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Data Science Training

  Date :
08 Jun - 14 Jul
(12 days)
  time :
09:00 PM - 12:00 AM IST
price :





At Root2learn, Data Science course online is designed in the way that it meets the increasing need of unique and skilled Data Scientists for many companies over the world. In this training, students will learn to merge different tools and techniques from Computer Science, Statistics, Data Visualization, and Social Sciences to solve the problems.

Importance of Data Science:

We could see vast change in Data Science over last few years and it is the biggest change in technology as our lives have become much easier than before. Data Science can crunch the large data and check all the possible chances to avoid the risk. So, the data science can be applied to any kind of business model to improve the business using statistics.

Many top corporations started investing in digital enterprises drastically and within 2 years, 50 times more data will be monitored by IT departments than what they are monitoring today. So, companies required the skilled persons to manage the large data without any problem.

At root2learn, we provide Data Science training online which helps you achieve job as a Data Scientist. This course helps you learn analytical techniques, such as data exploration and data visualization using real-time projects.



Data Science

Data Visualization and Summarization

Data Visualization and Interpretation

Different types of data

Data summarization and visualization methods

Tables, Graphs, Charts, Histograms

Frequency distributions

Relative frequency

Measures of central tendency and dispersion

Box Plot

Chebychev’s Inequality

Data visualization and Storytelling with Data

Basic probability concepts

Conditional probability

Bayes Theorem

Monty Hall Problem

Probability distributions

Continuous and discrete distributions

Sequential decision-making

Decision Tree

Sampling and estimation: Estimation problems, Point and interval estimates

Case Studies: 1. Central Parking Solutions Private Limited (IIMB Case);

  1. A Dean’s Dilemma: To Admit or Not to Admit (IIMB Case)

Descriptive Statistics

Introduction to Advanced Data Analytics

Statistical inferences for various Business problems

Types of Variables

Measures of central tendency


Variable Distributions



Normal Distribution and Properties


Exploratory Data Analysis


Probability Theory

Bayes Theorem

Random Variables

Cumulative Distribution Function

Continuous Distributions




Central limit Theorem

Monte Carlo Method


Kernel Density Estimate





Euclidean Distance

Data quality and outlier treatment

Outlier treatment with robust measurements (median)

Outlier treatment with central tendency Mean

Replacing with series means or median values

Z score Calculation

Data Normalization

Sampling and estimation

Test of Hypothesis

Null/Alternative Hypothesis formulation

Type I and Type II errors

One Sample TTEST

Paired TTEST

Independent Sample TTEST

Analysis of Variance (ANOVA),


Chi Square Test (Non Parametric Tests)



Wilcoxon, McNemar test

Data Preparation and Quality Check

Data Validation and Data Imputation

Proc Univariate techniques analysis (SAS)

Q-Q probability plots

Cumulative frequency (P P) plots

Explorer analysis (SPSS)

Steam and leaf analysis

Kolmogorov Smirnov test

Shapiro Wilks test


Data Transformation

Log transformation (s)

Arcsine transformation

Box- Cox transformation

Square root transformation

Inverse transformation

Predictive Analytics

Simple linear regression

Coefficient of determination

Significance tests

Residual analysis

Confidence and Prediction intervals

Multiple linear Regression

Coefficient of determination

Interpretation of regression coefficients

Categorical variables




Auto regression and Transformation of variables

Regression Model Building

Logistic and Multinomial Regression

Logistic function

Estimation of probability using logistic regression


Wald Test

Hosmer Lemshow Test

Classification table

Gini co-efficient


Moving average

Exponential smoothing

Casual Models

“Application of predictive analytics in retail, direct marketing, health care, financial services, insurance, supply chain, etc.”

Case Studies: Pricing of players in the Indian Premier League (IIMB Case)

Colonial Broadcasting Company (HBS Case)

Pedigree vs Grit: Predicting Mutual Fund Manager Performance (Kellogg Case)

Breaking Barriers – Micro-Mortgage Analytics (IIMB Case)

A Game of Two Halves: In-Play betting in Football (IIMB Case)

Optimization Analytics (Prescriptive Analytics)


Introduction to Operations Research (OR)

Linear programming (LP)

Formulating decision problems using linear programming

Interpreting the results and sensitivity analysis

Concepts of shadow price and reduced cost

Multi-period LP models

“Applications of linear programming in product mix, blending, cutting stock, transportation, transshipment, assignment, scheduling, planning and revenue management problems.”

Network models and project planning.

Integer Programming (IP) problems

Mixed-integer and Zero-one programming

Applications of IP in capital budgeting, location decisions, contracts

Multi-criteria decision making (MCDM) techniques

Goal Programming (GP) and analytic hierarchy process (AHP)

Applications of GP and AHP in solving problems with multiple objectives

Non-linear programming, portfolio theory

Case Studies: 1. Merton Truck Company (HBS Case),

  1. Supply Chain Optimization at Madurai Aavin Milk Dairy (IIMB Case)
  2. Red Brand Canners (Stanford Case)

Stochastic Models (Marketing and Retail Analytics)

Introduction to stochastic models

Markov models

Classification of states

Steady-state probability estimation

Brand switching and loyalty modeling

Market share estimation in the short and long run

Poisson process

Cumulative Poisson process

Applications of Poisson and cumulative Poisson in operations, marketing and insurance

Measuring effectiveness of retail promotions, warranty analytics

Renewal theory, Applications of renewal theory in operations and supply chain management

Markov decision process, Applications of Markov decision process in sequential decision making

Case Studies: 1. Browser Wars: Microsoft Vs Netscape (Darden Case)

  1. Consumer choices between house brands and national brands in detergent purchase at Reliance Retail (IIMB Case)”
  2. MNB ONE Credit card Portfolio (Darden Case).

Market Research and Operations Analytics

Principal component analysis


Factor analysis

Conjoint analysis

Discriminant analysis

“ARCH (autoregressive conditional heteroscedasticity)

ARCH (Generalized autoregressive conditional heteroscedasticity)”

Monte Carlo simulation

Supply chain analytics

Classification and regression trees (CART)

Chi-squared automatic interaction detector (CHAID)

Statistical process control, Value stream mapping, TRIZ

Case Studies: 1. Apollo Hospitals: Differentiation through Hospitality (IIMB Case)

  1. Dean’s Dilemma: To Admit or Not to Admit (IIMB Case)
  2. Dosa King – A Standardized Masala Dosa for Every Indian (IIMB Case)
  3. Delivering Doors in a Window – Supply Chain Management at Hindustan Aeronautics Limited (IIMB Case)

Analytics in Finance and Insurance

“Dynamic pricing and revenue management, high dimensional data analysis, financial data analysis and prediction”

Survival analysis and its applications:

Life tables

KapMeier estimates

Proportional hazards

Predictive hazard modeling using customer history data

Analytics in finance, discounted cash flows (DCF), Profitability analysis

Asset performance:

Sharpe ratio, Calmar ratio, Value at risk (VaR), Brownian motion process, Pricing options and Black–Scholes formula, Game theory

Insurance loss models: Aggregate loss models, discrete time ruin models, Continuous time ruin models

Predictive Modeling & Diagnostics

Correlation – Pearson, Kendall

SLR Regression

MLR Regression

Residual analysis

Auto Correlation

VIF Analysis

Indexing Eigen Value interpretation



Stepwise regression

Transformation of variables

Logistic Regression Analysis


Discriminant and Logit Analysis

Multiple Discriminant Analysis

Stepwise Discriminant Analysis Binary

Logit Regression

Estimation of probability using logistic regression, Wald Test

Hosmer Lemshow

Advanced Analysis

Factor Analysis

Introduction to Factor Analysis – PCA

Reliability Test

KMO MSA tests

Eigen Value Interpretation

Rotation and Extraction

Varimix Models

Principle component analysis

Conformity Factor Analysis

Exploitary Factor Analysis

Cluster Analysis

Introduction to Cluster Techniques

Distance Methodologies

Hierarchical and Non-Hierarchical Procedures

K Means clustering

Wards Method

Conjoint Analysis

Statistics and terms Association with Conjoint Analysis

Assumption and limitation of conjoint analysis

Hybrid Conjoint Analysis

Time Series Forecasting

Introduction to Time Series Data

Visualizing Time Series data

Data Exploration

Intro to AR, MA, ARMA, ARIMA Models

Smoothing and annual Time series

Time series forecasting for seasonal data

Multiplicative Models

Additive Models

Case studies with ARIMA Models (Using SAS & R)


Loss Forecasting & Portfolio Monitoring

Introduction to Survival Analysis

Using Survival Models to build Credit Baselines

Loss forecasting

Portfolio and monitoring &strategy

Introduction to Risk Management Practices by Bank

Overview of Scorecard’s

Stages of Scorecard Development

Types of Scorecards

Scorecard Development Project

Scorecard Development Project planning

Roles of Responsibilities

Data Review and Project Parameters

Project plan

Scorecard Development Preliminaries

Data Exclusion

Variable selection

Target Variable definition

Data Description

Scorecard Development

Missing Value Check

Exploratory Data Analysis

Correlation Analysis

Assigning predictive power


Model development

Model Selection

Model finalizations

Model validation

Variable Selection using Weight of Evidence & Information Value

Scorecard Development using grouped variables

Concept of variable grouping

Computing Weight of Evidence (WOE) and Information Values

Model development with WOE Variables

Reject Inference


Introduction to Reject Inference

Reject inference techniques

Strategies developed with Reject Inference

Performing reject inference and remodeling on SAS

Model Validations, Finalization and Uses

Introduction to Scaling

Finalize models/Model selection based on performance stats

Model Validations

Scorecard Management reports and documentation

Data Mining

Data Mining

Data partition (Training, Validating Testing)

Data Explore

Data Testing

Data Transform

Linear Model

SVM Model

Tree Analysis

RandomForest Analysis

Model Evaluation


Lift Curve


Error/ Confusion matrics

Text Mining

Vocabulary Mapping

Classify Text

Using NLTK

Feature Extraction

Market Basket Analysis

Association Rules

Support Vector Machines

Term Frequency and Weight

Term Document Matrix


Text Analysis

Named Entity Recognition


Business Analytics


Introduction and Data Analytics

Linear Regression

Logistic Regression

Decision Tree and Clustering

Time Series Modeling

Logistic Regression

Market Basket Analysis

Cross Sell Model

Market Mix Modeling

Churn Analytics

Buy till You Die Model

Customer Lifetime Value Analysis

Telecom Model to Estimate Bill

Machine Learning

Numerical and Categorical var

Supervised and Unsupervised Learning

Concepts, Inputs and Attributes



Bias and Variance

Trees and Classification

Decision trees


Naive Bayes Classifiers

K-Nearest Neighbor

Logistic and Linear Regression

Ranking and Preceptor

Hierarchical and K-Means Clustering

Neural networks

Sentimental Analysis

Collaborative Filtering


Vocabulary mapping

Linear Regression with One Variable

Model and Cost Function

Parameter Learning

Linear Regression with Multiple Variables


Environment Setup Instructions

Multivariate Linear Regression

Computing Parameters Analytically

Logistic Regression

Classification and Representation

Logistic Regression Model

Multiclass Classification


Solving the Problem of over fitting

Neural Networks: Representation


Neural Networks


Neural Networks: Learning

Cost Function and Back propagation

Back propagation in Practice

Application of Neural Networks

Advice for Applying Machine Learning

Evaluating a Learning Algorithm

Bias vs. Variance

Machine Learning System Design

Building a Spam Classifier

Handling Skewed Data

Using Large Data Sets


Support Vector Machines

Large Margin Classification


SVMs in Practice

Unsupervised Learning


Dimensionality Reduction


Principal Component Analysis

Applying PCA

Anomaly Detection

Density Estimation

Building an Anomaly Detection System

Multivariate Gaussian distribution (Optional)

Recommender Systems

Predicting Movie Ratings

Collaborative Filtering

Low Rank Matrix Factorization

Natural language Processing (NLP)

Language modeling

Hidden Markov models

Basic Text Processing

Minimum Edit Distance

Language Modeling

Spelling Correction

NLP Tasks and Text Similarity


Syntax and Parsing

Language Modeling and Word Sense Disambiguation

Part of Speech Tagging and Information Extraction

Text Summarization

Collocations and Information Retrieval

Sentiment Analysis and Semantics

Discourse, Machine Translation, and Generation

Tagging problems

Probabilistic context-free grammars

Parsing problem

Statistical approaches to machine translation

Log-linear models and their application to NLP problems

Unsupervised and semi-supervised learning in NLP

Deep Learning

Intro to Deep Learning

Simple Word Vector representations: word2vec, GloVe

Advanced word vector representations: language models, softmax, single layer networks

Neural Networks and back propagation — for named entity recognition

Project Advice, Neural Networks and Back-Prop (in full gory detail)

Practical tips: gradient checks, over fitting, regularization, activation functions, details

Introduction to Tensor flow

Recurrent neural networks — for language modeling and other tasks

GRUs and LSTMs — for machine translation

Recursive neural networks — for parsing

Recursive neural networks — for different tasks (e.g. sentiment analysis)

Convolutional neural networks — for sentence classification

The future of Deep Learning for NLP: Dynamic Memory Networks

Machine learning and artificial neural networks

Generative models

Restricted Boltzmann Machine

Contrastive Divergence algorithm

Deep Belief Network

Back propagation algorithm

Logistic regression and Soft max regression

Unsupervised pertaining in deep neural networks


Regularization in neural networks


Convolutional Neural Networks

Invariance, stability

Variability models (deformation model, stochastic model).

Scattering networks

Group Formalism

Supervised Learning: classification

Properties of CNN representations: invertibility, stability, invariance

Covariance/invariance: capsules and related models.

Connections with other models: dictionary learning, LISTA.

Other tasks: localization, regression.

Embeddings (DrLim), inverse problems

Extensions to non-euclidean domains

Dynamical systems: RNNs.

Deep Unsupervised Learning

Auto encoders (standard, denoising, contractive, etc)

Variational Autoencoders

Adversarial Generative Networks

Maximum Entropy Distributions

Non-convex optimization for deep networks

Stochastic Optimization

Attention and Memory Models

Artificial Intelligence



Agents: Perception, Decisions, and Actuation

Search and Planning

Uninformed Search (Depth-First, Breadth-First, Uniform-Cost)

Informed Search (A*, Greedy Search)

Heuristics and Optimality

Constraint Satisfaction Problems

Backtracking Search

Constraint Propagation (Arc Consistency)


Exploiting Graph Structure

Game Trees and Tree-Structured Computation

Minimax, Expectimax, Combinations

Evaluation Functions and Approximations

Alpha-Beta Pruning

Decision Theory

Preferences, Rationality, and Utilities

Maximum Expected Utility

Markov Decision Processes

Policies, Rewards, and Values

Value Iteration

Policy Iteration

Reinforcement Learning

TD/Q Learning



Geospatial Analysis

GIS Principles and Technology

Mapping Science

Principles of Spatial Analysis

Representation, Structures and Algorithms

Airborne Data Acquisition

Climate Modeling

Geo demographics and Population Geography

GIS Design

Network and Locational Analysis

Spatial Decision Support Systems

Spatio-temporal Analysis and Data Mining

Surface Water Modeling

Terrestrial Carbon: Modeling and Monitoring

Web and Mobile GIS

Big Data analysis


Introduction to big data

Sources of big data

Hadoop distributed file system

Statistical Analysis of Big Data

Tools: R, SPSS, SAS, SAS Enterprise Miner, WEKA, Excel

** Hadoop and Tableau Training without any Additional charges**

Why Learn Data Science?

Data Science has created a lot of impact in recent days in all the industries and almost every business needs it now. It is said that Data Scientists is declared as the ‘best job of the year’ 2016 by Glassdoor, which is a popular reviews website. Here are the more reasons you should know before joining Data Science certification training online


The demand for the Data Scientist jobs is already high and it is expected that it will even increase to one-third of the global market in coming years. Many small to big companies are looking for skilled Data Scientists who can understand and synthesize the data and makes decisions that helps improving the business.

Job Opportunities:

As the requirement for Data Scientists is increasing in all the organizations, the job opportunities are also increasing. Data Science certification training online helps people to get the job in data Science. There are many recruiters from various companies searching for skilled Data Scientists which is very hard to find. So, there is no doubt, learning Data Science will fetch your dream job in no time.

High Pay:

As the demand is high and not many skilled data Scientists are found easily, companies are offering high pay even for freshers. Also, the annual pay for Data Scientists is 50% more than other IT professionals. If you have done Data Science certification training, you have more chances to get hired by top companies, no matter if you are a fresher, experienced or an expert.


  • Knowledge in Python coding
  • In-depth knowledge on SAS/R
  • Skills in SQL database coding
  • Sound knowledge on Machine learning

Why Root2learn for Data Science Certification Training:

Root2learn is one of the best institutes for Data Science certification training online and in-class. You may have heard about many training institutes that provide Data Science training, but what makes us unique is our 100% dedication to make people reach their destination. Here are some reasons why you should choose Root2learn for Data Science training online over others.

Expert Trainers:

At Root2learn, we will always hire the trainers who have great hands-on experience on the subject and excellent teaching skills. We believe that trainers should be friendly with all the students and help them reach their goal by understanding their standards. We have the trainers who will take individual care of the students to make them understand each and every concept of the course theoretically and practically as well, which helps them get their dream job easily.

Classroom & Online Training:

Root2learn provides both classroom and online Data Science training and the students can choose as per their convenience. In classroom training, you can listen to the lectures in a peaceful environment, communicate with your fellow batch-mates, and can interact with the trainer directly. In online training, you will miss the interaction with your batch-mates directly, but there is a forum to get connected with them.

Job-oriented Training:

At Root2learn, we will provide job-oriented Data Science certification training that helps students to crack their dream job in a top company easily. We will provide complete study material, Assignments, and also conduct mock interviews to prepare them for the interviews.

Live Project:

To get a job in your dream company, practical sessions are much more important than the theoretical class. You will have practical classes where you have to do a live project on Data Science applications. This will be very useful when you are going for an interview and even after getting the job. Mentioning of the ‘live project’ in your resume definitely adds value.

Who can attained:

Data science career opportunities are on the rise, and it is quickly becoming a must-know technology for the following professionals:
  • Software Developers and Architects
  • Analytics Professionals
  • Data Management Professionals
  • Business Intelligence Professionals
  • Aspiring Data Scientists
  • Graduates looking to build a career in Big Data Analytics
  • Anyone interested in Data Analytics


The institute that offers a normal Data Science course will not be pricey but we are sure, it adds no value as there are many people who are learning the course. Here, certification helps! At Root2learn, we provide Data Science certification training that helps you get the job easily whereas other people who have no certification have to wait for years.

Lifetime Access:

At root2learn, we provide live classes for our students and you can have access to the live classes even after the course completion, for lifetime so that it will be easy for you to learn or remember the subject anytime you want.

Tips & Techniques:

Being experienced, our experts also know different tips and techniques to learn the Data Science easily. They will teach you the simplest way to learn all the concepts and apply them practically. Also, our team will explain tips and tricks that you have to know to perform well in the interview.

This course will be useful for professionals in the following roles, among others:
  • Associate Project Managers
  • Project Managers
  • IT Project Managers
  • Project Coordinators
  • Project Analysts
  • Project Leaders
  • Senior Project Managers
  • Team Leaders
  • Product Managers
  • Program Managers
  • Project Sponsors
  • Professionals or students who aspire to become data scientists would benefit from this course.

How do you provide online training ?

The training would be provided over a web platform. It is the most demanded & modernized way of “Instructor Led Training” without the need for expensive travelling that can be attended from anywhere in the world. You can attained from your home.

Which option do I choose for training, Virtual or classroom training?

You can decide which one suitable for you:

Virtual classroom Classroom
Less Expensive More Expensive
Recorded video of same session to refer in future No, recorded video
Can attain from any place, internet ( 512 KBPS speed) and System required Need to go at training venue
Can attain from home or office or from other country No, have to stay in same city
Interactive session Interactive session
Interaction with global professionals Mostly local professionals
Flexi class pass, can attain as many class want in same fee One class
If miss any class can go through same training video to connect in next session, and ask if have any query or can attain in any batch If miss the class, will not able to attain same session
Gradually learning ( as training will go near about one month, so you can prepare with training) will get enough time to revise covered topics Some training will finished in 4 days, or within one week. So it will be more load and will not have enough time to revise covered topics
Highly expected trainer ( 23 years, 6 years training experience) May be have experienced trainer
Demo session ( past recorded video) Not available

What is Virtual classroom training?

Virtual classroom training for PMP Certification is training conducted via online live streaming of a class. The classes are conducted by a PMP certified trainer with more than 23 years of work and training experience. It is interactive session, you can asked the question to trainer and will also ask the question. it is one to one interaction. It is video conference type of training.

Is this live training, or will I watch pre-recorded videos?

All the classes are live. They are interactive sessions that enable you to ask questions and participate in discussions during the class time. We do, however, provide recordings of each session you attend for your future reference.

What tools do I need to attend the training sessions?

The tools you’ll need to attend training are fairly basic:
  • Windows: any version newer than Windows XP SP3
  • Mac: any version newer than OSX 10.6
  • Internet speed: Preferably faster than 512 Kbps
  • Headset, speakers, microphone: You’ll need headphones or speakers to hear clearly, as well as a microphone to talk to the others. You can use a headset with a built-in microphone, or separate speakers and microphone.

Where is the training held?

There is no training venue for Virtual classroom training. It is online live training you can  attained from your home by login at your system, for that we will provide you login id and password.
For classroom training you will get email at your registered email id as per your location.

What is 100% training quality guarentee?

If you are not happy with our training quality, inform us within 1st half of Training on First Day. We will refund your entire training fee with 7 working days.

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