DESCRIPTION
ABOUT THE COURSE
COURSE DESCRIPTION
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);
- 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
Dispersion
Variable Distributions
Probability
Distributions
Normal Distribution and Properties
Histograms
Exploratory Data Analysis
Histograms
Probability Theory
Bayes Theorem
Random Variables
Cumulative Distribution Function
Continuous Distributions
Skewness
Anova
Central limit Theorem
Monte Carlo Method
Estimate
Kernel Density Estimate
Regression
Covariance
Correlation
Causation
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),
MANOVA
Chi Square Test (Non Parametric Tests)
Kruskal-Wallis,
Mann-Whitney,
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
Heteroscedasticity
Multicollinearity
Outliers
Auto regression and Transformation of variables
Regression Model Building
Logistic and Multinomial Regression
Logistic function
Estimation of probability using logistic regression
Deviance
Wald Test
Hosmer Lemshow Test
Classification table
Gini co-efficient
Forecasting
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),
- Supply Chain Optimization at Madurai Aavin Milk Dairy (IIMB Case)
- 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)
- Consumer choices between house brands and national brands in detergent purchase at Reliance Retail (IIMB Case)”
- 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)
- Dean’s Dilemma: To Admit or Not to Admit (IIMB Case)
- Dosa King – A Standardized Masala Dosa for Every Indian (IIMB Case)
- 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
Homoscedasticity
Homogeneity
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
Sampling
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
ROC
Lift Curve
Sensitivity
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
UIMA
Text Analysis
Named Entity Recognition
Corpus
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
Classifier
Prediction
Bias and Variance
Trees and Classification
Decision trees
Boosting
Naive Bayes Classifiers
K-Nearest Neighbor
Logistic and Linear Regression
Ranking and Preceptor
Hierarchical and K-Means Clustering
Neural networks
Sentimental Analysis
Collaborative Filtering
Tagging
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
Regularization
Solving the Problem of over fitting
Neural Networks: Representation
Motivations
Neural Networks
Applications
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
Kernels
SVMs in Practice
Unsupervised Learning
Clustering
Dimensionality Reduction
Motivation
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
Dropout
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
Introduction
Overview
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
Exploration
Approximation
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
Hadoop
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
Demand:
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.
Prerequisite:
- 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:
- 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
Certification:
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.
- 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?
- 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.
