Data Science for Engineers

Duration:8 Weeks

@VTU COE

5.0
(1) 433 Students
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What you will learn

  • Computer Science and Engineering

Learning Objectives : 1. Introduce R as a programming language 2. Introduce the mathematical foundations required for data science 3. Introduce the first level data science algorithms 4. Introduce a data analytics problem solving framework 5. Introduce a practical capstone case study Learning Outcomes: 1. Describe a flow process for data science problems (Remembering) 2. Classify data science problems into standard typology (Comprehension) 3. Develop R codes for data science solutions (Application) 4. Correlate results to the solution approach followed (Analysis) 5. Assess the solution approach (Evaluation) 6. Construct use cases to validate approach and identify modifications required (Creating)

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Linear Algebra for Data science
Preview 28:42
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Solving Linear Equations
Preview 24:33
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Solving Linear Equations ( Continued )
Preview 24:32
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Linear Algebra - Distance,Hyperplanes and Halfspaces,Eigenvalues,Eigenvectors
Preview 33:16
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Linear Algebra - Distance,Hyperplanes and Halfspaces,Eigenvalues,Eigenvectors ( Continued 1)
Preview 28:32
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Linear Algebra - Distance,Hyperplanes and Halfspaces,Eigenvalues,Eigenvectors ( Continued 2 )
Preview 33:41
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Linear Algebra - Distance,Hyperplanes and Halfspaces,Eigenvalues,Eigenvectors ( Continued 3 )
Preview 24:16

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Statistical Modelling
Preview 25:03
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Random Variables and Probability Mass/Density Functions
Preview 28:21
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Sample Statistics
Preview 33:53
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Hypotheses Testing
Preview 37:20

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Optimization for Data Science
Preview 39:42
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Unconstrained Multivariate Optimization
Preview 29:56
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Unconstrained Multivariate Optimization ( Continued )
Preview 18:34
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Gradient ( Steepest ) Descent ( OR ) Learning Rule
Preview 23:59

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Multivariate Optimization With Equality Constraints
Preview 30:49
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Multivariate Optimization With Inequality Constraints
Preview 44:53
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Introduction to Data Science
Preview 41:56
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Solving Data Analysis Problems - A Guided Thought Process
Preview 34:40

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Module : Predictive Modelling
Preview 23:44
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Linear Regression
Preview 41:02
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Model Assessment
Preview 35:41
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Diagnostics to Improve Linear Model Fit
Preview 29:09
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Simple Linear Regression Model Building
Preview 11:51
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Simple Linear Regression Model Assessment
Preview 08:44
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Simple Linear Regression Model Assessment ( Continued )
Preview 13:33
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Muliple Linear Regression
Preview 39:03

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Cross Validation
Preview 23:07
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Multiple Linear Regression Modelling Building and Selection
Preview 14:46
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Classification
Preview 19:19
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Logisitic Regression
Preview 29:21
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Logisitic Regression ( Continued )
Preview 18:36
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Performance Measures
Preview 32:49
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Logisitic Regression Implementation in R
Preview 20:52

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K - Nearest Neighbors (kNN)
Preview 26:24
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K - Nearest Neighbors implementation in R
Preview 29:11
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K - means Clustering
Preview 31:19
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K - means implementation in R
Preview 21:13
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Data Science for engineers - Summary
Preview 06:20
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5.0

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sanjana yg

9 months ago

Very nice

Meet Your Instructor
@VTU COE
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Instructor
3.3 Rating
8558 Students
802 Courses
About Instructor

VTU is one of the largest Technological Universities in India with 24 years of Tradition of excellence in Engineering & Technical Education, Research and Innovations. It came into existence in the year 1998 to cater the needs of Indian industries for trained technical manpower with practical experience and sound theoretical knowledge.

video

Free

  • Course Duration
    20 h 28 m 43 s
  • Course Level
    Intermediate
  • Student Enrolled
    433
  • Language
    English
This Course Includes
  • 20 h 28 m 43 s Video Lectures
  • 2 Quizzes
  • 0 Assignments
  • 0 Downloadable Resources
  • Full Lifetime Access
  • Certificate of Completion