Data Science with R Training in Bangalore

Many organizations need their employees to be skilled in Data Analytics and Data Science to help in understanding their business insights. Our Data Science with R training in Bangalore helps build your analytics skills. Industries like banking, e-commerce, finance, health care, insurance, telecom, gaming etc., are using Data Science. Our R for Data Science training in Bangalore helps validate your skills with real time practice sessions. Our Data Science with R course syllabus has been designed by R programming specialists and data analytics experts. We regularly update R Data Science syllabus to keep pace with latest data analytics upgrades. We assure that you will be learning the latest technology.

R is an open source and highly extensible language for statistical computing and analysis. Linear and nonlinear modeling, time series analysis, clustering are some of the statistical techniques of R. R can suit and compatible with variety of platforms like UNIX, Windows, MacOS, Linux and FreeBSD. Graphical facilities care available in R to enable effective data manipulation and representation.

Student Review about Our Data Science with R Training in Bangalore

I have been working as a “Business Analyst” for last 2 years. I was very much interested in data science and data analytics. So I joined here to learn statistical and analytical algorithms using R. My trainer taught me from basic data science algorithms and statistics. As I was already from the analytics background, this R language was very easier for me to understand. At the end of my course, I also did a project under his guidance. This wonderful training and support really helped me to upgrade my statistical skills and improved my confidence level.

More about Data Science with R Training in Bangalore

  • R is an effective and well-developed programming language which encompasses loops, conditions, user defined recursive functions and lot more.
  • Global Training Bangalore is the best Data Science with R training centre in Bangalore where you will be exposed to differentiated learning environment as the course syllabus has been prepared by the highly experienced professionals. With this course, you can learn about 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. Please check below for the detailed syllabus.

Prerequisites to Get Data Science with R Training in Bangalore

  • Strong knowledge on Python.
  • If you are already familiar with the above, this course will be quite easy for you to grasp the concepts. Otherwise, experts are here to help you with the concepts of Python and Data Science from the basics.

Data Science with R Training in Bangalore with Jobs and Placements

R Data Scientist jobs are suitable for experienced people, who have key skills on deep learning, statistics and data analysis. In the current IT market, there are plenty of data scientist opportunities for the experienced professionals who are aware of the above technologies.

  • If you possess analytics and statistics skills, you can get job as Data scientist with this course.
  • If you possess advanced analytics, predictive analysis, SAS and SQL Server as co-skills, you can get job as Statistical modeller.
  • If you possess robotics, Linux, Analytics and image processing as co-skills, you can get job as Imaging Scientist.
  • If you possess Java, NLP, algorithms as co-skills, you can get job as Data Science Engineer.
  • Some of the companies that hire for data science are JP Morgan, Amazon, IBM, Deloitte, Mphasis, Intel, Accenture, Capgemini, KPMG, Philips, Cyient.

Compared to other training institutes, Global Training Bangalore is one of the best Data Science with R training institutes in Bangalore where you can acquire the best Data Science with R training in Bangalore with placement guidance.

What is Special about Our Data Science with R Training in Bangalore? 

  • Global Training Bangalore is the only institute providing the best Data Science with R training in Bangalore. They have knowledgeable and experienced industrial professionals as the trainers who are working in fortune 500 MNCs with years of real time experience. So they can give relevant coaching for  you to become the best data scientist.
  • Since the trainers are all currently working during the day, the Data Science with R training program will be usually scheduled during weekdays early mornings between 7AM to 10AM, weekdays late evenings between 7PM to 9:30PM and flexible timings in weekends. They provide Data Science with R classroom training, Data Science with R online training and Data Science with R weekend training based upon the student’s time convenience. This training will expose you to the best Data Science with R course and placement support in Bangalore with moderate course fees.
  • The practical sessions throughout the course will help you to enhance your technical skills and confidence. Their links to the corporate job market will surely help you to get closer to your dream job. So start putting your sincere efforts into practice and grab the wonderful Data Science with R training in Bangalore with job opportunities.
Training in Bangalore Contact Number

Data science with R Course Timing & Duration

Classroom Data Science with R Training in Bangalore Timing

Mon – Fri : 7 AM to 10 AM & 7 PM to 9.30 PM 

Sat & Sun : Flexible Timing

Duration : 30 – 35 hrs.

Online Data Science with R Training in Bangalore Timing

Mon – Fri : 7 AM to 10 AM & 7 PM to 9.30 PM 

Sat & Sun : Flexible Timing

Duration : 5 weeks

Data Science with R Fast Track Training

Duration : within 20 days.

Data Science with R Training in Bangalore Reviews

For Data Science with R Training Support

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Data Science with R Training in Bangalore Syllabus

1.Introduction to Business Analytics

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

2.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

3.R Programming

  • 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
  • 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

4.Factor and List

  • 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

5.Apply

  • 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
  • Quiz and Summary

6.Data Visualization

  • Introduction
  • 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
  • Histograms
  • Creating a Histogram
  • Kernel Density Plots
  • Creating a Kernel Density Plot
  • Line Charts
  • Creating a Line Chart
  • Box Plots
  • 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 R Studio
  • Exporting Graphs as PDFs in R Studio
  • Demo – Save Graphics Using R Studio
  • Quiz and Summary

7.Introduction to Statistics

  • Basics of Statistics
  • Types of Data
  • Qualitative vs. Quantitative Analysis
  • Types of Measurements in Order
  • 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 and its example
  • Manhattan, 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 and Summary

8.Hypothesis Testing

  • 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 and Summary

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
  • 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

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

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 DecisionTree
  • 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

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

Global Training Bangalore Academy is one of the best R Data Science training institutes in Bangalore, providing the best Data science R training in Bangalore. Our professionally designed syllabus enables you to get the best Data Science using R training with placements in Bangalore, flexible Data Science course duration and affordable Data Science course fees in Bangalore. Students can choose to get Data Science classes from Data Science classroom training in Bangalore or Data Science online training in Bangalore.

We help the students with popular R Data Science interview questions at the end of the course, to help them get the best Data Science with R training in Bangalore with Jobs and Placements. For professionals who want to extend their career to a level above, we provide Machine Learning with R training and big data analytics training in Bangalore.

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