About Analytics
Business Analytics domain is an everlasting technology which was initially implemented only in Financial Services, Banking, Pharmaceutical, etc but in recent times it has been realized that smart decision can only be taken thru data based analysis and research – Be a start up or a BIG MNC from any sector they are investing in huge amounts in gathering the available data and make smarter and efficient decisions.
What is it?
Analytics is the art and science of extracting valuable insight from the enormous noise of data. Analytics is a systematic application of statistics, computer programming and operations research for unearthing, interpreting, and communicating meaningful patterns and trend in data in order to make informed and intelligent decisions to gain competitive advantage.
How does it work?
In this age of information data is the biggest asset in an organization possess. The computational power along with the development in statistical techniques has helped in transforming data into invaluable knowledge guiding decisions and strategies delivering unprecedented performance improvements.
Who’s doing it?
 Analytics is a multidisciplinary field with its application in almost all frontiers.
 Study of genetical pattern for early detection of potential.
 Text analysis for understanding customer sentiments.
 Fraud detection and customer profiling by financial institutions
 Targeted Advertising and recommender selling on all online stores.
 Logistic organization like DHL and UPS use it for to design optimal route plans.
 Business organization use it for predicting sales and customer behaviors.
 With unlimited and unparalleled scope opportunity in Analytics is gigantic.
Why Analytics with MindMap
Our Courses
Courses designed to help you find the right job in the field of Analytics
Analytics Foundation Training
 Trend
 Overview
 Importance
 Descriptive More..
 Central Statistics
 Central Limit Theorem
 Summary Statistics
 Basic Statistical Concept Building
 Inferential Statistics More..
 Introduction to Inferential Statistics
 What is Inferential Statistics?
 Drawing inference from data examples
 Probability
 Correlation and Regression
 Fundamental Inferential Statistics
 Hypothesis Testing
 Working with multiple datasets and drawing inferences
 Estimation of population parameters from sample statistics
 Confidence intervals and hypothesis testing within confidence intervals
 Chisquared test for association and goodness of fit estimation for categorical data
 Ttest
 ZScores and
 ANOVA (Manova)
 Linear regression More..
 Introduction to linear regression, usage examples
 Regression equation and components
 Assumptions regarding linear regression
 Basics of Linear Regression
 Understanding concepts of Slope, Intercept & Error
 Calculation of parameters
 Validation of Regression model
 Creation of SAS/R dataset to be used for regression
 Examination of data prior to modeling
 Creating the regression model using relevant procedures in R & SAS
 Understanding the output of a fundamental regression procedure
 Common problems during model building and tests of a good model
 Creating regression and residual plots to analyze patterns
 Using data transformation to remedy lack of normal assumptions
 PROC UNIVARIATE, diagnostic statistics from REG (SAS)
 Getting inputs from the final regression equation
 Introduction to Multicollinearity, autocorrelation and outliers
 Using VIF to test for multicollinearity
 Using DW statistic test to test for multicollinearity
 Outlier identification using Cook’s D/RSTUDENT/DFFITS
 Testing model fit using F value and p value
 Multivariate Linear Regression, an extension of Simple Linear Regression
 Creation of Multivariate Regression model
 Comparison with Simple Linear Regression
 Validation of Model Heteroscedasticity concepts
 Normal Distribution Curve
 Logistic regression More..
 Introduction to Logistic Regression, usage examples
 The Likelihood Ratio Test
 Finding a Confidence Interval for β and π
 The Multiple Logistic Regression Model
 Fitting the Multiple Logistic Regression Model
 Interpretation of Coefficients
 Dichotomous Independent Variables
 Overview to the modelling process
 Data loading/variable labelling/data transformation
 Potential predictors variable selection/use visual tool to discover relationship pattern between predictors and target variable
 Transformation of variables and final model development
 Model validation
 Estimating Odd Ratios in the Presence of Interaction
 Assessing the Fit of the Logistic Regression Model
 Summary Measures of GoodnessofFit
 Area Under the ROC Curve
 AIC
 Weight of Evidence
 VIF
 Clustering More..
 Clustering : Principal component analysis
 Kmeans clustering
 Factor Analysis using PCA
 DBSCAN introduction Hierarchical clustering
 Decision trees More..
 Introduction to decision trees – simple and complex/multinode
 Variance
 Gini Index
 Entropy
 Regression tree
 Overfitting
 Pruning
 Time Series and Forecasting More..
 Introduction to Time Series Forecasting
 Assumptions
 Decomposition of Time Series
 Seasonality & Stationarity
 Forecasting Techniques:

 Moving Averages
 Exponential Smoothing Techniques
 Pattern Based Time series analysis
 Step AR Techniques
 Holt Winter Method
 ARIMA Model
 Box Jenkins Procedure
 Accuracy Analysis
 Method selection Process

N.A., only for Professional and Data Scientist Training.
A comparative introduction to SAS and R
Comparative introduction/capabilities/strongweak areas for SAS/R
Introduction of dataset to be used for regression demonstration
Setting up R and SAS and getting ready for classroom activity
 SAS
 R
 Dissecting Datasets
 Industry Case Study Simulation 1
 Interim Practical Assessment
 Final Practical Assessment
 Mock Interview
 Stress Interview Handling
 Group Discussion
 90 Hours
 12 Weeks
Analytics Professional Training
 Trend
 Overview
 Importance
 Descriptive More..
 Central Statistics
 Central Limit Theorem
 Summary Statistics
 Basic Statistical Concept Building
 Inferential Statistics More..
 Introduction to Inferential Statistics
 What is Inferential Statistics?
 Drawing inference from data examples
 Probability
 Correlation and Regression
 Fundamental Inferential Statistics
 Hypothesis Testing
 Working with multiple datasets and drawing inferences
 Estimation of population parameters from sample statistics
 Confidence intervals and hypothesis testing within confidence intervals
 Chisquared test for association and goodness of fit estimation for categorical data
 Ttest
 ZScores and
 ANOVA (Manova)
 Linear regression More..
 Introduction to linear regression, usage examples
 Regression equation and components
 Assumptions regarding linear regression
 Basics of Linear Regression
 Understanding concepts of Slope, Intercept & Error
 Calculation of parameters
 Validation of Regression model
 Creation of SAS/R dataset to be used for regression
 Examination of data prior to modeling
 Creating the regression model using relevant procedures in R & SAS
 Understanding the output of a fundamental regression procedure
 Common problems during model building and tests of a good model
 Creating regression and residual plots to analyze patterns
 Using data transformation to remedy lack of normal assumptions
 PROC UNIVARIATE, diagnostic statistics from REG (SAS)
 Getting inputs from the final regression equation
 Introduction to Multicollinearity, autocorrelation and outliers
 Using VIF to test for multicollinearity
 Using DW statistic test to test for multicollinearity
 Outlier identification using Cook’s D/RSTUDENT/DFFITS
 Testing model fit using F value and p value
 Multivariate Linear Regression, an extension of Simple Linear Regression
 Creation of Multivariate Regression model
 Comparison with Simple Linear Regression
 Validation of Model Heteroscedasticity concepts
 Normal Distribution Curve
 Logistic regression More..
 Introduction to Logistic Regression, usage examples
 The Likelihood Ratio Test
 Finding a Confidence Interval for β and π
 The Multiple Logistic Regression Model
 Fitting the Multiple Logistic Regression Model
 Interpretation of Coefficients
 Dichotomous Independent Variables
 Overview to the modelling process
 Data loading/variable labelling/data transformation
 Potential predictors variable selection/use visual tool to discover relationship pattern between predictors and target variable
 Transformation of variables and final model development
 Model validation
 Estimating Odd Ratios in the Presence of Interaction
 Assessing the Fit of the Logistic Regression Model
 Summary Measures of GoodnessofFit
 Area Under the ROC Curve
 AIC
 Weight of Evidence
 VIF
 Clustering More..
 Clustering : Principal component analysis
 Kmeans clustering
 Factor Analysis using PCA
 DBSCAN introduction
 Hierarchical clustering
 Decision trees More..
 Introduction to decision trees – simple and complex/multinode
 Variance
 Gini Index
 Entropy
 Regression tree
 Overfitting
 Pruning
 Time Series and Forecasting More..
 Introduction to Time Series Forecasting
 Assumptions
 Decomposition of Time Series
 Seasonality & Stationarity
 Forecasting Techniques
 Moving Averages
 Exponential Smoothing Techniques
 Pattern Based Time series analysis
 Step AR Techniques
 Holt Winter Method
 ARIMA Model
 Box Jenkins Procedure
 Accuracy Analysis
 Method selection Process
History of machine learning and current trends in industry.
What is machine learning: supervised and unsupervised learning.
What to expect and not to expect from machine learning possibilities.
 KNearest neighbor classifier More..
 KNN introduction
 Data Normalization
 Examples and Application
 Naïve Bayes More..
 Introduction
 Examples and Application
 Limitation
 Random Forest More..
 Introduction
 Examples and Application
 Limitation
 Features of Random Forests
 How random forests work
 he outofbag (oob) error estimate
 Variable importance; Gini importance; Interactions;
 Balancing prediction error
 Detecting novelties
 Gradient boosting machines More..
 Introduction to bagging and boosting
 Understanding Underlyning Mathematics
 Practical usage examples
A comparative introduction to SAS and R
Comparative introduction/capabilities/strongweak areas for SAS/R
Introduction of dataset to be used for regression demonstration
Setting up R and SAS and getting ready for classroom activity
 SAS
 R
 Dissecting Datasets
 Industry Case Study Simulation 1
 Industry Case Study Simulation 2
 Interim Practical Assessment
 Final Practical Assessment
 Mock Interview
 Stress Interview Handling
 Resume Preparation
 124 Hours
 16 Weeks
Data Scientist Certification
 Trend
 Overview
 Importance
 Descriptive More..
 Central Statistics
 Central Limit Theorem
 Summary Statistics
 Basic Statistical Concept Building
 Inferential Statistics More..
 Introduction to Inferential Statistics
 What is Inferential Statistics?
 Drawing inference from data examples
 Probability
 Correlation and Regression
 Fundamental Inferential Statistics
 Hypothesis Testing
 Working with multiple datasets and drawing inferences
 Estimation of population parameters from sample statistics
 Confidence intervals and hypothesis testing within confidence intervals
 Chisquared test for association and goodness of fit estimation for categorical data
 Ttest
 ZScores and
 ANOVA (Manova)
 Linear regression More..
 Introduction to linear regression, usage examples
 Regression equation and components
 Assumptions regarding linear regression
 Basics of Linear Regression
 Understanding concepts of Slope, Intercept & Error
 Calculation of parameters
 Validation of Regression model
 Creation of SAS/R dataset to be used for regression
 Examination of data prior to modeling
 Creating the regression model using relevant procedures in R & SAS
 Understanding the output of a fundamental regression procedure
 Common problems during model building and tests of a good model
 Creating regression and residual plots to analyze patterns
 Using data transformation to remedy lack of normal assumptions
 PROC UNIVARIATE, diagnostic statistics from REG (SAS)
 Getting inputs from the final regression equation
 Introduction to Multicollinearity, autocorrelation and outliers
 Using VIF to test for multicollinearity
 Using DW statistic test to test for multicollinearity
 Outlier identification using Cook’s D/RSTUDENT/DFFITS
 Testing model fit using F value and p value
 Multivariate Linear Regression, an extension of Simple Linear Regression
 Creation of Multivariate Regression model
 Comparison with Simple Linear Regression
 Validation of Model Heteroscedasticity concepts
 Normal Distribution Curve
 Logistic regression More..
 Introduction to Logistic Regression, usage examples
 The Likelihood Ratio Test
 Finding a Confidence Interval for β and π
 The Multiple Logistic Regression Model
 Fitting the Multiple Logistic Regression Model
 Interpretation of Coefficients
 Dichotomous Independent Variables
 Overview to the modelling process
 Data loading/variable labelling/data transformation
 Potential predictors variable selection/use visual tool to discover relationship pattern between predictors and target variable
 Transformation of variables and final model development
 Model validation
 Estimating Odd Ratios in the Presence of Interaction
 Assessing the Fit of the Logistic Regression Model
 Summary Measures of GoodnessofFit
 Area Under the ROC Curve
 AIC
 Weight of Evidence
 VIF
 Clustering More..
 Clustering : Principal component analysis
 Kmeans clustering
 Factor Analysis using PCA
 DBSCAN introduction
 Hierarchical clustering
 Decision trees More..
 Introduction to decision trees – simple and complex/multinode
 Variance
 Gini Index
 Entropy
 Regression tree
 Overfitting
 Pruning
 Time Series and Forecasting More..
 Introduction to Time Series Forecasting
 Assumptions
 Decomposition of Time Series
 Seasonality & Stationarity
 Forecasting Techniques
 Moving Averages
 Exponential Smoothing Techniques
 Pattern Based Time series analysis
 Step AR Techniques
 Holt Winter Method
 ARIMA Model
 Box Jenkins Procedure
 Accuracy Analysis
 Method selection Process
History of machine learning and current trends in industry.
What is machine learning: supervised and unsupervised learning.
What to expect and not to expect from machine learning possibilities.
 KNearest neighbor classifier More..
 KNN introduction
 Data Normalization
 Examples and Application
 Naïve Bayes More..
 Introduction
 Examples and Application
 Limitation
 Random Forest More..
 Introduction
 Examples and Application
 Limitation
 Features of Random Forests
 How random forests work
 he outofbag (oob) error estimate
 Variable importance; Gini importance; Interactions;
 Balancing prediction error
 Detecting novelties
 Gradient boosting machines More..
 Introduction to bagging and boosting
 Understanding Underlyning Mathematics
 Practical usage examples
 SVM (Support Vector Machine) More..
 Introduction
 Examples and Application
 Limitation
 Linear Kernels
 Cross validation
 Radial kernels (all with examples)
 Neural networks: More..
 Introduction to neural networks and training neural networks
 Error and gradient calculation, backpropagation
 Application of neural networks
 Deep learning
 Practical usage examples
A comparative introduction to SAS and R
Comparative introduction/capabilities/strongweak areas for SAS/R
Introduction of dataset to be used for regression demonstration
Setting up R and SAS and getting ready for classroom activity
 SAS
 R
 Excel
 Dissecting Datasets
 Industry Case Study Simulation 1
 Industry Case Study Simulation 2
 Industry Case Study Simulation 3
 Industry Case Study Simulation 4
 Interim Practical Assessment
 Final Practical Assessment
 Mock Interview
 Stress Interview Handling
 Resume Preparation
 Group Discussion
 180 Hours
 23 Weeks
Analytics with Excel
 Trend
 Overview
 Importance
 Excel
 Dissecting Datasets
 Industry Case Study Simulation 1
 Industry Case Study Simulation 2
 Industry Case Study Simulation 3
 Industry Case Study Simulation 4
 Interim Practical Assessment
 Final Practical Assessment
 Mock Interview
 Stress Interview Handling
 Group Discussion
 22 Hours
 3 Weeks
Who is it for ?
IT/ Technology Professionals:
IT or Technology professionals who want to transform their career to technoanalytics roles in the industry by adding business analysis as a skill set.
Business Leaders/ Entrepreneurs:
Business leaders or Entrepreneurs who want to take advantage of on the techniques and tools expertise to scale their business units to new heights.
MidCareer Professionals:
Midcareer professionals Looking to improve competencies and launch their career into greater roles guided by data driven Decisions.
Freshers and budding professionals:
Freshers and budding professionals to make the right beginning in their career.
Building Domains to Enhance Careers!
Find out if you should sign up  talk to our career advisor
Practical Projects
Marketing Analysis
Predict future sales volume and pattern with the help of Analytics to manage. Supply change and product dynamics better with application of linear regression.
Sentiment Analysis
Text Analytics to understand customer sentiment and emotions better. Used for improvement in product features and categorization
Targeted Marketing
Analytics used to determine which is the best channel for marketing and the cost allocation based on value, determine the optimal spend
Risk Analytics
Application of Analytics to determine the creditworthiness of customers and determination of credit score. whether they be issued a credit card or not.