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Quick Facts

Medium Of InstructionsMode Of LearningMode Of Delivery
EnglishSelf StudyVideo and Text Based

Courses and Certificate Fees

Fees InformationsCertificate AvailabilityCertificate Providing Authority
INR 1000yesIIT Bombay

The Syllabus

  • Intro To Data Analytics
  • What is LA! Definition
  • Academic Analytics, and Educational Data Mining
  • Four Levels of Analytics
  • Descriptive, Diagnostic, Predictive and Prescriptive Analytics

  • Data Collection from Different learning environment
  • Technology Enhanced Learning, Classroom and MOOC environment
  • Preprocessing
  • Ethics in Learning Analytics, Student Privacy

  • Intro to Machine Learning
  • Supervised and Unsupervised learning
  • Regression, Clustering and Classification
  • Metrics for ML algorithms –Recall, Precision, Accuracy, F-Score and Kappa
  • Demo of ML algorithms using Orange

  • Descriptive Analytics
  • Data Visualization
  • Data visualization using Excel
  • Dashboard Analytics
  • Dashboard of Youtube, MOOC

  • Intro to iSAT
  • iSAT Demo with example
  • Diagnostic Analysis
  • Correlation

  • Sequential Pattern Mining
  • SPM tool Demo
  • Process Mining
  • ProM Tool Demo

  • Predictive Analytics
  • Modeling – Feature Selection
  • Linear Regression
  • Demo of Linear Regression using Weka

  • Decision Tree
  • Demo of Decision Tree using Orange
  • Naïve Bayes algorithm
  • Demo of Naïve Bayes

  • Clustering in predictive algorithm
  • K-Means clustering
  • Demo of K-Means clustering

  • Text analytics
  • Words, Token, Stem and lemma
  • Minimum edit distance
  • Develop algorithm to automatically grade subjective answers
  • Demo of Word embedding

  • Intro Multimodal Learning Analytics
  • Eye-gaze data collection
  • Affective computing
  • Aligning and analyzing data from Multiple sensors

  • Advanced topics in LA
  • How to apply LA in our class
  • Data repos, Research papers to read, and where to present your work

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