Mathematics for Data Science and Machine Learning using R (Udemy.com)
Learn the fundamental mathematics for Data Science, AI &ML using R
Created by: Eduonix Learning Solutions
Last updated July 2019
What you will learn
- Master the fundamental mathematical concepts required for Datas Science and Machine Learning
- Learn to implement mathematical concepts using R
- Master Linear alzebra, Calculus and Vector calculus from ground up
- Master R programming langauge
How it compares
Against the other 461 rated Udemy courses in Data Science on CourseDuck:
- Rating4.5 out of 5higher than 46% of them
- Reviews123#399 of 462 by number of reviews
- Length10.7 hourslonger than 59% of them
- Last updatedJuly 2019earlier than 93% of them
Course Description
With the increase of data by each passing day, Data Science has become one of the most important aspects in most of the fields. From healthcare to business, everywhere data is important. However, it revolves around 3 major aspects i.e. data, foundational concepts and programming languages for interpreting the data. This course teaches you everything about all the foundational mathematics for Data Science using R programming language, a language developed specifically for performing statistics, data analytics and graphical modules in a better way.
Why Learn Foundational mathematical Concepts for Data Science Using R?
Data Science has become an interdisciplinary field which deals with processes and systems used for extracting knowledge or making predictions from large amounts of data. Today, it has become an integral part of numerous fields resulting in the high demand of professionals of data science. From helping brands to understand their customers, solving complex IT problems, to its usability in almost every other field makes it very important for the functioning and growth of any organizations or companies. Depending upon the location the average salary of data scientist expert can be over $120,000. This course will help you learn the concepts the correct way.
Why You Should Take This Online Tutorial?
Despite the availability of several tutorials on data science, it is one of the online guides containing hand-picked topics on the concepts for foundational mathematics for Data Science using R programming language. It includes myriads of sections (over 9 hours of video content) lectured by Timothy Young, a veteran statistician and data scientists . It explains different concepts in one of the simplest form making the understanding of Foundational mathematics for Data Science very easy and effective.
This Course includes:
Overview of Machine Learning and R programming language
Linear Algebra- Scalars, vectors & Metrices
Vector and Matrix Operations
Linear Regression
Calculus- Tangents, Derivatives and others
Vector Calculus- Vector spaces, Gradient Descent and others
So Much More!
This field is constantly become important for both industries as well as developers. If you are one of those who loves data science and are having issues with all the foundational concepts related to it, then it’s the right online tutorial to solve your issues. Start today, in order to become the expert of tomorrow!
Instructor Details
- 4.5 Rating
123 Reviews
Eduonix Learning Solutions
Eduonix creates and distributes high quality technology training content. Our team of industry professionals have been training manpower for more than a decade. We aim to teach technology the way it is used in industry and professional world. We have professional team of trainers for technologies ranging from Mobility, Web to Enterprise and Database and Server Administration.
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Reviews
By Aravinda on 11/29/2022
Easy to follow lecture, If you have studied Math until 12th grade. Wish there was some sort of final real life project to tie up all the concepts.
By Abhishek Gupta on 6/24/2022
Good conceptual understanding of mathematical topics, some reference reads for usage in Machine learning area would have been icing on the cake.
By Pitabas Mohanty on 5/27/2021
This is a great course! Extremely useful to understand the basic maths needed to understand Data Science and Machine Learning.
By Nick Jorgensen on 3/9/2021
Presentation and instruction are clear and easy to follow, although much of the R overview was already familiar to me.
By Daniel Nilsson on 1/8/2021
Good explanation of the math behind Linear regression, and optimization algorithms. It's also a good refresher of calculus/linear algebra, and you have the chance to work through some problems to form a good intuition for what behind the hood of some algorithms.
By Eric Martins on 10/7/2020
This is a great course for you to get a sense of how real data scientists uses math (and R) on Linear Regression cases.
By Office Word on 6/17/2020
Wow! I came into this course with absolutely no understanding of the topics he taught (My prior experience was just Python and Algebra II), But he really explains the stuff really well!
By AMRITPAL SINGH on 4/18/2020
This is the the course, I was looking for. The tutor has done tremendous work to describe the basics of Mathematics, behind the Data Science and Machine Learning (though the course just covers the basic concepts, there could have been more topics and detail analysis on the eigenvalues and eigenvectors.). Nevertheless, the objective of the course is achieved and people can build a solid base and conceptual understanding of the mathematics involved in Machine Learning. It also provides some familiarity with the R programming and it basic . Hands-on practice in R and visualization through plots, is definitely a plus point. Kudos to the teacher . Thanks for such a wonderful course
By Charles Smith on 10/18/2019
It's a lot to absorb and I'm glad I'll be able to come back for refreshers. Having said that, the instructor really knows the material and he comes across very well. I'm glad I took this course.
By Sharath Purighalla on 9/16/2019
Some intro o how to install RStudio would have been nice. I installed RStudio but none of the tidyverse commands are working. But i like the structure, pace and examples so far.
Quality Score
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Overall Score : 90 / 100












