Data Science with R training in Bangalore
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.
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.
Tell me more about Data Science with R!
- R is an effective and well-developed programming language which encompasses loops, conditions, user defined recursive functions and lot more.
- TIB Academy is the best Data Science with R training center 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.
Are there any prerequisites for Data Science with R?
- 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.
Can you help me about Data Science with R Job Opportunities?
- 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, TIB Academy is one of the best Data Science with R training institutes in Bangalore where you can acquire the best Data Science with R training and placement guidance.
What is special about the Data Science with R training in Global Training Bangalore?
- TIB Academy 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 opportunities.
What are the Data Science with R Class timings and course duration?
Day | Data Science with R Classroom Training Timing | Data Science with R Online Training Timing |
---|---|---|
Mon – Fri |
7AM to 10AM 7PM to 9.30PM |
7AM to 10AM 7PM to 9.30PM |
Sat,
Sun |
Flexible Timing |
Flexible Timing |
Please contact us soon to book your preferable time slot.
I was looking for a Data Science with R training in Marathahalli and Global Training Bangalore 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. I recommend this place with 5 star ratings!
Data science with R Course Duration
Regular Classes (Morning, Day time & Evening)
Duration: 30 – 35 hrs.
Weekend Training Classes (Saturday, Sunday & Holidays)
Duration: 5 weeks
Fast Track Training Program (5+ hours daily)
Duration: within 20 days.
Call Global Training Bangalore (TIB Academy)
+91 9513332301 / 02 / 03
Data Science with R Training in Bangalore 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
OPTIONAL
- DBSCAN Clustering
- Concepts of DBSCAN
- DBSCAN Clustering Algorithm
- DBSCAN in R
- DBSCAN Clustering – Case Study
- Quiz
- Summary
- Conclusion
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