

    The Data Science with R certification course from Simplilearn will teach you how to analyze data using the R programming language. This online R training allows you to apply your Data Science skills to a range of organizations, assisting them in data analysis and making smarter business decisions.

  • 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.

  • Can I cancel my enrollment? Will I get a refund?

    Yes, you can cancel your enrollment if necessary. We will refund the course price after deducting an administration fee. To learn more, you can view our Refund Policy.

  • Are there any group discounts for classroom training programs?

    Yes, we offer group discounts for our online training programs. Get in touch with us over the Drop us a Query or Request a Callback or Live Chat channels to find out more about our group discount packages.

  • 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.

Data Science with R Programming Training in Manila, Philippines

Manila is densely populated, and it was the world's most populous town in 2019. The Philippines' main seaport and key international maritime gateway are the Port of Manila.

Manila's climate is classified as tropical. Summers in Manila are noticeably wetter than winters. The average annual temperature is 26.6°C, with rainfall of approximately 1666 millimeters.

The Philippines' economy is the world's 27th major by nominal GDP and Asia's 10th largest. The Philippines is among Southeast Asia's growing economies, with the third-biggest GDP absolute behind Thailand and Indonesia.

Due to the sheer variety of attractions to visit in the city, such as religious establishments and historic buildings, Manila is regarded as the "Rome of the East." So don’t forget to check out these places.

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