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Sl.No Chapter Name MP4 Download
1Data science for engineers Course philosophy and expectation Download
2Introduction to R Download
3Introduction to R (Continued)Download
4Variables and datatypes in RDownload
5Data framesDownload
6Recasting and joining of dataframesDownload
7Arithmetic,Logical and Matrix operations in RDownload
8Advanced programming in R : FunctionsDownload
9Advanced Programming in R : Functions (Continued)Download
10Control structures Download
11Data visualization in R Basic graphicsDownload
12Linear Algebra for Data scienceDownload
13Solving Linear EquationsDownload
14Solving Linear Equations ( Continued )Download
15Linear Algebra - Distance,Hyperplanes and Halfspaces,Eigenvalues,EigenvectorsDownload
16Linear Algebra - Distance,Hyperplanes and Halfspaces,Eigenvalues,Eigenvectors ( Continued 1)Download
17Linear Algebra - Distance,Hyperplanes and Halfspaces,Eigenvalues,Eigenvectors ( Continued 2 )Download
18Linear Algebra - Distance,Hyperplanes and Halfspaces,Eigenvalues,Eigenvectors ( Continued 3 )Download
19Statistical ModellingDownload
20Random Variables and Probability Mass/Density FunctionsDownload
21Sample Statistics Download
22Hypotheses TestingDownload
23Optimization for Data ScienceDownload
24Unconstrained Multivariate OptimizationDownload
25Unconstrained Multivariate Optimization ( Continued )Download
26Gradient ( Steepest ) Descent ( OR ) Learning RuleDownload
27Multivariate Optimization With Equality ConstraintsDownload
28Multivariate Optimization With Inequality ConstraintsDownload
29Introduction to Data ScienceDownload
30Solving Data Analysis Problems - A Guided Thought ProcessDownload
31Module : Predictive ModellingDownload
32Linear RegressionDownload
33Model AssessmentDownload
34Diagnostics to Improve Linear Model FitDownload
35Simple Linear Regression Model BuildingDownload
36Simple Linear Regression Model AssessmentDownload
37Simple Linear Regression Model Assessment ( Continued )Download
38Muliple Linear RegressionDownload
39Cross ValidationDownload
40Multiple Linear Regression Modelling Building and SelectionDownload
41ClassificationDownload
42Logisitic RegressionDownload
43Logisitic Regression ( Continued )Download
44Performance MeasuresDownload
45Logisitic Regression Implementation in RDownload
46K - Nearest Neighbors (kNN)Download
47K - Nearest Neighbors implementation in RDownload
48K - means ClusteringDownload
49K - means implementation in RDownload
50Data Science for engineers - SummaryDownload

Sl.No Chapter Name English
1Data science for engineers Course philosophy and expectation Download
Verified
2Introduction to R Download
Verified
3Introduction to R (Continued)Download
Verified
4Variables and datatypes in RDownload
Verified
5Data framesDownload
Verified
6Recasting and joining of dataframesDownload
Verified
7Arithmetic,Logical and Matrix operations in RDownload
Verified
8Advanced programming in R : FunctionsDownload
Verified
9Advanced Programming in R : Functions (Continued)Download
Verified
10Control structures Download
Verified
11Data visualization in R Basic graphicsDownload
Verified
12Linear Algebra for Data scienceDownload
Verified
13Solving Linear EquationsDownload
Verified
14Solving Linear Equations ( Continued )Download
Verified
15Linear Algebra - Distance,Hyperplanes and Halfspaces,Eigenvalues,EigenvectorsDownload
Verified
16Linear Algebra - Distance,Hyperplanes and Halfspaces,Eigenvalues,Eigenvectors ( Continued 1)Download
Verified
17Linear Algebra - Distance,Hyperplanes and Halfspaces,Eigenvalues,Eigenvectors ( Continued 2 )Download
Verified
18Linear Algebra - Distance,Hyperplanes and Halfspaces,Eigenvalues,Eigenvectors ( Continued 3 )Download
Verified
19Statistical ModellingDownload
Verified
20Random Variables and Probability Mass/Density FunctionsDownload
Verified
21Sample Statistics Download
Verified
22Hypotheses TestingDownload
Verified
23Optimization for Data ScienceDownload
Verified
24Unconstrained Multivariate OptimizationDownload
Verified
25Unconstrained Multivariate Optimization ( Continued )Download
Verified
26Gradient ( Steepest ) Descent ( OR ) Learning RuleDownload
Verified
27Multivariate Optimization With Equality ConstraintsDownload
Verified
28Multivariate Optimization With Inequality ConstraintsDownload
Verified
29Introduction to Data ScienceDownload
Verified
30Solving Data Analysis Problems - A Guided Thought ProcessDownload
Verified
31Module : Predictive ModellingDownload
Verified
32Linear RegressionDownload
Verified
33Model AssessmentDownload
Verified
34Diagnostics to Improve Linear Model FitDownload
Verified
35Simple Linear Regression Model BuildingDownload
Verified
36Simple Linear Regression Model AssessmentDownload
Verified
37Simple Linear Regression Model Assessment ( Continued )Download
Verified
38Muliple Linear RegressionDownload
Verified
39Cross ValidationDownload
Verified
40Multiple Linear Regression Modelling Building and SelectionDownload
Verified
41ClassificationDownload
Verified
42Logisitic RegressionDownload
Verified
43Logisitic Regression ( Continued )Download
Verified
44Performance MeasuresDownload
Verified
45Logisitic Regression Implementation in RDownload
Verified
46K - Nearest Neighbors (kNN)Download
Verified
47K - Nearest Neighbors implementation in RDownload
Verified
48K - means ClusteringDownload
Verified
49K - means implementation in RDownload
Verified
50Data Science for engineers - SummaryDownload
Verified
Sl.No Chapter Name Bengali
1Data science for engineers Course philosophy and expectation Download
2Introduction to R Download
3Introduction to R (Continued)Download
4Variables and datatypes in RDownload
5Data framesDownload
6Recasting and joining of dataframesDownload
7Arithmetic,Logical and Matrix operations in RDownload
8Advanced programming in R : FunctionsDownload
9Advanced Programming in R : Functions (Continued)Download
10Control structures Download
11Data visualization in R Basic graphicsDownload
12Linear Algebra for Data scienceDownload
13Solving Linear EquationsDownload
14Solving Linear Equations ( Continued )Download
15Linear Algebra - Distance,Hyperplanes and Halfspaces,Eigenvalues,EigenvectorsDownload
16Linear Algebra - Distance,Hyperplanes and Halfspaces,Eigenvalues,Eigenvectors ( Continued 1)Download
17Linear Algebra - Distance,Hyperplanes and Halfspaces,Eigenvalues,Eigenvectors ( Continued 2 )Download
18Linear Algebra - Distance,Hyperplanes and Halfspaces,Eigenvalues,Eigenvectors ( Continued 3 )Download
19Statistical ModellingDownload
20Random Variables and Probability Mass/Density FunctionsDownload
21Sample Statistics Download
22Hypotheses TestingDownload
23Optimization for Data ScienceDownload
24Unconstrained Multivariate OptimizationDownload
25Unconstrained Multivariate Optimization ( Continued )Download
26Gradient ( Steepest ) Descent ( OR ) Learning RuleDownload
27Multivariate Optimization With Equality ConstraintsDownload
28Multivariate Optimization With Inequality ConstraintsDownload
29Introduction to Data ScienceDownload
30Solving Data Analysis Problems - A Guided Thought ProcessDownload
31Module : Predictive ModellingDownload
32Linear RegressionDownload
33Model AssessmentDownload
34Diagnostics to Improve Linear Model FitDownload
35Simple Linear Regression Model BuildingDownload
36Simple Linear Regression Model AssessmentDownload
37Simple Linear Regression Model Assessment ( Continued )Download
38Muliple Linear RegressionDownload
39Cross ValidationDownload
40Multiple Linear Regression Modelling Building and SelectionDownload
41ClassificationDownload
42Logisitic RegressionDownload
43Logisitic Regression ( Continued )Download
44Performance MeasuresDownload
45Logisitic Regression Implementation in RDownload
46K - Nearest Neighbors (kNN)Download
47K - Nearest Neighbors implementation in RDownload
48K - means ClusteringDownload
49K - means implementation in RDownload
50Data Science for engineers - SummaryDownload
Sl.No Chapter Name Gujarati
1Data science for engineers Course philosophy and expectation Download
2Introduction to R Download
3Introduction to R (Continued)Download
4Variables and datatypes in RDownload
5Data framesDownload
6Recasting and joining of dataframesDownload
7Arithmetic,Logical and Matrix operations in RDownload
8Advanced programming in R : FunctionsDownload
9Advanced Programming in R : Functions (Continued)Download
10Control structures Download
11Data visualization in R Basic graphicsDownload
12Linear Algebra for Data scienceDownload
13Solving Linear EquationsDownload
14Solving Linear Equations ( Continued )Download
15Linear Algebra - Distance,Hyperplanes and Halfspaces,Eigenvalues,EigenvectorsDownload
16Linear Algebra - Distance,Hyperplanes and Halfspaces,Eigenvalues,Eigenvectors ( Continued 1)Download
17Linear Algebra - Distance,Hyperplanes and Halfspaces,Eigenvalues,Eigenvectors ( Continued 2 )Download
18Linear Algebra - Distance,Hyperplanes and Halfspaces,Eigenvalues,Eigenvectors ( Continued 3 )Download
19Statistical ModellingDownload
20Random Variables and Probability Mass/Density FunctionsDownload
21Sample Statistics Download
22Hypotheses TestingDownload
23Optimization for Data ScienceDownload
24Unconstrained Multivariate OptimizationDownload
25Unconstrained Multivariate Optimization ( Continued )Download
26Gradient ( Steepest ) Descent ( OR ) Learning RuleDownload
27Multivariate Optimization With Equality ConstraintsDownload
28Multivariate Optimization With Inequality ConstraintsDownload
29Introduction to Data ScienceDownload
30Solving Data Analysis Problems - A Guided Thought ProcessDownload
31Module : Predictive ModellingDownload
32Linear RegressionDownload
33Model AssessmentDownload
34Diagnostics to Improve Linear Model FitDownload
35Simple Linear Regression Model BuildingDownload
36Simple Linear Regression Model AssessmentDownload
37Simple Linear Regression Model Assessment ( Continued )Download
38Muliple Linear RegressionDownload
39Cross ValidationDownload
40Multiple Linear Regression Modelling Building and SelectionDownload
41ClassificationDownload
42Logisitic RegressionDownload
43Logisitic Regression ( Continued )Download
44Performance MeasuresDownload
45Logisitic Regression Implementation in RDownload
46K - Nearest Neighbors (kNN)Download
47K - Nearest Neighbors implementation in RDownload
48K - means ClusteringDownload
49K - means implementation in RDownload
50Data Science for engineers - SummaryDownload
Sl.No Chapter Name Hindi
1Data science for engineers Course philosophy and expectation Download
2Introduction to R Download
3Introduction to R (Continued)Download
4Variables and datatypes in RDownload
5Data framesDownload
6Recasting and joining of dataframesDownload
7Arithmetic,Logical and Matrix operations in RDownload
8Advanced programming in R : FunctionsDownload
9Advanced Programming in R : Functions (Continued)Download
10Control structures Download
11Data visualization in R Basic graphicsDownload
12Linear Algebra for Data scienceDownload
13Solving Linear EquationsDownload
14Solving Linear Equations ( Continued )Download
15Linear Algebra - Distance,Hyperplanes and Halfspaces,Eigenvalues,EigenvectorsDownload
16Linear Algebra - Distance,Hyperplanes and Halfspaces,Eigenvalues,Eigenvectors ( Continued 1)Download
17Linear Algebra - Distance,Hyperplanes and Halfspaces,Eigenvalues,Eigenvectors ( Continued 2 )Download
18Linear Algebra - Distance,Hyperplanes and Halfspaces,Eigenvalues,Eigenvectors ( Continued 3 )Download
19Statistical ModellingDownload
20Random Variables and Probability Mass/Density FunctionsDownload
21Sample Statistics Download
22Hypotheses TestingDownload
23Optimization for Data ScienceDownload
24Unconstrained Multivariate OptimizationDownload
25Unconstrained Multivariate Optimization ( Continued )Download
26Gradient ( Steepest ) Descent ( OR ) Learning RuleDownload
27Multivariate Optimization With Equality ConstraintsDownload
28Multivariate Optimization With Inequality ConstraintsDownload
29Introduction to Data ScienceDownload
30Solving Data Analysis Problems - A Guided Thought ProcessDownload
31Module : Predictive ModellingDownload
32Linear RegressionDownload
33Model AssessmentDownload
34Diagnostics to Improve Linear Model FitDownload
35Simple Linear Regression Model BuildingDownload
36Simple Linear Regression Model AssessmentDownload
37Simple Linear Regression Model Assessment ( Continued )Download
38Muliple Linear RegressionDownload
39Cross ValidationDownload
40Multiple Linear Regression Modelling Building and SelectionDownload
41ClassificationDownload
42Logisitic RegressionDownload
43Logisitic Regression ( Continued )Download
44Performance MeasuresDownload
45Logisitic Regression Implementation in RDownload
46K - Nearest Neighbors (kNN)Download
47K - Nearest Neighbors implementation in RDownload
48K - means ClusteringDownload
49K - means implementation in RDownload
50Data Science for engineers - SummaryDownload
Sl.No Chapter Name Marathi
1Data science for engineers Course philosophy and expectation Download
2Introduction to R Download
3Introduction to R (Continued)Download
4Variables and datatypes in RDownload
5Data framesDownload
6Recasting and joining of dataframesDownload
7Arithmetic,Logical and Matrix operations in RDownload
8Advanced programming in R : FunctionsDownload
9Advanced Programming in R : Functions (Continued)Download
10Control structures Download
11Data visualization in R Basic graphicsDownload
12Linear Algebra for Data scienceDownload
13Solving Linear EquationsDownload
14Solving Linear Equations ( Continued )Download
15Linear Algebra - Distance,Hyperplanes and Halfspaces,Eigenvalues,EigenvectorsDownload
16Linear Algebra - Distance,Hyperplanes and Halfspaces,Eigenvalues,Eigenvectors ( Continued 1)Download
17Linear Algebra - Distance,Hyperplanes and Halfspaces,Eigenvalues,Eigenvectors ( Continued 2 )Download
18Linear Algebra - Distance,Hyperplanes and Halfspaces,Eigenvalues,Eigenvectors ( Continued 3 )Download
19Statistical ModellingDownload
20Random Variables and Probability Mass/Density FunctionsDownload
21Sample Statistics Download
22Hypotheses TestingDownload
23Optimization for Data ScienceDownload
24Unconstrained Multivariate OptimizationDownload
25Unconstrained Multivariate Optimization ( Continued )Download
26Gradient ( Steepest ) Descent ( OR ) Learning RuleDownload
27Multivariate Optimization With Equality ConstraintsDownload
28Multivariate Optimization With Inequality ConstraintsDownload
29Introduction to Data ScienceDownload
30Solving Data Analysis Problems - A Guided Thought ProcessDownload
31Module : Predictive ModellingDownload
32Linear RegressionDownload
33Model AssessmentDownload
34Diagnostics to Improve Linear Model FitDownload
35Simple Linear Regression Model BuildingDownload
36Simple Linear Regression Model AssessmentDownload
37Simple Linear Regression Model Assessment ( Continued )Download
38Muliple Linear RegressionDownload
39Cross ValidationDownload
40Multiple Linear Regression Modelling Building and SelectionDownload
41ClassificationDownload
42Logisitic RegressionDownload
43Logisitic Regression ( Continued )Download
44Performance MeasuresDownload
45Logisitic Regression Implementation in RDownload
46K - Nearest Neighbors (kNN)Download
47K - Nearest Neighbors implementation in RDownload
48K - means ClusteringDownload
49K - means implementation in RDownload
50Data Science for engineers - SummaryDownload
Sl.No Chapter Name Tamil
1Data science for engineers Course philosophy and expectation Download
2Introduction to R Download
3Introduction to R (Continued)Download
4Variables and datatypes in RDownload
5Data framesDownload
6Recasting and joining of dataframesDownload
7Arithmetic,Logical and Matrix operations in RDownload
8Advanced programming in R : FunctionsDownload
9Advanced Programming in R : Functions (Continued)Download
10Control structures Download
11Data visualization in R Basic graphicsDownload
12Linear Algebra for Data scienceDownload
13Solving Linear EquationsDownload
14Solving Linear Equations ( Continued )Download
15Linear Algebra - Distance,Hyperplanes and Halfspaces,Eigenvalues,EigenvectorsDownload
16Linear Algebra - Distance,Hyperplanes and Halfspaces,Eigenvalues,Eigenvectors ( Continued 1)Download
17Linear Algebra - Distance,Hyperplanes and Halfspaces,Eigenvalues,Eigenvectors ( Continued 2 )Download
18Linear Algebra - Distance,Hyperplanes and Halfspaces,Eigenvalues,Eigenvectors ( Continued 3 )Download
19Statistical ModellingDownload
20Random Variables and Probability Mass/Density FunctionsDownload
21Sample Statistics Download
22Hypotheses TestingDownload
23Optimization for Data ScienceDownload
24Unconstrained Multivariate OptimizationDownload
25Unconstrained Multivariate Optimization ( Continued )Download
26Gradient ( Steepest ) Descent ( OR ) Learning RuleDownload
27Multivariate Optimization With Equality ConstraintsDownload
28Multivariate Optimization With Inequality ConstraintsDownload
29Introduction to Data ScienceDownload
30Solving Data Analysis Problems - A Guided Thought ProcessDownload
31Module : Predictive ModellingDownload
32Linear RegressionDownload
33Model AssessmentDownload
34Diagnostics to Improve Linear Model FitDownload
35Simple Linear Regression Model BuildingDownload
36Simple Linear Regression Model AssessmentDownload
37Simple Linear Regression Model Assessment ( Continued )Download
38Muliple Linear RegressionDownload
39Cross ValidationDownload
40Multiple Linear Regression Modelling Building and SelectionDownload
41ClassificationDownload
42Logisitic RegressionDownload
43Logisitic Regression ( Continued )Download
44Performance MeasuresDownload
45Logisitic Regression Implementation in RDownload
46K - Nearest Neighbors (kNN)Download
47K - Nearest Neighbors implementation in RDownload
48K - means ClusteringDownload
49K - means implementation in RDownload
50Data Science for engineers - SummaryDownload
Sl.No Chapter Name Telugu
1Data science for engineers Course philosophy and expectation Download
2Introduction to R Download
3Introduction to R (Continued)Download
4Variables and datatypes in RDownload
5Data framesDownload
6Recasting and joining of dataframesDownload
7Arithmetic,Logical and Matrix operations in RDownload
8Advanced programming in R : FunctionsDownload
9Advanced Programming in R : Functions (Continued)Download
10Control structures Download
11Data visualization in R Basic graphicsDownload
12Linear Algebra for Data scienceDownload
13Solving Linear EquationsDownload
14Solving Linear Equations ( Continued )Download
15Linear Algebra - Distance,Hyperplanes and Halfspaces,Eigenvalues,EigenvectorsDownload
16Linear Algebra - Distance,Hyperplanes and Halfspaces,Eigenvalues,Eigenvectors ( Continued 1)Download
17Linear Algebra - Distance,Hyperplanes and Halfspaces,Eigenvalues,Eigenvectors ( Continued 2 )Download
18Linear Algebra - Distance,Hyperplanes and Halfspaces,Eigenvalues,Eigenvectors ( Continued 3 )Download
19Statistical ModellingDownload
20Random Variables and Probability Mass/Density FunctionsDownload
21Sample Statistics Download
22Hypotheses TestingDownload
23Optimization for Data ScienceDownload
24Unconstrained Multivariate OptimizationDownload
25Unconstrained Multivariate Optimization ( Continued )Download
26Gradient ( Steepest ) Descent ( OR ) Learning RuleDownload
27Multivariate Optimization With Equality ConstraintsDownload
28Multivariate Optimization With Inequality ConstraintsDownload
29Introduction to Data ScienceDownload
30Solving Data Analysis Problems - A Guided Thought ProcessDownload
31Module : Predictive ModellingDownload
32Linear RegressionDownload
33Model AssessmentDownload
34Diagnostics to Improve Linear Model FitDownload
35Simple Linear Regression Model BuildingDownload
36Simple Linear Regression Model AssessmentDownload
37Simple Linear Regression Model Assessment ( Continued )Download
38Muliple Linear RegressionDownload
39Cross ValidationDownload
40Multiple Linear Regression Modelling Building and SelectionDownload
41ClassificationDownload
42Logisitic RegressionDownload
43Logisitic Regression ( Continued )Download
44Performance MeasuresDownload
45Logisitic Regression Implementation in RDownload
46K - Nearest Neighbors (kNN)Download
47K - Nearest Neighbors implementation in RDownload
48K - means ClusteringDownload
49K - means implementation in RDownload
50Data Science for engineers - SummaryDownload


Sl.No Language Book link
1EnglishDownload
2BengaliDownload
3GujaratiDownload
4HindiDownload
5KannadaNot Available
6MalayalamNot Available
7MarathiDownload
8TamilDownload
9TeluguNot Available