Course Description

About The Course

Optimization involves finding the best possible solution from multiple available solutions which is a cost-effective and high-performance solution. Optimization is used in solving multiple business problems and one of the core concept in advance analytics.

Some Of The Areas Where Optimization Is Used Widely Are

  • Crew member allocation in various industries
  • Airlines industries to schedule their cargo flight routes
  • Supply chain optimization
  • Pricing and Revenue optimization
  • Manufacturing sector for production
  • Transportation and logistics

Optimization techniques not only provide the best optimal solution but also provides feedback on how changing the objective function coefficient will change the optimal solution. Every industry faces optimization problem and different optimization techniques are used to solve different problems. Different optimization problems include linear optimization problem, combinatorial optimization problem, constrained optimization problem, multi-objective optimization problem, nonlinear optimization problem, etc.

Are you looking to build an optimized routing system and solve a transportation problem; Do you want to identify what is the right volume of spare parts that need to be manufactured based on demand, supply, resource availability; Do you want to know what is the right way to allocated resources to a project without any redundancy or do you want to know a simple trick to solve Sudoku puzzle, then Optimization is the right subject that you need to learn.

It is important that we understand which optimization technique needs to be used to solve a problem and also get a clear view on how to identify the objective function, decision variables and constraints to solve the problem correctly. We help you in learning different optimization techniques by using industry business scenarios and also teach widely used optimization tools like Solver, @Risk optimizer.

Data for a Data Scientist is what Oxygen is to Human Beings. This is also a profession where statistical adroit works on data – incepting from Data Collection to Data Cleansing to Data Mining to Statistical Analysis and right through Forecasting, Predictive modeling and finally Data Optimization. A Data Scientist does not provide a solution; they provide the most optimized solution of the lot. Gartner predicted in 2012 that Data Scientist & Business Analytics jobs will increase to the tunes of Millions by the end of 2015. This is very evident with the rise in job opportunities in various job portals. As a Data Scientist or an aspirant, you should not believe us. Go search for your own and confirm with facts and figures.

Course Curriculum

  • Geometry & Sensitivity analysis
  • Introduction to Linear Programming (LP) and Management Science applications
  • The geometry of linear programs and graphical solution of LPs
  • A systematic explanation of how to formulate LPs and then implement them using spreadsheets
  • LP sensitivity analysis
  • Applications and Duality
  • LP duality and optimality conditions
  • Application in retail operations and multi-period planning applications
  • Network Optimisation
  • Network Flow models and specifically discuss the transportation problem, assignment problem, and —-the max-flow problem with their applications
  • Binary variable modeling
  • The use of binary variables and modeling logical constraints
  • The fixed-charge problem
  • Other applications in finance and operations
  • Combinatorial optimization
  • Matching, spanning trees and others along with their application
  • Non-linear and Stochastic optimization
  • Basic mean-variance single period quadratic programming optimization model which was developed in the pioneering work of Markowitz
  • Solver
  • @Risk optimizer
  • Maths behind Optimization
    • Introduction to derivatives
    • Derivatives in optimization – Maxima & Minima
    • Application of optimization in arriving at Linear Least Squares
    • Gradient Descent Optimization
  • Linear Programming
    • Introduction to Linear programming
    • Formulating linear programming models
    • Solving linear programming models
    • Understand resource allocation problems
    • Understand cost-benefit analysis problems
  • Duality & other analysis
    • Decision variables, constraints & objective function
    • Duality problems
    • Sensitivity analysis
    • Network Analysis
    • Transportation, Shortest path, Maximal flow problems
    • Introduction to integer linear programming
    • Introduction to Non-linear optimization
  • Introduction to Probability
    • Review of probability
    • Conditional Probability
    • Bayes theorem
    • Permutations & Combinations
  • Introduction to Probability Distributions
    • Bernoulli
    • Binomial
    • Geometric
    • Negative Binomial
    • Poisson
    • Uniform Distribution
    • Triangular
    • Exponential
    • Normal
  • Introduction to Simulation
    • Basics of simulation
    • Statistical sampling
    • The case study on the application of simulation
  • Bidding
  • Marketing
    • Fitting distributions to data
    • Decision Tree Simulation
    • Discrete Event Simulation
    • Queuing Theory

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