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Big Data Analytics from IIM Bangalore

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

  1. Royaluni

    Royaluni Administrator

    IIM bangalore is accepting admissions for Big Data Analytics course. Please find the details here and share your opinions or questions here.


    Certificate Programme on Big Data Analytics

    Program Objectives

    This program is designed to equip its participants with an in-depth knowledge of Big Data Analytics (BDA). We will use real case studies and handson demonstrations to illustrate the applications of key concepts. At the end of the course, the participants will be able to: 1. Appreciate the emergence of business analytics and big data as a competitive strategy. 2. Analyze datasets by applying techniques from statistics, operations research, machine learning, deep learning, network analysis and data mining. 3. Process unstructured data such as social media messages and machine generated clickstream logs. 4. Have a working knowledge of languages, platforms and tools that support statistical analysis and visualization (R/Python), distributed computing (Hadoop/Spark) and network analysis (Gephi). 5. Apply the theories, techniques and tools to solve problems from industry sectors such as manufacturing, services, retail, software, banking and finance, sports, pharmaceuticals, and aerospace.

    Module 1: Foundations of Data Science

    The process of fact-based decision-making requires managers to know how to summarize, analyze, and interpret data, as well as to communicate the results using data visualization. Some of the techniques that we introduce here apply to “small” datasets: they will have to be suitably modified to handle large data volumes. Along the way, we shall introduce the participants to two platforms for machine learning: R and Python.


    Foundations of Data Science: Probability and Random variables

    Exploratory data analysis with R and Python.

    Data visualization – techniques and principles. The grammar of ggplot2

    Handling geospatial datasets

    Normality. Sampling and central limit theorem. Estimation and hypothesis testing., maximum likelihood estimation

    Matrix algebra. Eigenvalues and diagonalization. Singular value decomposition.

    Bayes theorem

    Concepts of multivariate calculus

    Case Studies: 1. Central Parking Solutions Private Limited (IIMB Case); 2. A Dean’s Dilemma: To Admit or Not to Admit (IIMB Case) 3. Analytics in HR – Predicting Job Acceptance (IIMB Case)

    Module 2: Predictive Analytics

    Predictive analytics models predict the occurrence of future events such as customer churn, default in loan repayment etc. based on historical data. In many business problems, we deal with data on several variables, sometime more than the number of observations. Regression models help us understand the relationships among these variables, and how these relationships can be exploited to make decisions. The 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 on case-based practical problem-solving using predictive analytics techniques to interpret model outputs. The participants will be exposed to software tools such as MS Excel, R, SPSS, and SAS and how to use these software tools to perform regression, logistic regression and forecasting.


    Regression model building framework: Problem definition, Data Pre-Processing; Model Building; Diagnostics and Validation • 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, Autoregression and Transformation of variables, Regression Model Building • Logistic and Multinomial Regression: Logistic function, Estimation of probability using logistic regression, Deviance, Wald Test, Hosmer Lemshow Test, Classification table, Gini co-efficient. • Forecasting: Moving average, Exponential smoothing, Casual Models, ARIMA • Application of predictive analytics in retail, direct marketing, health care, financial services, insurance, supply chain, etc.

    Eligibility Criteria and Selection Process:

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

    Selection Process:

    After submitting their applications online, candidates shall be short-listed for an on-campus test. Questions on the test will examine the candidate’s grasp of basic quantitative concepts. As prior preparation, the candidates are suggested to enrol in a beginner edX course dedicated to Statistics and complete the exercises: Based on a combination of test score, past academic performance, quality of work experience and fit for an analytics career, candidates will be called in for a face-toface interview. The test and interviews will be conducted in succession during June 2018.

    Program Fee:

    Application Deadline: 25 May 2018 On Campus Test and Face to-Face Interview June 2018 Announcement of Decision: Fourth week of June Course Commencement: 5 August 2018

    Download Brochure

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