Modules / Lectures
Module NameDownload
noc20_cs89_assignment_Week_0noc20_cs89_assignment_Week_0
noc20_cs89_assignment_Week_1noc20_cs89_assignment_Week_1
noc20_cs89_assignment_Week_10noc20_cs89_assignment_Week_10
noc20_cs89_assignment_Week_11noc20_cs89_assignment_Week_11
noc20_cs89_assignment_Week_12noc20_cs89_assignment_Week_12
noc20_cs89_assignment_Week_2noc20_cs89_assignment_Week_2
noc20_cs89_assignment_Week_3noc20_cs89_assignment_Week_3
noc20_cs89_assignment_Week_4noc20_cs89_assignment_Week_4
noc20_cs89_assignment_Week_5noc20_cs89_assignment_Week_5
noc20_cs89_assignment_Week_6noc20_cs89_assignment_Week_6
noc20_cs89_assignment_Week_7noc20_cs89_assignment_Week_7
noc20_cs89_assignment_Week_8noc20_cs89_assignment_Week_8
noc20_cs89_assignment_Week_9noc20_cs89_assignment_Week_9


Sl.No Chapter Name MP4 Download
1Lecture 1 : Intro to Data Analytics. What is Learning Analytics?Download
2Lecture 2 : Academic Analytics, and Educational Data Mining.Download
3Lecture 3 : Four Levels of AnalyticsDownload
4Lecture 4 : Four Levels of Learning Analytics Overview -IIDownload
5Lecture 5 : Data Collection from Different learning environmentDownload
6Lecture 6 : Data collection in TELEDownload
7Lecture 7 : Data PreprocessingDownload
8Lecture 8 : Ethics in Learning Analytics, Student PrivacyDownload
9Lecture 9 : Demo of WekaDownload
10Lecture 10 : Introduction to Machine LearningDownload
11Lecture 11 : Introduction to Machine Learning Part 2Download
12Lecture 12 : Training and testing dataDownload
13Lecture 13 : Performance Metrics-IDownload
14Lecture 14 : Performance Metrics-IIDownload
15Lecture 15 : Performance Metrics-IIIDownload
16Lecture 16 : Demo of Orange Download
17Lecture 17 : Descriptive AnalyticsDownload
18Lecture 18 : Descriptive Analytics-IIDownload
19Lecture 19 : ChartsDownload
20Lecture 20 : Charts-IIDownload
21Lecture 21 : Charts-IIDownload
22Lecture 22 : Comparing ChartsDownload
23Lecture 23 : Descriptive Analytics – Example IDownload
24Lecture 24 : Descriptive Analytics – Example IIDownload
25Lecture 25 : Excel toolDownload
26Lecture 26 : Diagnostics AnalyticsDownload
27Lecture 27 : CorrelationDownload
28Lecture 28 : Correlation MatrixDownload
29Lecture 29 : Spearman’s Rank CorrelationDownload
30Lecture 30 : Data MiningDownload
31Lecture 31 : iSATDownload
32Lecture 32 : Diagnostic Analytics - SPMDownload
33Lecture 33 : Sequential pattern mining (SPM-II)Download
34Lecture 34 : Differential Sequence Mining (DSM)Download
35Lecture 35 : Process MiningDownload
36Lecture 36 : Diagnostic Analytics - ClusteringDownload
37Lecture 37 : K-means ClusteringDownload
38Lecture 38 : Hierarchical ClusteringDownload
39Lecture 39 : Clustering - ExamplesDownload
40Lecture 40 : Predictive AnalyticsDownload
41Lecture 41 : Linear RegressionDownload
42Lecture 42 : Multiple RegressionDownload
43Lecture 43 : Logistic RegressionDownload
44Lecture 44 : Linear Regression - ExampleDownload
45Lecture 45 : Predictive Analytics-IIDownload
46Lecture 46 : Naive Bayes ClassifierDownload
47Lecture 47 : Decision TreeDownload
48Lecture 48 : Decision Tree ClassifierDownload
49Lecture 49 : DT, NB - ExamplesDownload
50Lecture 50 : Text AnalyticsDownload
51Lecture 51 : Introduction to NLPDownload
52Lecture 52 : NLP-IIDownload
53Lecture 53 : NLP-ToolsDownload
54Lecture 54 : NLP-ExamplesDownload
55Lecture 55 : Intro Multimodal Learning AnalyticsDownload
56Lecture 56 : Affective Computing -1Download
57Lecture 57 : Affective Computing -2Download
58Lecture 58 : Eye TrackingDownload
59Lecture 59 : Revision of Learning Analytics tools courseDownload
60Lecture 60 : Source of Data collection and Research Community Download
61Lecture 61 : Machine Learning tools used in industryDownload

Sl.No Chapter Name English
1Lecture 1 : Intro to Data Analytics. What is Learning Analytics?Download
Verified
2Lecture 2 : Academic Analytics, and Educational Data Mining.Download
Verified
3Lecture 3 : Four Levels of AnalyticsDownload
Verified
4Lecture 4 : Four Levels of Learning Analytics Overview -IIDownload
Verified
5Lecture 5 : Data Collection from Different learning environmentPDF unavailable
6Lecture 6 : Data collection in TELEPDF unavailable
7Lecture 7 : Data PreprocessingPDF unavailable
8Lecture 8 : Ethics in Learning Analytics, Student PrivacyPDF unavailable
9Lecture 9 : Demo of WekaPDF unavailable
10Lecture 10 : Introduction to Machine LearningDownload
Verified
11Lecture 11 : Introduction to Machine Learning Part 2Download
Verified
12Lecture 12 : Training and testing dataDownload
Verified
13Lecture 13 : Performance Metrics-IDownload
Verified
14Lecture 14 : Performance Metrics-IIDownload
Verified
15Lecture 15 : Performance Metrics-IIIPDF unavailable
16Lecture 16 : Demo of Orange Download
Verified
17Lecture 17 : Descriptive AnalyticsPDF unavailable
18Lecture 18 : Descriptive Analytics-IIPDF unavailable
19Lecture 19 : ChartsPDF unavailable
20Lecture 20 : Charts-IIPDF unavailable
21Lecture 21 : Charts-IIPDF unavailable
22Lecture 22 : Comparing ChartsPDF unavailable
23Lecture 23 : Descriptive Analytics – Example IPDF unavailable
24Lecture 24 : Descriptive Analytics – Example IIPDF unavailable
25Lecture 25 : Excel toolPDF unavailable
26Lecture 26 : Diagnostics AnalyticsPDF unavailable
27Lecture 27 : CorrelationPDF unavailable
28Lecture 28 : Correlation MatrixPDF unavailable
29Lecture 29 : Spearman’s Rank CorrelationPDF unavailable
30Lecture 30 : Data MiningPDF unavailable
31Lecture 31 : iSATPDF unavailable
32Lecture 32 : Diagnostic Analytics - SPMPDF unavailable
33Lecture 33 : Sequential pattern mining (SPM-II)PDF unavailable
34Lecture 34 : Differential Sequence Mining (DSM)PDF unavailable
35Lecture 35 : Process MiningPDF unavailable
36Lecture 36 : Diagnostic Analytics - ClusteringPDF unavailable
37Lecture 37 : K-means ClusteringPDF unavailable
38Lecture 38 : Hierarchical ClusteringPDF unavailable
39Lecture 39 : Clustering - ExamplesPDF unavailable
40Lecture 40 : Predictive AnalyticsPDF unavailable
41Lecture 41 : Linear RegressionPDF unavailable
42Lecture 42 : Multiple RegressionPDF unavailable
43Lecture 43 : Logistic RegressionPDF unavailable
44Lecture 44 : Linear Regression - ExamplePDF unavailable
45Lecture 45 : Predictive Analytics-IIPDF unavailable
46Lecture 46 : Naive Bayes ClassifierPDF unavailable
47Lecture 47 : Decision TreePDF unavailable
48Lecture 48 : Decision Tree ClassifierPDF unavailable
49Lecture 49 : DT, NB - ExamplesPDF unavailable
50Lecture 50 : Text AnalyticsPDF unavailable
51Lecture 51 : Introduction to NLPPDF unavailable
52Lecture 52 : NLP-IIPDF unavailable
53Lecture 53 : NLP-ToolsPDF unavailable
54Lecture 54 : NLP-ExamplesPDF unavailable
55Lecture 55 : Intro Multimodal Learning AnalyticsPDF unavailable
56Lecture 56 : Affective Computing -1PDF unavailable
57Lecture 57 : Affective Computing -2PDF unavailable
58Lecture 58 : Eye TrackingPDF unavailable
59Lecture 59 : Revision of Learning Analytics tools coursePDF unavailable
60Lecture 60 : Source of Data collection and Research Community PDF unavailable
61Lecture 61 : Machine Learning tools used in industryPDF unavailable


Sl.No Language Book link
1EnglishNot Available
2BengaliNot Available
3GujaratiNot Available
4HindiNot Available
5KannadaNot Available
6MalayalamNot Available
7MarathiNot Available
8TamilNot Available
9TeluguNot Available