Data Science with R Training in Marathahalli

“R” is a highly extensible and open source language used for statistical computing and analysis. Some of the commonly used statistical techniques using “R” are Linear and nonlinear modeling, time series analysis, clustering. “R” can be combined with any of the platforms like UNIX, Windows, MacOS, Linux and FreeBSD. It allows a data analyst to perform the data reporting, data manipulation and representation easily and effectively by using graphical in-built facilities available in “R”. Overall “R” is a well-developed programming language which includes loops, conditions, user defined recursive functions and lot more.

Data Science with R Job Opportunities

  • Make yourself strong in R programming, deep learning, statistics and data analysis. Then, you can find opportunities for “Data Analyst” jobs.
  • Start learning analytics and statistics skills along with R programming, You can make yourself eligible for “Data Scientist” jobs.
  • If you have know advanced analytics, predictive analysis, SAS and SQL Server along with R programming, then you can find opportunities for “Statistical Modeller” jobs.
  • Make yourself strong in Robotics, Linux, Analytics and image processing along with R. Then, you can find opportunities for “Imaging Scientist” jobs.
  • Add Java, NLP, algorithms as co-skills with R programming. Then, you can find opportunities for “Data Science Engineer” jobs.
  • JPMorgan, Amazon, IBM, Deloitte, Mphasis, Intel, Accenture, Capgemini, KPMG, Philips, Cyient are some of the companies hire for Data Scientists.
  • If you are a newbie to Data Science, then you need proper training and real time experience in any RDBMS and R programming for at least 3 years. That will help you meet the current market trends and demands.

Expand your data science job opportunities and maximize the chances by acquiring the best support and training from TIB Academy.

Best Data Science with R Training in Marathahalli

I was looking for a Data Science with R training in Marathahalli and TIB Academy was suggested by some of my friends. The Data Science with R Trainer was very good. His knowledge on Data Science was good. And most importantly he was able to deliver the knowledge among us.

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Prerequisites for Data Science with R

  • Strong R programming knowledge.
  • If you are already familiar with the above, this course will be easier for you to learn. Otherwise, our experienced professionals are here to teach you and coach you from the data science fundamentals.

Are you a beginner? Evaluate yourself with the following basic prerequisite questions.

  1. What is R?
  2. How to define a function using R?
  3. What are the advantages of R?
  4. What are the difference between library() and require()?

Our Data Science with R Training and Support

TIB Academy is the best Data Science with R training institute in Marathahalli. Our trainers are highly experienced professionals. Currently, they are all working in top rated MNCs and Corporates, carrying years of real time experience in their particular technologies. In this Data Science with R training in Marathahalli, you will be experiencing a differentiated learning environment. Our Data Science with R syllabus includes classes, functions, OOPs, file operations, memory management, garbage collections, standard library modules, generators, iterators, fourier transforms, discrete cosine transforms, signal processing, linear algebra, spatial data structures and algorithms, multi-dimensional image processing and lot more. For the detailed Data Science with R course syllabus, please check below.

Usually, our Data Science with R training sessions are scheduled during weekday mornings (7AM – 10AM), weekday evenings (7PM – 9:30PM) and weekends (flexible timings). We do provide Data Science with R classroom training and Data Science with R online training, both on weekdays and weekends based upon the student’s preferred time slots.

You will surely enhance your technical skills and confidence via this Data Science with R training. Our connections and networks in the job market will help you to achieve your dream job easily. Compared to other training institutes, we are offering the best Data Science with R training course in Marathahalli, Bangalore, where you can get the best Data Science with R training and placement guidance for reasonable and affordable cost.

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Data Science with R Training in Marathahalli Syllabus

1. Introduction to Business Analytics

  •  Introduction
  •  Objectives
  •  Need of Business Analytics
  •  Business Decisions
  •  Introduction to Business Analytics
  •  Features of Business Analytics
  •  Types of Business Analytics
  •  Descriptive Analytics
  •  Predictive Analytics
  •  Prescriptive Analytics
  •  Supply Chain Analytics
  •  Health Care Analytics
  •  Marketing Analytics
  • Human Resource Analytics
  •  Web Analytics
  • Application of Business Analytics – Wal-Mart Case Study
  • Application of Business Analytics – Signet Bank Case Study
  • Business Decisions
  • Business Intelligence (BI)
  • Data Science
  • Importance of Data Science
  • Data Science as a Strategic Asset
  • Big Data
  • Analytical Tools
  • Quiz
  • Summary
  • Conclusion

2. Introduction to R

  • Introduction
  • Objectives
  • An Introduction to R
  • Comprehensive R Archive Network (CRAN)
  • Cons of R
  • Companies Using R
  • Understanding R
  • Installing R on Various Operating Systems
  • Installing R on Windows from CRAN Website
  • Demo – Install R
  • Install R
  • IDEs for R
  • Installing R-Studio on Various Operating Systems
  • Demo – Install R-Studio
  • Install R-Studio
  • Steps in R Initiation
  • Benefits of R Workspace
  • Setting the Workplace
  • Functions and Help in R
  • Demo – Access the Help Document
  • Access the Help Document
  • R Packages
  • Installing an R Package
  • Demo – Install and Load a Package
  • Install and Load a Package
  • Quiz
  • Summary
  • Conclusion

3. R Programming

  • Introduction
  • Objectives
  • Operators in R
  • Arithmetic Operators
  • Demo – Perform Arithmetic Operations
  • Use Arithmetic Operations
  • Relational Operators
  • Demo – Use Relational Operators
  • Use Relational Operators
  • Logical Operators
  • Demo – Perform Logical Operations
  • Use Logical Operators
  • Assignment Operators
  • Demo – Use Assignment Operator
  • Use Assignment Operator
  • Conditional Statements in R
  • Ifelse() Function
  • Demo – Use Conditional Statements
  • Use Conditional Statements
  • Switch Function
  • Demo – Use the Switch Function
  • Use Switch Function
  • Loops in R
  • Break Statement
  • Next Statement
  • Demo – Use Loops
  • Use Loops
  • Scan() Function
  • Running an R Script
  • R Functions
  • Demo – Use R Functions
  • Use Commonly Used Functions
  • Demo – Use String Functions
  • Use Commonly used String Functions
  • Quiz
  • Summary
  • Conclusion

4. R Data Structure

  • Introduction
  • Objectives
  • Types of Data Structures in R
  • Vectors
  • Demo – Create a Vector
  • Create a Vector
  • Scalars
  • Colon Operator
  • Accessing Vector Elements
  • Matrices
  • Accessing Matrix Elements
  • Demo – Create a Matrix
  • Create a Matrix
  • Arrays
  • Accessing Array Elements
  • Demo – Create an Array
  • Create an Array
  • Data Frames
  • Elements of Data Frames
  • Demo – Create a Data Frame
  • Create a Data Frame
  • Factors
  • Demo – Create a Factor
  • Create a Factor
  • Lists
  • Demo – Create a List
  • Create a List
  • Importing Files in R
  • Importing an Excel File
  • Importing a Minitab File
  • Importing a Table File
  • Importing a CSV File
  • Demo – Read Data from a File
  • Read Data from a File
  • Exporting Files from R
  • Quiz
  • Summary
  • Conclusion

5. Apply Functions

  • Introduction
  • Objectives
  • Types of Apply Functions
  • Apply() Function
  • Demo – Use Apply() Function
  • Use Apply Function
  • Lapply() Function
  • Demo – Use Lapply() Function
  • Use Lapply Function
  • Sapply() Function
  • Demo – Use Sapply() Function
  • Use Sapply Function
  • Tapply() Function
  • Demo – Use Tapply() Function
  • Use Tapply Function
  • Vapply() Function
  • Demo – Use Vapply() Function
  • Use Vapply Function
  • Mapply() Function
  • Dplyr Package – An Overview
  • Dplyr Package – The Five Verbs
  • Installing the Dplyr Package
  • Functions of the Dplyr Package
  • Functions of the Dplyr Package – Select()
  • Demo – Use the Select() Function
  • Use the Select Function
  • Functions of Dplyr-Package – Filter()
  • Demo – Use the Filter() Function 00:05
  • Use Select Function
  • Functions ofDplyr Package – Arrange()
  • Demo – Use the Arrange() Function
  • Use Arrange Function
  • Functions of Dplyr Package – Mutate()
  • Functions ofDplyr Package – Summarise()
  • Demo – Use the Summarise() Function
  • Use Summarise Function
  • Quiz
  • Summary
  • Conclusion

6. Data Visualization

  • Introduction
  • Objectives
  • Graphics in R
  • Types of Graphics
  • Bar Charts
  • Creating Simple Bar Charts
  • Demo – Create a Bar Chart
  • Editing a Simple Bar Chart
  • Demo – Create a Stacked Bar Plot and Grouped Bar Plot
  • Pie Charts
  • Create a Pie Chart
  • Editing a Pie Chart
  • Histograms
  • Creating a Histogram
  • Kernel Density Plots
  • Creating a Kernel Density Plot
  • Line Charts
  • Creating a Line Chart
  • Box Plots
  • Creating a Box Plot
  • Create Line Graphs and a Box Plot
  • Heat Maps
  • Creating a Heat Map
  • Create a Heatmap
  • Word Clouds
  • Creating a Word Cloud
  • Demo – Create a Word Cloud
  • File Formats for Graphic Outputs
  • Saving a Graphic Output as a File
  • Demo – Save Graphics to a File
  • Exporting Graphs in RStudio
  • Exporting Graphs as PDFs in RStudio
  • Demo – Save Graphics Using RStudio
  • Quiz
  • Summary
  • Conclusion

7. Introduction to Statistics

  • Introduction
  • Objectives
  • Basics of Statistics
  • Types of Data
  • Qualitative vs. Quantitative Analysis
  • Types of Measurements in Order
  • Nominal Measurement
  • Ordinal Measurement
  • Interval Measurement
  • Ratio Measurement
  • Statistical Investigation
  • Statistical Investigation Steps
  • Normal Distribution
  • Example of Normal Distribution
  • Importance of Normal Distribution in Statistics
  • Use of the Symmetry Property of Normal Distribution
  • Standard Normal Distribution
  • Demo – Use Probability Distribution Functions
  • Use Probability Distribution Functions
  • Distance Measures
  • Distance Measures – A Comparison
  • Euclidean Distance
  • Example of Euclidean Distance
  • Manhattan Distance
  • Minkowski Distance
  • Demo – Mahalanobis Distance
  • Cosine Similarity
  • Correlation
  • Correlation Measures Explained
  • Pearson Product Moment Correlation (PPMC)
  • Pearson Correlation – Case Study
  • Dist() Function in R
  • Demo – Perform the Distance Matrix Computations
  • Quiz
  • Summary
  • Conclusion

8. Hypothesis Testing I

  • Introduction
  • Objectives
  • Hypothesis
  • Need of Hypothesis Testing in Businesses
  • Null Hypothesis
  • Alternate Hypothesis
  • Null vs. Alternate Hypothesis
  • Chances of Errors in Sampling
  • Types of Errors
  • Contingency Table
  • Decision Making
  • Critical Region
  • Level of Significance
  • Confidence Coefficient
  • Beta Risk
  • Power of Test
  • Factors Affecting the Power of Test
  • Types of Statistical Hypothesis Tests
  • An Example of Statistical Hypothesis Tests
  • Upper Tail Test
  • Test Statistic
  • Factors Affecting Test Statistic
  • Critical Value Using Normal Probability Table
  • Quiz
  • Summary
  • Conclusion

9. Hypothesis Testing II

  • Introduction
  • Objectives
  • Parametric Tests
  • Z-Test
  • Z-Test in R – Case Study
  • T-Test
  • T-Test in R – Case Study
  • Demo – Use Normal and Student Probability Distribution Functions
  • Objectives of Null Hypothesis Test
  • Testing Null Hypothesis
  • Three Types of Hypothesis Tests
  • Hypothesis Tests About Population Means
  • Decision Rules
  • Hypothesis Tests About Population Means – Case Study
  • Hypothesis Tests About Population Proportions 00:28
  • Chi-Square Test
  • Steps of Chi-Square Test
  • Degree of Freedom
  • Chi-Square Test for Independence
  • Chi-Square Test for Goodness of Fit
  • Chi-Square Test for Independence – Case Study
  • Chi-Square Test in R – Case Study
  • Demo – Use Chi-Squared Test Statistics
  • Introduction to ANOVA Test
  • One-Way ANOVA Test
  • The F-Distribution and F-Ratio
  • F-Ratio Test
  • F-Ratio Test in R – Example
  • One-Way ANOVA Test – Case Study
  • One-Way ANOVA Test in R – Case Study
  • Demo – Perform ANOVA
  • Perform ANOVA
  • Quiz
  • Summary
  • Conclusion

Lesson 10 – Regression Analysis

  • Introduction
  • Objectives
  • Introduction to Regression Analysis
  • Use of Regression Analysis – Examples
  • Types Regression Analysis
  • Simple Regression Analysis
  • Multiple Regression Models
  • Simple Linear Regression Model
  • Simple Linear Regression Model Explained
  • Demo – Perform Simple Linear Regression
  • Perform Simple Linear Regression
  • Correlation
  • Correlation Between X and Y
  • Demo – Find Correlation
  • Method of Least Squares Regression Model
  • Coefficient of Multiple Determination Regression Model
  • Standard Error of the Estimate Regression Model
  • Dummy Variable Regression Model
  • Interaction Regression Model
  • Non-Linear Regression
  • Non-Linear Regression Models
  • Demo – Perform Regression Analysis with Multiple Variables
  • Non-Linear Models to Linear Models
  • Algorithms for Complex Non-Linear Models
  • Quiz
  • Summary
  • Conclusion

Lesson 11 – Classification

  • Introduction
  • Objectives
  • Introduction to Classification
  • Examples of Classification
  • Classification vs. Prediction
  • Classification System
  • Classification Process
  • Classification Process – Model Construction
  • Classification Process – Model Usage in Prediction
  • Issues Regarding Classification and Prediction
  • Data Preparation Issues
  • Evaluating Classification Methods Issues
  • Decision Tree
  • Decision Tree – Dataset
  • Classification Rules of Trees
  • Overfitting in Classification
  • Tips to Find the Final Tree Size
  • Basic Algorithm for a Decision Tree
  • Statistical Measure – Information Gain
  • Calculating Information Gain – Example
  • Calculating Information Gain for Continuous-Value Attributes
  • Enhancing a Basic Tree
  • Decision Trees in Data Mining
  • Demo – Model a Decision Tree
  • Model a Decision Tree
  • Naive Bayes Classifier Model
  • Features of Naive Bayes Classifier Model
  • Bayesian Theorem
  • Naive Bayes Classifier
  • Applying Naive Bayes Classifier – Example
  • Naive Bayes Classifier – Advantages and Disadvantages
  • Demo – Perform Classification Using the Naive Bayes Method
  • Nearest Neighbor Classifiers
  • Computing Distance and Determining Class
  • Choosing the Value of K
  • Scaling Issues in Nearest Neighbor Classification
  • Support Vector Machines
  • Advantages of Support Vector Machines
  • Geometric Margin in SVMs
  • Linear SVMs
  • Non-Linear SVMs
  • Demo – Support a Vector Machine
  • Quiz
  • Summary
  • Conclusion

Lesson 12 – Clustering

  • Introduction
  • Objectives
  • Introduction to Clustering
  • Clustering vs. Classification
  • Use Cases of Clustering
  • Clustering Models
  • K-means Clustering
  • K-means Clustering Algorithm
  • Pseudo Code of K-means
  • K-means Clustering Using R
  • K-means Clustering – Case Study
  • Demo – Perform Clustering Using K-means
  • Hierarchical Clustering
  • Hierarchical Clustering Algorithms
  • Requirements of Hierarchical Clustering Algorithms
  • Agglomerative Clustering Process
  • Hierarchical Clustering – Case Study
  • Demo – Perform Hierarchical Clustering
  • Quiz
  • Summary
  • Conclusion


  • DBSCAN Clustering
  • Concepts of DBSCAN
  • DBSCAN Clustering Algorithm
  • DBSCAN in R
  • DBSCAN Clustering – Case Study
  • Quiz
  • Summary
  • Conclusion

Data Science Interview Questions

  1. Which technique is used to predict categorical responses?
  2. What is logistic regression? Or State an example when you have used logistic regression recently.
  3. What are Recommender Systems?
  4. Why data cleaning plays a vital role in analysis?
  5. What do you understand by the term Normal Distribution?
  6. What is Linear Regression?
  7. What is K-means? How can you select K for K-means?
  8. What is the difference between Cluster and Systematic Sampling?
  9. Are expected value and mean value different?
  10. What does P-value signify about the statistical data?
  11. What are categorical variables?
  12. How you can make data normal using Box-Cox transformation?
  13. What is the difference between Supervised Learning an Unsupervised Learning?
  14. How can outlier values be treated?
  15. What are various steps involved in an analytics project?
  16. During analysis, how do you treat missing values?
  17. What is Machine Learning?
  18. How are confidence intervals constructed and how will you interpret them?
  19. Is Naïve Bayes bad? If yes, under what aspects.
  20. How will you define the number of clusters in a clustering algorithm?
  21. What do you understand by Fuzzy merging ? Which language will you use to handle it?
  22. What is the difference between skewed and uniform distribution?
  23. You created a predictive model of a quantitative outcome variable using multiple regressions. What are the steps you would follow to validate the model?
  24. What do you understand by Hypothesis in the content of Machine Learning?
  25. How will you find the right K for K-means?
  26. What do you understand by conjugate-prior with respect to Naïve Bayes?
  27. Can you explain the difference between a Test Set and a Validation Set?
  28. Can you write the formula to calculat R-square?
  29. How will you assess the statistical significance of an insight whether it is a real insight or just by chance?
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