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 Mind-Map

Distinct Faculty

Faculty who are not just trainers but industry professional with in depth theoretical knowledge along with strong practical experience

Best Pedagogy

Every trainee is provided personal attention and grooming through small batch size and individual counselling. Practice assignments on real life business datasets part of the course for effective and applicable learning

Career Support

Leaders in source and train module Guiding your placement

Industry Endorsed

Have been leading training partners for major corporate houses. Trainings designed with experience and collaboration from business houses

Blended learning

Flexibility in learning is key at mindmap. Both Online as well classroom training for self-paced along with instructor led training upto 180hrs

Weekend Batches

Weekend batches available for advancing your career with minimal disruption to work schedule

Our Courses

Courses designed to help you find the right job in the field of Analytics

Analytics Foundation Training

  • Trend
  • Overview
  • Importance
  1. Descriptive  More..
    • Central Statistics
    • Central Limit Theorem
    • Summary Statistics
    • Basic Statistical Concept Building 
  2. 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 data-sets and drawing inferences
    • Estimation of population parameters from sample statistics
    • Confidence intervals and hypothesis testing within confidence intervals
    • Chi-squared test for association and goodness of fit estimation for categorical data
    • T-test
    • Z-Scores and
    • ANOVA (Manova) 
  1. 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, auto-correlation 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
    • Multi-variate 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 
  2. 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 Goodness-of-Fit
    • Area Under the ROC Curve
    • AIC
    • Weight of Evidence
    • VIF 
  3. Clustering  More..
    • Clustering : Principal component analysis
    • K-means clustering
    • Factor Analysis using PCA
    • DB-SCAN introduction Hierarchical clustering 
  4. Decision trees More..
    • Introduction to decision trees – simple and complex/multi-node
    • Variance
    • Gini Index
    • Entropy
    • Regression tree
    • Over-fitting
    • Pruning 
  5. 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 
  6.  

N.A., only for Professional and Data Scientist Training.

A comparative introduction to SAS and R

Comparative introduction/capabilities/strong-weak 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 Data-sets
  • 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
  1. Descriptive  More..
    • Central Statistics
    • Central Limit Theorem
    • Summary Statistics
    • Basic Statistical Concept Building 
  2. 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 data-sets and drawing inferences
    • Estimation of population parameters from sample statistics
    • Confidence intervals and hypothesis testing within confidence intervals
    • Chi-squared test for association and goodness of fit estimation for categorical data
    • T-test
    • Z-Scores and
    • ANOVA (Manova) 
  1. 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, auto-correlation 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
    • Multi-variate 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 
  2. 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 Goodness-of-Fit
    • Area Under the ROC Curve
    • AIC
    • Weight of Evidence
    • VIF 
  3. Clustering  More..
    • Clustering : Principal component analysis
    • K-means clustering
    • Factor Analysis using PCA
    • DB-SCAN introduction
    • Hierarchical clustering 
  4. Decision trees  More..
    • Introduction to decision trees – simple and complex/multi-node
    • Variance
    • Gini Index
    • Entropy
    • Regression tree
    • Over-fitting
    • Pruning 
  5. 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.

  1. K-Nearest neighbor classifier  More..
    • KNN introduction
    • Data Normalization
    • Examples and Application 
  2. Naïve Bayes  More..
    • Introduction
    • Examples and Application
    • Limitation 
  3. Random Forest  More..
    • Introduction
    • Examples and Application
    • Limitation
    • Features of Random Forests
    • How random forests work
    • he out-of-bag (oob) error estimate
    • Variable importance; Gini importance; Interactions;
    • Balancing prediction error
    • Detecting novelties 
  4. 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/strong-weak 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 Data-sets
  • 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
  1. Descriptive  More..
    • Central Statistics
    • Central Limit Theorem
    • Summary Statistics
    • Basic Statistical Concept Building 
  2. 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 data-sets and drawing inferences
    • Estimation of population parameters from sample statistics
    • Confidence intervals and hypothesis testing within confidence intervals
    • Chi-squared test for association and goodness of fit estimation for categorical data
    • T-test
    • Z-Scores and
    • ANOVA (Manova) 
  1. 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, auto-correlation 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
    • Multi-variate 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 
  2. 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 Goodness-of-Fit
    • Area Under the ROC Curve
    • AIC
    • Weight of Evidence
    • VIF 
  3. Clustering  More..
    • Clustering : Principal component analysis
    • K-means clustering
    • Factor Analysis using PCA
    • DB-SCAN introduction
    • Hierarchical clustering 
  4. Decision trees  More..
    • Introduction to decision trees – simple and complex/multi-node
    • Variance
    • Gini Index
    • Entropy
    • Regression tree
    • Over-fitting
    • Pruning 
  5. 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.

  1. K-Nearest neighbor classifier  More..
    • KNN introduction
    • Data Normalization
    • Examples and Application 
  2. Naïve Bayes  More..
    • Introduction
    • Examples and Application
    • Limitation 
  3. Random Forest  More..
    • Introduction
    • Examples and Application
    • Limitation
    • Features of Random Forests
    • How random forests work
    • he out-of-bag (oob) error estimate
    • Variable importance; Gini importance; Interactions;
    • Balancing prediction error
    • Detecting novelties 
  4. Gradient boosting machines  More..
    • Introduction to bagging and boosting
    • Understanding Underlyning Mathematics
    • Practical usage examples 
  5.  SVM (Support Vector Machine)  More..
    • Introduction
    • Examples and Application
    • Limitation
    • Linear Kernels
    • Cross validation
    • Radial kernels (all with examples) 
  6.  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/strong-weak 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 Data-sets
  • 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 Data-sets
  • 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
Flyier

Who is it for ?

IT/ Technology Professionals:

IT or Technology professionals who want to transform their career to  techno-analytics 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.

Mid-Career Professionals:

Mid-career 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.

Companies Hiring

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