Business Analytics Training

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The course is intended to offer comprehensive knowledge of handling data and apply statistics in work for problem solving and decision making using real-time case studies. The course is designed with an assumption that participants have very basic knowledge about statistics, hence the course starts with basic descriptive statistics (Measure of central Tendency – Mean, Median & Mode) & ends with data modelling (Structures Equation Modelling). Sufficient theory offered in this course will help the candidates to understand the principles involved to develop in them the power of logical thinking, practical approach and exposition.

About Business Analytics Course

What is Business Analytics?

Business Analytics is the implementation that is applied to past data, efficient exploration of an Organisation's data emphasis on statistical analysis and processes to derive insights that can be used for future business planning.

Benefits of Business Analytics
  • Reduction in costs
  • Improved Efficiency
  • Better process in Decision making
  • Increased Revenues
  • Greater alignment with strategy

Key Features

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Business Analytics Training Course Content

Chapter 1:Business Statistics

  • Different types of data
  • Data summarization methods
  • Tables
  • Graphs
  • Charts
  • Histograms
  • Frequency distributions
  • Relative frequency measures of central tendency and dispersion
  • Box Plot
  • Chebychev’s Inequality
  • Basic probability concepts
  • Conditional probability
  • Bayes Theorem
  • Probability distributions
  • Continuous and discrete distributions
  • Sequential decision-making
  • Sampling and estimation
    • Estimation problems
    • Point and interval estimates
  • Hypothesis testing
    • Null and alternate hypotheses
    • Types of errors, Level of significance
    • Power of a test
    • ANOVA Test for goodness of fit
    • Non-parametric tests

Chapter 2: Predictive Analytics

  • Simple linear regression
    • Coefficient of determination
    • Significance tests
    • Residual analysis
    • Confidence and Prediction intervals
  • Multiple linear regression
    • Coefficient of multiple coefficient of determination
    • Interpretation of regression coefficients
    • Categorical variables, heteroscedasticity
    • Multi-collinearity
    • outliers
    • Auto regression and Transformation of variables
  • Logistic and Multinomial Regression
    • Logistic function
    • Estimation of probability
    • using logistic regression, Deviance, Wald Test, Hosmer Lemshow Test Forecasting: Moving average, Exponential smoothing, Trend, Cyclical and seasonality components, ARIMA (autoregressive integrated moving average).
    • Application of predictive analytics in retail, direct marketing, health care, financial services, insurance, supply chain, etc.

Chapter 3: Optimization Analytics

  • Introduction to Operations Research (OR)linear programming (LP)
  • formulating decision problems using linear programming
  • interpreting the results and sensitivity analysis
  • Multi-period LP models
  • Applications of linear programming in product mix
  • blending, cutting stock
  • transportation
  • transhipment
  • 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) and applications of GP and AHP in solving problems with multiple objectives
  • Non-linear programming, portfolio theory

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Chapter 4: Stochastic Models

  • Introduction to stochastic models
  • Markov models
  • Classification of states
  • Steady-state probability estimation
  • Brand switching and loyalty modeling
  • Market share estimation
  • Poisson process
  • Cumulative Poisson process
  • Applications of Poisson and cumulative Poisson in operations
  • marketing and insurance
  • Renewal theory
  • Applications of renewal theory in operations and supply chain management
  • Markov decision process
  • Applications of Markov decision process in sequential decision-making

Chapter 5: Advanced Analytics

  • Principal component analysis
  • Factor analysis
  • Conjoint analysis
  • Discriminant analysis
  • ARCH (autoregressive conditional heteroscedasticity) and GARCH (autoregressive conditional heteroscedasticity)
  • Monte Carlo simulation
  • Survival analysis and its applications
  • Life tables
  • KapMeier estimates
  • Proportional hazards
  • Predictive hazard modeling using customer history data
  • Six Sigma as a problem solving methodology
  • DMAIC and DMADV methodology
  • Six Sigma Tool Box
  • Seven quality tools
  • Quality function deployment (QFD)
  • Statistical process control
  • Value stream mapping, TRIZ
  • Classification and regression trees (CART)
  • Chi-squared automatic interaction detector (CHAID) Lean thinking
  • Lean manufacturing
  • Value stream mapping



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At the completion of the course, the applicants will be able to:

      • Understand the importance of business analytics in emerging market conditions
      • Analyze data using statistical methods
      • Learn data visualization and interpretation.
      • Learn decision-making tools / Operations Research techniques
      • Use advanced analytical methods to analyze complex scenarios under uncertainty
      • Manage business processes using statistical and analytical tools/techniques
      • Implement analytics in TCE (Total Customer Experience/Customer Survey Analysis), general management, operations and supply chain management.
      • Hands on experience with software such as R, Microsoft Excel & Minitab

Minimum educational qualifications required:

      1. Bachelor’s/Diploma in any branch of Engineering or Technology from a recognized Institution
      2. Bachelor’s degree (with mathematics at least up to the PUC level or equivalent) from a recognized University or Institution
      3. Candidate shall possess a minimum of one year’s working experience in IT/ITeS, Industrial, Commercial or Scientific Organizations

Faculty : Trained Statistics Professional from ISI (Indian Statistical Institute), Bangalore

Course Duration: 40 Hours

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Business Analytics Training in Credo Systemz – Reviews

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I joined Credo Systemz for my Business Analytics Course. They gave me excellent support while training. Trainer had depth knowledge on the course and an experienced Tutor. Best place to pursue our career among many other fake institutes. Thanks to the Credo Systemz.

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"I have attended the Business Analytics Course at Credo Systemz. Course content is excellent. I would recommend this course to anyone who wants to make a good career in Business Analytics. Instructor describes the course in detail. All Presentations are very good. You will get Real time Project Explanations with Case Studies. Thank you Credo Systemz"

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