Learn · Learning Analytics Tools
Lecture 28 of 65Week 4
Lecture 28 : Descriptive Analytics – Example II
Prof. Ramkumar Rajendran · IIT Bombay
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- Learning Analytics Tools, IIT Bombay (at NPTEL)
- Licence
- CC BY-NC-SA, as NPTEL states it. Non-commercial use only.
- Attribution
- NPTEL states: "Distributed under Creative Commons Attribution-ShareAlike / CC BY - NC - SA", beside a BY-NC-SA badge.
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All the lectures
- Lecture 1 : Intro to Data Analytics. What is Learning Analytics?
- Lecture 2 : Academic Analytics, and Educational Data Mining.
- Lecture 3 : Four Levels of Analytics
- Lecture 4 : Four Levels of Learning Analytics Overview -II
- Lecture 5 : Data Collection from Different learning environment
- Lecture 6 : Data collection in TELE
- Lecture 7 : Data Preprocessing
- Lecture 8 : Ethics in Learning Analytics, Student Privacy
- Lecture 9 : Introduction to Data Preprocessing
- Lecture 10 : Major Tasks in Data Preprocessing
- Lecture 11 : Missing Data Handling
- Lecture 12 : Outliers, Categorical Data Encoding and Data Transformation
- Lecture 13 : Demo of Weka
- Lecture 14 : Introduction to Machine Learning
- Lecture 15 : Introduction to Machine Learning Part 2
- Lecture 16 : Training and testing data
- Lecture 17 : Performance Metrics-I
- Lecture 18 : Performance Metrics-II
- Lecture 19 : Performance Metrics-III
- Lecture 20 : Demo of Orange
- Lecture 21 : Descriptive Analytics
- Lecture 22 : Descriptive Analytics-II
- Lecture 23 : Charts
- Lecture 24 : Charts-II
- Lecture 25 : Charts-II
- Lecture 26 : Comparing Charts
- Lecture 27 : Descriptive Analytics – Example I
- Lecture 28 : Descriptive Analytics – Example II
- Lecture 29 : Excel tool
- Lecture 30 : Diagnostics Analytics
- Lecture 31 : Correlation
- Lecture 32 : Correlation Matrix
- Lecture 33 : Spearman’s Rank Correlation
- Lecture 34 : Data Mining
- Lecture 35 : iSAT
- Lecture 36 : Diagnostic Analytics - SPM
- Lecture 37 : Sequential pattern mining (SPM-II)
- Lecture 38 : Differential Sequence Mining (DSM)
- Lecture 39 : Process Mining
- Lecture 40 : Diagnostic Analytics - Clustering
- Lecture 41 : K-means Clustering
- Lecture 42 : Hierarchical Clustering
- Lecture 43 : Clustering - Examples
- Lecture 44 : Predictive Analytics
- Lecture 45 : Linear Regression
- Lecture 46 : Multiple Regression
- Lecture 47 : Logistic Regression
- Lecture 48 : Linear Regression - Example
- Lecture 49 : Predictive Analytics-II
- Lecture 50 : Naive Bayes Classifier
- Lecture 51 : Decision Tree
- Lecture 52 : Decision Tree Classifier
- Lecture 53 : DT, NB - Examples
- Lecture 54 : Text Analytics
- Lecture 55 : Introduction to NLP
- Lecture 56 : NLP-II
- Lecture 57 : NLP-Tools
- Lecture 58 : NLP-Examples
- Lecture 59 : Intro Multimodal Learning Analytics
- Lecture 60 : Affective Computing -1
- Lecture 61 : Affective Computing -2
- Lecture 62 : Eye Tracking
- Lecture 63 : Revision of Learning Analytics tools course
- Lecture 64 : Source of Data collection and Research Community
- Lecture 65 : Machine Learning tools used in industry