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Business Analytics & Intelligence from IIM Bangalore

Discussion in 'Artificial Intelligence & Machine Learning' started by Royaluni, Apr 17, 2018.

  1. Royaluni

    Royaluni Administrator

    IIM bangalore is accepting applications for 2018 batch for their Business Analytics & Intelligence program.

    Who should attend?

    The course will benefit executives, project leaders and senior managers working in various sectors. The course is designed for professionals who would like to improve ROI for their companies using analytics.

    Eligibility Criteria

    The participants should have a Bachelor degree in engineering/science/commerce or arts with mathematics as one of the subjects during their Bachelor’s programme. Preferable work experience is 3 years, in exceptional cases applicants with less than 3 years are admitted into the programme.

    Selection Process

    Candidates will be short-listed for interview based on online aptitude test and their past academic performance, quality of work experience and fitness for analytics job. Interviews for early decision will be conducted in March 2018 and interviews for normal decision will be held in May 2018.


    Course Content
    Module 1

    Foundations of Date Science: Data Visualization and Interpretation (6 Days) The process of fact-based decision making requires managers to know how to summarize, analyse, conduct hypothesis tests, interpret and communicate data using data visualization and descriptive statistics techniques to facilitate decision making. Statistical analysis is a fundamental method of quantitative reasoning that is extensively used for decision making. This module is aimed at providing participants with the most often used methods of statistical analysis along with appropriate statistical tests. The module is oriented towards application without compromising the theoretical aspects

    Foundations of Data Science Module Contents • Introduction to data science; Different types and scales of data (ratio, interval, nominal and ordinal); Data summarization and visualization methods; Tables, Graphs, Charts, Histograms, Frequency distributions, Relative frequency measures of central tendency and dispersion; Box Plot; Chebychev’s Inequality. • Data visualization and story telling with data. • Basic probability concepts, Conditional probability, Bayes Theorem, Probability distributions, Continuous and discrete distributions, Binomial Distribution, Uniform Distribution, Exponential Distribution, Normal distribution, Central Limit Theorem, Sequential decision making, Decision tree • Sampling and estimation: Estimation problems, Point and interval estimates, Confidence Intervals • Hypothesis testing: Constructing a hypothesis test; Null and alternate hypotheses; Test Statistic; Type I and Type II Error; Z test, t test, two sample t tests; Level of significance, Power of a test, ANOVA

    Test for goodness of fit, Non-parametric tests. • Introduction to R and Python Case Studies: 1. Central Parking Solutions Private Limited (IIMB Case); 2. A Dean’s Dilemma: Selection of Students for the MBA Program (IIMB Case).

    Module 2
    Data Preprocessing and Imputation (2 Days) Quality of the data is important for success of any analytics project. Anecdotal evidence suggests that more than 80% of time taken for an analytics project is spent on data preparation and data imputation. In this short module, we will be discussing data preparation and imputation techniques before advanced analytics tools can be applied. Contents Data Quality Check, data cleaning and Imputation. K Nearest Neighbours (KNN) algorithm for data imputation. Case Study: Analytics in H R — Predicting Job Acceptance

    Module 3
    Predictive Analytics: Supervised Learning Algorithms (6 Days) Predictive analytics model predicts occurrence of future events such as demand for a product, revenue forecast, customer churn, employee attrition, fraud, default in loan repayment, etc. based on historical data. In many business problems, we try to deal with data on several variables, sometimes more than the number of observations. Regression models help us understand the relationships among these variables and how the relationships can be exploited to make decisions using supervised learning algorithms. Primary objective of this module is to understand how regression and causal forecasting models can be used to analyse real-life business problems such as prediction, classification and discrete choice problems. The focus will be case-based practical problemsolving using predictive analytics techniques to interpret model outputs. The participants will be exposed to software tools such as MS Excel, R, Python, SPSS, and SAS and how to use these software tools to perform regression, logistic regression and forecasting.

    Module 4
    Optimization Analytics (Prescriptive Analytics (5 Days) Optimization models are core tools used in prescriptive analytics and are used in arriving at optimal or near optimal decisions for a given set of managerial objectives under various constraints. Optimization techniques such as gradient descent plays an important role in many machine learning algorithms. Optimization is an integral part of operations analytics with specific applications in operations and supply chain management. The objective of the module is to acquaint participants with the construction of mathematical models for managerial decision situations and use freely available Excel Solver to obtain solutions and interpret the results.

    and more, download brochure.

    Programme Fee
    Rs.6,50,000/- + GST (as applicable) per participant.
    The fee is payable in three installments as per indicated schedule. The payment schedule is as follows:
    Rs. 2,50,000/- + GST I installment on admission
    Rs. 2,00,000/- + GST II installment on or before August 4, 2018
    Rs. 2,00,000/- + GST III installment November 3, 2018
     

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