Data Analytics using R

Learn the upcoming tool R to be used for Advanced Analytics and Machine Learning!

Acquire hand-on skills on Data Analytics using R - the golden boy of Data Science! Learn how to run analytics using R and also machine learning concepts for the same.

Course duration: 60 hours videos + Practice and Self-study

Who Should do this course?

Candidates from various quantitative backgrounds, like Engineering, Finance, Maths, Statistics, Business Management who want to head start their career in analytics.

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Course Duration 120 hours
Classes 19
Tools R/ R-Studio
Learning Mode Live Training
Next Batch02nd April, 2017

Introduction to R- environment

  • What is Data Science?
  • Analytics vs. Data warehousing, OLAP, MIS Reporting
  • Types of problems and business objectives in various industries
  • Critical success drivers
  • Different phases of a typical Analytics project
  • Understanding Heuristic vs. statistical models/analysis
  • Understanding classical techniques vs. machine learning techniques

Introduction to R- environment

  • The Workspace
  • Input/ Output
  • Useful Packages (Base & other packages)
  • Graphic User Interfaces (R studio)
  • Customizing Startup
  • Batch Processing and reusing results

Data Input & Output (Importing & Exporting)

  • Data Structure & Data Types (Vectors, Matrices, factors, Data frames,  and Lists)
  • Importing Data from various sources
  • Database Input (Connecting to database)
  • 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)
  • Operators (Using multiple operators)
  • Built-in Functions & User Defined Function
  • Control Structures (conditional statements, Loops, apply functions)
  • Sorting Data
  • Merging and Appending Data
  • Aggregating/summarizing Data
  • Reshaping Data
  • Sub setting Data
  • Data Type Conversions
  • Sampling
  • Renaming-formatting data
  • Handling duplicates/Missing values


  • Creating Graphs
  • Histograms & Density Plot
  • Dot Plots – Bar Plots – Line Charts – Pie Charts – Boxplots – Scatterplots

Basic Statistics (Exploratory Analysis)

  • Univariate Analysis
  • Bi-Variate Analysis (correlation, association)
  • Descriptive Statistics(central tendency/variance)
  • Frequency Tables /Summarization
  • Exploratory Analysis
  • Probability distributions
  • Sampling – Central Limit Theorem
  • Inferential statistics – Hypothesis testing
  • Statistical tests (t/z-test, ANOVA, chi-square)

Data Prep & Reduction techniques

  • Data Audit report creation and understanding
  • Need for data preparation
  • File preparation (Aggregation, merging, appending, type conversion etc)
  • Binning, Dummy and Derived variable creation
  • Standardization, Normalization
  • Outlier treatment/Flat-liners treatment
  • Missing values treatment (KNN, MI, clustering)
  • Dimension reduction - Factor Analysis – PCA

Customer Segmentation

  • Introduction to Segmentation
  • Heuristic segmentation (RFM, Life stage, value based etc)
  • Cluster analysis (K-means and Hierarchical)
  • Objective & subjective segmentation
  • Decision Trees (CHAID/CART/C5.0)
  • Cluster evaluation and profiling
  • Interpretation and application

Regression modeling

  • Basics of regression analysis
  • Approach: Model Estimation, MLE & Error Function,Optimization for finding parameters
  • Linear regression Model fitting
  • Logistic regression Model Fitting
  • Model Diagnostics – Decile Analysis – ROC Curves etc.
  • Interpretation of results

Predictive modeling

  • Multivariate Regression modelling
  • Cross-sell and Up-sell modelling
  • Churn prediction models and management
  • Credit risk score building, its interpretation and implementation

Time Series Forecasting

  • Introduction
  • Time Series components
  • Regression on Time
  • Modelling Seasonality as Deviation
  • Basic methods (pattern & pattern less)
  • Averages (MA, WMA, CMA etc)
  • Smoothening Techniques (Exponential)
  • Advanced Methods (ARIMA etc)
  • Goodness Metrics: MSE, MAPE, RMSE

Introduction to Machine Learning

  • Statistical learning vs. Machine learning
  • Major Classes of Learning Algorithms -Supervised vs Unsupervised Learning
  • Concept of Overfitting and Under fitting (Bias-Variance Trade off) & Performance Metrics
  • Types of Cross validation(Train & Test, Bootstrapping, K-Fold validation etc
  • Recursive Partitioning(Decision Trees)
  • Ensemble Models (Random Forest, Bagging & Boosting)
  • K-Nearest neighbours

Credit Card Customers Segmentation

Build an enriched customer profile using intelligent KPIs. Apply advanced algorithms like factor and cluster analysis for data reduction and customer segmentation based on the behavioral data.

Key Drivers for Customer Spending

The objective of this case study is to understand what's driving the total spend of credit card(Primary Card + Secondary card) and identify the key spend drivers . This will require candidates to apply OLS/ linear regression and follow end-to-end model building process

Proactive Attrition Management

Build a logistic regression based predictive model for a telecom service provider to identify churn indicators to predict and proactively manage the customer attrition.

Predicting Loan Default

Apply the logistic regression to identify the risky customers with high likelihood to default on loan repayment.

Time Series Forecasting

Use time series analysis to forecast the outbound passenger movement for next four quarters.

For how long are the recordings available to me?

One year post your course completion. Virtually the recordings are available to you for lifetime, but for judicious use of IT resources, the access to these recordings get deactivated post one year. However, this access can be extended upon request free of charge.

Can I download the recordings?

No. Our recordings can be accessed through your account on LMS or stream them live online at any point of time though.

Recordings are 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 copyright act. Incase 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, course fee will be forfeited and the institute will have all the rights to take strict legal action against the individual.

What if I share my LMS login 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.

Will I get a certificate in the end?

Yes. All our course are certified. As part of the course, students get weekly assignments and module-wise case studies. Once all you submissions are received and evaluated, the certificate shall be awarded.

Do you help in placements?

We follow a comprehensive and a self-sustaining system to help our students with placements. This is a win-win situation for our candidates and corporate clients. As a pre-requisite for learning validation, candidates are required to submit the case studies and project work provided as a part of the course (flexible deadline). Support from our side is continuous and encompasses help in profile building, CV referrals through our ex-students, HR consultants and companies directly reaching out to us.

We will provide guidance to you in terms of what are the right profiles for you based on your education and experience, interview preparation and conducting mock interviews, if required. The placement process for us doesn’t end at a definite time post your course completion, but is a long relationship that we will like to build.

Do you guarantee placements?

No institute can guarantee placements, unless they are doing so as a marketing gimmick! It is on a best effort basis.

In professional environment, it is not feasible for any institute to do so, except for a marketing gimmick. For us, it is on a best effort basis but not time – bound – in some cases students reach out to us even after 3 years for career support.

Do you have a classroom option?

No. For this course we don't provide a classroom option.

How can I reach out to someone if I have doubts post class?

Through the LMS, students can always connect with the trainer or even schedule one-to-one time over the phone or online. During the course we also schedule periodic doubts-clearing classes though students can also ask doubts of a class in the subsequent class.

LMS also has a discussion forum where a lot of your doubts might get easily answered.

Incase you are having a problem still, repeat the class and schedule one-to-one time with the trainer.

What is your refund policy?

  • Instructor Led Live online or Classroom - Within 7 days of registartion date and latest 3 days before batch start
  • Video-based - 2 days

What are the system requirements for the software?

There is no particular system requirement for this course since the tool required for this course (R) can easily be installed on almost every laptop with basic configuration available these days.

Can I pay in instalments?

No installment option is available for this course since it is a self-paced course.

Data analysis skills are indeed the most sought after ones but the task to acquire the appropriate skills is even a much harder challenge specifically because this is an unforeseen territory. I was fortunate enough to have found out AnalytixLabs and taken my 1st course in Data Science with SAS & R last year and it was a very valuable one. The hands on experience I gained through the practice exercises and instructors expertise are quite visible from the very first class. I took Big Data Certified course as well after the data science course and my trust in this organization has increased manifold. Now I am considering my third course with Analytixlabs and looking forward to it. It's worth investing your money & time here!!!!!!

- Ketan Walia (Analytics and Data Science Graduate Student, New Brunswick)
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