Data Science for Engineers

Data Science for Engineers


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SKU: cid_90672 Category:
About the course

Professor Raghunathan Rengaswamy and Professor Shankar Narasimhan, the faculties from the Department of Chemical Engineering at the Indian Institute of Technology, Madras have designed this course. This course covers in details, an introduction to R, Linear algebra for data science which includes algebraic view consisting of vectors, matrices, a product of matrix & vector, rank, null space, a solution of overdetermined set of equations and pseudo-inverse and Geometric view consisting of vectors, distance, projections, and eigenvalue decomposition. Then it teaches you about Statistics which include descriptive statistics, the notion of probability, distributions, mean, variance, covariance, covariance matrix, understanding univariate and multivariate normal distributions, introduction to hypothesis testing, and the confidence interval for estimates.

After that, it introduces you to the concept of optimization followed by the typology of data science problems and a solution framework. Then it covers what is Simple linear regression and how to verify assumptions used in linear regression. Also, the course covers Multivariate linear regression, model assessment, assessing the importance of different variables, subset selection. After that it teaches you the classification using logistic regression and finally, the course concludes with classification using K Nearest Neighbour and K-Means Clustering. This is completely an online course, and you can access it from anywhere in the world.

Learning Outcomes

After completing this course, you will be able to:

  • Develop relevant programming abilities.
  • Demonstrate proficiency with statistical analysis of data.
  • Develop the ability to build and assess data-based models.
  • Execute statistical analyses with professional statistical software.
  • Demonstrate skill in data management.
  • Boost your hireability through innovative and independent learning.
Target Audience

The course can be taken by:

Students: Students: All students who are pursuing any Computer Science and Engineering, Information Technology related courses.

Teachers/Faculties: All teachers/faculties who wish to acquire new skills or improve their efficiency in Data Science.

Professionals: All working professionals, who wish to enhance their skills by learning data science.

Why Learn Data Science for Engineers?

Data science has over the past few years come a really long way. That is why it is an integral part of understanding the working of many industries, however complex and intricate. Data science is the future of the world today. The data scientists are also an integral part of the organization and they help the world address major global challenges, that in turn can have far-reaching impacts across countries. The demand for data scientists is increasing so quickly, that McKinsey predicts that by 2018, there will be a 50 percent gap in the supply of data scientists versus demand.

Course Features
  • 24X7 Access: You can view lectures as per your own convenience.
  • Online lectures: Online lectures with high-quality videos.
  • Updated Quality content: Content is the latest and gets updated regularly to meet the current industry demands.
Test & Evaluation

There will be a final test containing a set of multiple-choice questions. Your evaluation will include the scores achieved in the final test.

Basic mathematical calculation skills and logical skills

Topics to be covered
  1. Module-1: Data Science for Engineers Course Philosophy and Expectation

    In this module, we will see the course objectives and expected the outcome of the course.

    • What are the course objectives?
    • What will not be covered?
    • What are the course outcomes and objectives?
  2. Module-2: Introduction to R

    Introduce R as a programming language to perform data analysis and the brief introduction of R studio.

    • What is R and RStudio and how to get started with it?
    • How to write, sav, and execute R files?
  3. Module-3: Introduction to R (Continued)

    In this module, we will see adding comments to the R file, clear environment of R studio and save the workspace of R.

    • How to add comments in the R file?
    • How to clear the console and environment and how to save the data from the workspace?
  4. Module-4: Variables and data types in R

    In this module, we are going to see the rules for naming the variables in R, basic data types that are available in R and we are also going to see two basic R objects-Vectors and Lists in detail.

    • What are the rules for naming the variables and what are the basic data types in R?
    • What are the basic objects in R?