A Complete Guide To Math And Statistics For Data Science

Key Info
Description - a brief synopsis, abstract or summary of what the learning resource is about: 
Math and Statistics for Data Science are essential because these disciples form the basic foundation of all the Machine Learning Algorithms. In fact, Mathematics is behind everything around us, from shapes, patterns and colors, to the count of petals in a flower. Although having a good understanding of programming languages, Machine Learning algorithms and following a data-driven approach is necessary to become a Data Scientist, Data Science isn’t all about these fields.
In this blog post, you will understand the importance of Math and Statistics for Data Science and how they can be used to build Machine Learning models. Here’s a list of topics author will be covering in this Math and Statistics for Data Science blog:

-Introduction to Statistics
-Terminologies in Statistics
-Categories in Statistics
-Understanding Descriptive Analysis
-Descriptive Statistics In R
-Understanding Inferential Analysis
-Inferential Statistics In R

Authoring Person(s) Name: 
Zulaikha Lateef
Access Cost: 
No fee
Primary language(s) in which the learning resource was originally published or made available: 
English
More info about
Keywords - short phrases describing what the learning resource is about: 
Data analysis
Data skills education
Machine learning
R software
Statistical modeling
Subject Discipline - subject domain(s) toward which the learning resource is targeted: 
Physical Sciences and Mathematics: Statistics and Probability
Published / Broadcast: 
Wednesday, May 22, 2019
Publisher - organization credited with publishing or broadcasting the learning resource: 
Edureka
Media Type - designation of the form in which the content of the learning resource is represented, e.g., moving image: 
Text - an explanation of a concept or a story using human readable characters formed into words, usually distinguished from graphical images.
Educational Info
Purpose - primary educational reason for which the learning resource was created: 
Instruction - detailed information about aspects or processes related to data management or data skills.
Learning Resource Type - category of the learning resource from the point of view of a professional educator: 
Lesson - detailed description of an element of instruction in a course, [could be] contained in a unit of one or more lessons, and used by a teacher to guide class instruction. Example: presentation slides on a topic.
Target Audience - intended audience for which the learning resource was created: 
Citizen scientist
Data manager
Early-career research scientist
Educator
Graduate student
Mid-career research scientist
Research faculty
Research scientist
Technology expert group
Intended time to complete - approximate amount of time the average student will take to complete the learning resource: 
Up to 1 hour