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Quick Facts

Medium Of InstructionsMode Of LearningMode Of Delivery
EnglishSelf Study, Virtual ClassroomVideo and Text Based

Courses and Certificate Fees

Certificate AvailabilityCertificate Providing Authority
yesIIT Kanpur

The Syllabus

  • Introduction to analytics
  • Differentiating descriptive, predictive, and prescriptive analytics, data mining vs data analytics

  • Industrial problem solving process
  • Decision needs and analytics, stakeholders and analytics, SWOT analysis

  • Model and modeling process, modeling pitfalls, good modelers, decision models and business expectations,

  • Different types of models – overview of context diagrams, mathematical models, network models, control systems models, workflow models, capability models

  • Data and its types, phases of data analysis
  • Hypothesis and data Scales, relations, similarity and dissimilarity measures 
  • Sampling process
  • Types of sampling, sampling strategies, error mitigation

  • Visualization of numeric data
  • Visualization of non-numeric data
  • Tools available for visualizations Hypothesis testing, pairwise comparisons, t-test, ANOVA
  • Wilcoxon signed-rank test, Kruskal-Wallis test, A/B testing

  • Data infrastructure
  • Analytics and BI
  • Data sources, data warehouse, data stewardship, meta data management Data and forecasting, super-forecasting, S-curve (lifecycle), moving average, exponential smoothing, error in forecasting Linear correlation, correlation and causality
  • Spearman’s rank correlation, Linear regression, logistic regression, robust regression

  • Hierarchical clustering (Euclidean & Manhattan), k-means clustering, Nearest neighbor 
  • Decision trees Basics, customer lifetime value, customer probability model
  • Net promoter score, survival analysis Product lifecycle analysis, Ansoff’s matrix, competitive map
  • Fundamentals of simulation, simulation types, Monte-Carlo simulation

Instructors

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