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:
- Bachelor’s/Diploma in any branch of Engineering or Technology from a recognized Institution
- Bachelor’s degree (with mathematics at least up to the PUC level or equivalent) from a recognized University or Institution
- Candidate shall possess a minimum of one year’s working experience in IT/ITeS, Industrial, Commercial or Scientific Organizations
Course Duration: 40 Hours
Course Features
- Duration60 hours
- Skill levelAll level
- Batch Strength15
- AssessmentsYes
- Mock InterviewsYes
- Resume BuildingYes
- PlacementsYes
- Flexible TimingYes
- Fee InstallmentsYes
- LanguageTamil/English
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
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)
- SIPOC
- 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
You will be going through detailed 2 to 3 months of Business Analytics Hands-on training
- Detailed instructor led sessions to help you become a proficient Expert in Business Analytics.
- Build a Business Analytics professional portfolio by working on hands on assignments and projects.
- Personalised mentorship from professionals working in leading companies.
- Lifetime access to downloadable Business Analytics course materials, interview questions and project resources.
Credo Systemz - Velachery, Chennai
Call Us +91 9884412301
