Data Science using R
Best R training for industry relevant Advanced Analytics and Machine Learning skills!
124 Hours
18 Classes
13APR
Bangalore
04 MAY
Gurgaon
20 APR
Noida
Learning Modes
Classroom & Bootcamp
INR 6496*/-
With No Cost EMI
Fully Interactive Live Online
INR 6387*/-
With No Cost EMI
Blended eLearning
INR 6387*/-
With No Cost EMI
Courses Included
Artificial Intelligence Engineering
₹ 20000/-
Artificial Intelligence Engineering
₹ 20000/-
Artificial Intelligence Engineering
₹ 20000/-
₹ 65000/-
₹ 48000*/-
Optional Course
Data Science using R
₹ 20,000
₹ 4000/-
Overview
No of classes X Hours
18 x 3 = 54 Hours
54 hrs eLearning
Self Study Hours
70 Hours
(8-10 hrs/ week)
10 Assignments & Projects
Placement Readiness Program
With this comprehensive R training learn hand-on skills on Data Science with R – the golden boy of Data Science! Over past several years R has garnered immense popularity among Data Science practitioners and it is no surprise that R language is often as referred as lingua franca of Data Science! This Data Science R course effectively covers basic data analytics, statistical predictive modelling and machine learning through various practical examples and projects.
Best R training in Bangalore and Delhi NCR, for candidates who do not have programming background but want to acquire job oriented practical skills on a prominent open source Data Science platform. This Data Science R course is also available through live online and self-paced video based mode as well.
You may also check for amazing value combo course Data Science Specialization to learn Data Science & Machine Learning using Python & R.
Course Curriculum
The course curriculum starts with R fundamentals, using it for data handling, manipulation and descriptive analytics. Next, you learn using R with databases and data visualization. This is followed by statistical analysis, regression modelling and gardually move to learning important machine learning concepts and techniques.
Building Blocks
Building Blocks
- Introduction to Bridge Course & Analytics Software’s Basic Excel
- Basic Programming Elements
- Introduction to Basic Statistics
- RDBMS & SQL (Basics)
- Introduction to Analytics & Data Science
- Introduction to Mathematical Foundations
R for Data Science
Data Importing/Exporting
- Introduction R/R-Studio – GUI
- Concept of Packages – Useful Packages (Base & Other packages)
- Data Structure & Data Types (Vectors, Matrices, factors, Data frames, and Lists)
- Importing Data from various sources
- Exporting Data to various formats
- Viewing Data (Viewing partial data and full data)
- Variable & Value Labels – Date Values
Data Manipulation
- Creating New Variables (calculations & Binning)
- Dummy variable creation
- Applying transformations
- Handling duplicates/missing’s
- Sorting and Filtering
- Sub setting (Rows/Columns)
- Appending (Row/column appending)
- Merging/Joining (Left,right,inner,full,outer)
- Data type conversions
- Renaming
- Formatting
- Reshaping data
- Sampling
- Operators
- Control Structures (if, if else)
- Loops (Conditional, iterative loops)
- apply functions
- Arrays
- R Built-in Functions
- Text, Numeric, Date, utility
- R User Defined Functions
- Aggregation/Summarization
Data Analysis
- Introduction exploratory data analysis
- Descriptive statistics, Frequency Tables and summarization
- Uni-variate Analysis (Distribution of data)
- Bivariate Analysis (Cross Tabs, Distributions & Relationships)
Using R with Databases
- R and Relational Databases
- Connecting to Relational Databases using RJDBC and RODBC
- Database Design and Querying Data
- Modifying Data and Using Stored Procedures
- In-Database Analytics with R
Data Visualization with R
- Basic Visualization Tools
- Bar Charts/Histograms/Pie Charts
- Scatter Plots
- Line Plots and Regression
- Specialized Visualization Tools
- Word Clouds/ Radar Charts
- Waffle Charts/ Box Plots
- How to create Maps
- Creating Maps in R
- How to build interactive web pages
- Introduction to Shiny
- Creating and Customizing Shiny Apps
- Additional Shiny Features
Predictive Modeling in R
Dimensionality Reduction & Collaborative Filtering (e-learning)
- Dimensionality Reduction: Feature Extraction & Selection
- Collaborative Filtering & Its Challenges
Introduction to Statistics (e-learning)
- Basic Statistics – Measures of Central Tendencies and Variance
- Building blocks – Probability Distributions – Normal distribution – Central Limit Theorem
- Inferential Statistics -Sampling – Concept of Hypothesis Testing
- Statistical Methods – Z/t-tests (One sample, independent, paired), Anova, Correlations and Chi-square
Linear Regression: Solving regression problems (e-learning)
- Introduction – Applications
- Assumptions of Linear Regression
- Building Linear Regression Model
- Understanding standard metrics (Variable significance, R-square/Adjusted R-square, Global hypothesis, etc)
- Assess the overall effectiveness of the model
- Validation of Models (Re running Vs. Scoring)
- Standard Business Outputs (Decile Analysis, Error distribution (histogram), Model equation, drivers etc.)
- Interpretation of Results – Business Validation – Implementation on new data
Machine Learning vs Statistical Modeling & Supervised vs Unsupervised Learning (e-learning)
- Machine Learning Languages, Types, and Examples
- Machine Learning vs Statistical Modelling
- Supervised vs Unsupervised Learning
- Supervised Learning Classification
- Unsupervised Learning
Supervised Learning I (e-learning)
- K-Nearest Neighbors
- Decision Trees
- Random Forests
- Reliability of Random Forests
- Advantages & Disadvantages of Decision Trees
Supervised Learning II (e-learning)
- Regression Algorithms
- Model Evaluation
- Model Evaluation: Overfitting & Underfitting
- Understanding Different Evaluation Models
Unsupervised Learning (e-learning)
- K-Means Clustering plus Advantages & Disadvantages
- Hierarchical Clustering plus Advantages & Disadvantages
- Measuring the Distances Between Clusters – Single Linkage Clustering
- Measuring the Distances Between Clusters – Algorithms for Hierarchy Clustering
- Density-Based Clustering
Advance Analytics and Machine Learning in R (e-learning)
Important R packages for Machine Learning (caret, H2O, Randomforest, nnet, tm etc)
Fine tuning the models using Hyper parameters, grid search, piping etc.
Project - Consolidate Learnings
- Applying different algorithms to solve the business problems and bench mark the results
Time Series Forecasting: Solving forecasting problems
- Introduction – Applications
- Time Series Components( Trend, Seasonality, Cyclicity and Level) and Decomposition
- Classification of Techniques(Pattern based – Pattern less)
- Basic Techniques – Averages, Smoothening, etc
- Advanced Techniques – AR Models, ARIMA, etc
- Understanding Forecasting Accuracy – MAPE, MAD, MSE, etc
Machine Learning -Predictive Modeling – Basics
- Introduction to Machine Learning & Predictive Modeling
- Types of Business problems – Mapping of Techniques – Regression vs. classification vs. segmentation vs. Forecasting
- Major Classes of Learning Algorithms -Supervised vs Unsupervised Learning
- Different Phases of Predictive Modeling (Data Pre-processing, Sampling, Model Building, Validation)
- Overfitting (Bias-Variance Trade off) & Performance Metrics
- Feature engineering & dimension reduction
- Concept of optimization & cost function
- Overview of gradient descent algorithm
- Machine Learning -Predictive Modeling – Basics
- Overview of Cross validation(Bootstrapping, K-Fold validation etc)
- Model performance metrics (R-square, Adjusted R-square, RMSE, MAPE, AUC, ROC curve, recall, precision, sensitivity, specificity, confusion metrics )
Segmentation: Solving segmentation problems
- Introduction to Segmentation & Role of ML
- Types of Segmentation (Subjective Vs Objective, Heuristic Vs. Statistical)
- Heuristic Segmentation Techniques (Value Based, RFM Segmentation and Life Stage Segmentation)
- Behavioral Segmentation Techniques (K-Means Cluster Analysis)
- Cluster evaluation and profiling – Identify cluster characteristics
- Interpretation of results – Implementation on new data
Unsupervised Learning: Segmentation
- Concept of Distance and related math background
- Expectation Maximization
- Hierarchical Clustering
- Spectral Clustering (DBSCAN)
- Principal component Analysis (PCA)
Supervised Learning: Decision Trees
- Decision Trees – Introduction – Applications
- Types of Decision Tree Algorithms
- Construction of Decision Trees through Simplified Examples; Choosing the “Best” attribute at each Non-Leaf node; Entropy; Information Gain, Gini Index, Chi Square, Regression Trees
- Generalizing Decision Trees; Information Content and Gain Ratio; Dealing with Numerical Variables; other Measures of Randomness
- Pruning a Decision Tree; Cost as a consideration; Unwrapping Trees as Rules
- Decision Trees – Validation
- Overfitting – Best Practices to avoid
Supervised Learning: Artificial Neural Networks (ANN)
- Motivation for Neural Networks and Its Applications
- Perceptron and Single Layer Neural Network, and Hand Calculations
- Learning In a Multi Layered Neural Net: Back Propagation and Conjugant Gradient Techniques
- Neural Networks for Regression
- Neural Networks for Classification
- Interpretation of Outputs and Fine tune the models with hyper parameters
- Validating ANN models
Supervised Learning: Ensemble Learning
- Concept of Ensembling
- Manual Ensembling Vs. Automated Ensembling
- Methods of Ensembling (Stacking, Mixture of Experts)
- Bagging (Logic, Practical Applications)
- Random forest (Logic, Practical Applications)
- Boosting (Logic, Practical Applications)
- Ada Boost
- Gradient Boosting Machines (GBM)
- XGBoost
Supervised Learning: Support Vector Machines
- Motivation for Support Vector Machine & Applications
- Support Vector Regression
- Support vector classifier (Linear & Non-Linear)
- Supervised Learning: Support Vector Machines
- Mathematical Intuition (Kernel Methods Revisited, Quadratic Optimization and Soft Constraints)
- Interpretation of Outputs and Fine tune the models with hyper parameters
- Validating SVM models
Supervised Learning: KNN
- What is KNN & Applications?
- KNN for missing treatment
- KNN For solving regression problems
- KNN for solving classification problems
- Validating KNN model
- Model fine tuning with hyper parameters
Supervised Learning: Naïve Bayes
- Concept of Conditional Probability
- Bayes Theorem and Its Applications
- Naïve Bayes for classification
- Applications of Naïve Bayes in Classifications
Text Mining & Analytics
- Taming big text, Unstructured vs. Semi-structured Data; Fundamentals of information retrieval, Properties of words; Creating Term-Document (TxD);Matrices; Similarity measures, Low-level processes (Sentence Splitting; Tokenization; Part-of-Speech Tagging; Stemming; Chunking)
- Text Mining & Analytics
- Finding patterns in text: text mining, text as a graph
- Natural Language processing (NLP)
- Text Analytics – Sentiment Analysis using R
- Text Analytics – Word cloud analysis using R
- Text Analytics – Segmentation using K-Means/Hierarchical Clustering
- Text Analytics – Classification (Spam/Not spam)
- Applications of Social Media Analytics
- Metrics(Measures Actions) in social media analytics
- Examples & Actionable Insights using Social Media Analytics
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Who Should Do?
Candidates from various quantitative backgrounds, who are not just looking for advance Excel training but also want to learn job-oriented Analytics & Reporting skills using MS Excel, VBA, MS-Access, SQL, and Power BI.
Job Roles
- Analytics Consultant
- Data Science Consultant
- Data Analyst
- Data Scientist
- Business Analyst
- Statistical Analyst
Key Skills
- Data Mining & Analysis
- Data Science Methods
- R Programming
- Supervised Learning Models
- Predictive Modelling
- Unsupervised Learning Models
- Statistical Analysis
- Check your eligibility
Capstone Projects
In addition to multiple case studies used for class sessions, course includes following assignments and projects for self-study and hands-on skills.
Assignments
- 3 Basic exercises ( Excel, SQL & Tableau)
- 4 case studies on Pandas for data munging, descriptive & visual analysis
- 2 exercises for statistical analysis
- 3 Analytics case studies using R
Projects
- Sports event analysis and reporting
- Sports equipment retail data analysis and visualization
- Network intrusion detection (Supervised Machine learning)
- Retail chain sales forecasting (Multivariate Time Series)
- Banking customer clustering (Unsupervised Machine Learning)
- Marketing & Sales data manipulation and analysis
- Peer group lending analysis & prediction (Regression Methods)
- Airlines data analysis and reporting
- Predicting credit card spend (Regression Methods)
- Consumer electronics pricing data analysis & visualization
Programming Tools and Languages
Training Modes
Classroom & Bootcamp
Skill mastery with immersive, hands-on learning guided by mentors. Opt for either our intensive weekday bootcamps or flexible weekend classes to suit your schedule. Experience firsthand the power of in-person mentorship, available in weekday classroom bootcamps, ensuring a rich, experiential learning environment.
Fully Interactive Live Online
Blend the dynamic experience of traditional classroom with engaging, real-time interactive sessions, carefully tailored to meet the demand of busy schedules. This innovative approach ensures effective learning, fostering a deeper understanding and retention of knowledge.
Classroom Blended with eLearning
Fuse the rich atmosphere of classroom instruction with the flexibility and accessibility of eLearning modules, meticulously integrated to accommodate learning preferences. This unique blend ensures an optimal learning experience, empowering participants to delve into subjects deeply.
Certification
Owing to our well established domain expertise and prestigious clientele in India and overseas, AnalytixLabs certification is highly regarded in the industry. Being India’s top ranked Data Science institute it is imperative that we uphold the sanctity of our certification process.
Certification is awarded after the fair evaluation of mandatory case studies, assignments, MCQs, and viva included in the course.
Certification must be completed within one year of course registration.
In case the assignments and projects are not up-to-the-mark, trainees are welcome to take help and support for improvisation. But no kind plagiarism will be tolerated during evaluation.
Our objective is to ensure that trainees get vital hands-on experience so that they are well-prepared for job interviews along with a performance at their jobs.
Course Fees
Select the course you want to Enroll:
Classroom & Bootcamp
₹ 30000 + taxes
EMI starts @ 6496
EMI Options
*Pay in easy EMIs starting at INR ₹6387 per month.
Skill mastery with immersive, hands-on learning guided by mentors. Opt for either our intensive weekday bootcamps or flexible weekend classes to suit your schedule. Experience firsthand the power of in-person mentorship, available in weekday classroom bootcamps, ensuring a rich, experiential learning environment.
- Fees payable in up to 3 installments
- 12 months EMI available at 0% interest (though education financing partners).
- Cost-effective courses with high ROI, making it worth every penny you invest.
Fully Interactive Live Online
₹ 25000 + taxes
EMI starts @ 6496
EMI Options
*Pay in easy EMIs starting at INR ₹6387 per month.
Blend the dynamic experience of traditional classroom with engaging, real-time interactive sessions, carefully tailored to meet the demand of busy schedules. This innovative approach ensures effective learning, fostering a deeper understanding and retention of knowledge.
- Fees payable in up to 3 installments
- 12 months EMI available at 0% interest (though education financing partners).
- Cost-effective courses with high ROI, making it worth every penny you invest.
Blended eLearning
₹ 25,000+ taxes
EMI starts @ 6496
EMI Options
*Pay in easy EMIs starting at INR ₹6387 per month.
Fuse the rich atmosphere of classroom instruction with the flexibility and accessibility of eLearning modules, meticulously integrated to accommodate learning preferences. This unique blend ensures an optimal learning experience, empowering participants to delve into subjects deeply.
- Fees payable in up to 3 installments
- 12 months EMI available at 0% interest (though education financing partners).
- Cost-effective courses with high ROI, making it worth every penny you invest.
Career Support
Profile Building
A team of seasoned professionals will provide you personalized help for preparing CV and online profiles, based on your educational background and experience.
Interview Preparation
This will be followed by one to one interview preparation along with mock interviews (if required).
Job Referrals
We get different types of job requirements from various organizations, our clients, HR consultants, and a large pool of AnalytixLabs’ alumni working in various companies.
Continuous Support
There will be continuous support from our side for as long as you need it. Most of our students do get multiple interview calls and good career options based on the skills they learn during the course.
How To Apply
select your course, complete the registration form, and make payment.
Submit your application
Submit your application by filling out the form, providing necessary details, and clicking submit to complete the process.
Give a selection test
Take the selection test to demonstrate your skills and knowledge. This will help us assess your readiness for the course.
Reserve your seat
Reserve your seat today to secure your spot in the course. Act fast, as spaces are limited and fill quickly!
Payment
Complete your payment to finalize your enrollment in the course. Choose your preferred payment method and follow the prompts to proceed.
What Students Say About Us?
True Stories, Transformative Career Experience
Piyush Ganar, Class of 2012 IIM Ahmedabad
(Director of Operations, Kenty.AI)
The course material is very easy to understand and the case studies were based on real time business problems. What I love the most about Sumeet and his team is that they never operated the institute like a typical commercial enterprise but more like a temple for learning. The gates of Alabs are always open for students for any kind of help and guidance. I would recommend ALabs to all.
Raajeev Kumar Sahu
(Senior Manager – Data Science, FinTech Startup)
I am from Advance Big Data Science course of Nov 2016 batch. The course was very structured and got real world problems to practice during the final case studies. It has also boosted my skills to start participating in the hackathons. Chandra sir was really helped me in preparing my resume right from very beginning of the course. Also, the placement team was very well organized and connected to industry leaders so that people get the right opportunity right after completing the course. I really recommend this institute, who are interested to transition into analytics field.
Sumit Asthana
(Assistant Manager, Senior Data Scientist)
The rapid pace at which the institutes are mushrooming all over India and Facebook newsfeed being inundated with thousands of options for analytics training, there are only handful of training institutes or rather say only couple of places where they prepare you to foray into rapidly growing analytics vertical. I have been working into legacy systems for past 5 years and was quite apprehensive if I will ever break into data science successfully.
Surbhi Sultania
(Analytics Manager, Mastercard Data & Services)
I took three months Big Data training from Analytix labs. Before joining this course, I had so many questions and doubts that will this course be worthy enough but later I found myself lucky to join this program. All trainers are amazing and always ready to help. Live examples and Case Studies are given which helps in understanding the concept and hands on practice.Not only they help in quality in depth knowledge transfer but also tend to give right direction to your career.
Vibhu Gautam
(Professor of Practice – Computer Science, UPES)
I had been associated with AnalytixLabs since 2012. This has been possible because of the mentor ship and support you get from faculty there specially from Sumeet and Chandra.I did a Business Analytic course with a focus on Marketing Analytic. The course deliver had been great and most importantly you never feel like you are part of formal classroom teaching. You are mentored there at every step, from Code Errors to Understanding Statistics behind it.
Upcoming Batches
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- Get access to trial sessions to choose the most suitable course.
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Frequently Asked Questions
For how long class recordings and LMS access is available?
LMS and course access are available for one year. If needed, you can also repeat any number of classes you want in the next one year after course completion. Batch change policies will, however, apply in this case.
This is valid for AnalytixLabs content. In the case of co-branded global certification, the general duration of access to partner content is limited to 6 months.
In case required because any genuine reasons, the recordings access can be extended further for upto 1 year post the completion of one-year validity. Please note that given the constant changes in the Analytics industry, our courses continue to be upgraded and hence old courses might no longer hold relevance. Hence, we do not promise lifetime access just for marketing purposes.
What if I share my learning account details with a friend?
The sharing of LMS login credentials is unauthorized, and as a security measure, if the LMS is accessed by multiple places, it will flag in the system and your access to LMS can be terminated.
What if I miss a class?
Don’t worry! You will always get a recording for the class in your Learning Management System (LMS) account. Have a look at that and reach out to the faculty in case of doubts. All our live classes are recorded for self-study purpose and future reference, and these can also be accessed through our LMS. Hence, in case you miss a class, you can refer to the video recording and then reach out to the faculty during their doubts clearing time or ask your question at the beginning of the subsequent class.
You can also repeat any class you want in the next one year after your course completion. Batch change policies will, however, apply in this case.
Please note that in case you are not able to complete your course within one year of course validity, due to reasons at your end, limited support might be available post the completion of one year.
Can I download the recordings?
No. Our recordings can be accessed through your account on LMS or stream them live online at any point in time though.
Recordings are an integral part of AnalytixLabs’ intellectual property by Suo Jure. The downloading/distribution of these recordings in anyway is strictly prohibited and illegal as they are protected under the copyright act. In case a student is found doing the same, it will lead to an immediate and permanent suspension in the services, access to all the learning resources will be blocked, the course fee will be forfeited and the institute will have all the rights to take strict legal action against the individual.
The Business Analytics course at AnalytixLabs has been touted as one of India’s best business analytics certification courses since 2013 by various prestigious publications. Our course curriculum is designed in collaboration with industry experts and is updated according to the present market scenario.
For additional learning, our team of experts will continue to share free and easy-to-access resources and reading materials for easy upskilling. Meanwhile, you can read our blogs on Business Analytics and related topics.
How do I get the course completion certificate?
As part of the course, students get weekly assignments and module-wise case studies. Once all your submissions are received and evaluated well (without any plagiarism), the certificate shall be awarded. Without fairly submissions and evaluation of the assignments and projects so certificate shall be issued.
Please note that in case you are not able to complete the course within the one-year validity, AnalytixLabs might hold a mock interview/viva, apart from your submissions, before issuing the certificate.
Do you provide guaranteed placements?
No institute can provide a blanket placement guarantee unless they are doing so as a marketing gimmick!
We provide a job guarantee or money back for some selected learning tracks, mentioned in the course details wherever applicable, subjected to the stipulated requirements and terms. You can read more about it here.
For all other course modules meant for professional upskilling, our placement support is on a best effort basis, with you in the driving seat! We have transparently explained our career support in the course details. For us, it is on a best-effort basis but not time-bound – in some cases, students reach out to us even after three years for career guidance.
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