# Coursera: Data Science Math Skills

Data science courses contain math—no avoiding that! This course is designed to teach learners the basic math you will need in order to be successful in almost any data science math course and was created for learners who have basic math skills but may not have taken algebra or pre-calculus. Data Science Math Skills introduces the core math that data science is built upon, with no extra complexity, introducing unfamiliar ideas and math symbols one-at-a-time.

Learners who complete this course will master the vocabulary, notation, concepts, and algebra rules that all data scientists must know before moving on to more advanced material.

Topics include:
~Set theory, including Venn diagrams
~Properties of the real number line
~Interval notation and algebra with inequalities
~Uses for summation and Sigma notation
~Math on the Cartesian (x,y) plane, slope and distance formulas
~Graphing and describing functions and their inverses on the x-y plane,
~The concept of instantaneous rate of change and tangent lines to a curve
~Exponents, logarithms, and the natural log function.
~Probability theory, including Bayes’ theorem.

While this course is intended as a general introduction to the math skills needed for data science, it can be considered a prerequisite for learners interested in the course, "Mastering Data Analysis in Excel," which is part of the Excel to MySQL Data Science Specialization. Learners who master Data Science Math Skills will be fully prepared for success with the more advanced math concepts introduced in "Mastering Data Analysis in Excel."

Good luck and we hope you enjoy the course!

## Syllabus

Welcome to Data Science Math Skills
This short module includes an overview of the course's structure, working process, and information about course certificates, quizzes, video lectures, and other important course details. Make sure to read it right away and refer back to it whenever needed

Building Blocks for Problem Solving
This module contains three lessons that are build to basic math vocabulary. The first lesson, "Sets and What They’re Good For," walks you through the basic notions of set theory, including unions, intersections, and cardinality. It also gives a real-world application to medical testing. The second lesson, "The Infinite World of Real Numbers," explains notation we use to discuss intervals on the real number line. The module concludes with the third lesson, "That Jagged S Symbol," where you will learn how to compactly express a long series of additions and use this skill to define statistical quantities like mean and variance.

Functions and Graphs
This module builds vocabulary for graphing functions in the plane. In the first lesson, "Descartes Was Really Smart," you will get to know the Cartesian Plane, measure distance in it, and find the equations of lines. The second lesson introduces the idea of a function as an input-output machine, shows you how to graph functions in the Cartesian Plane, and goes over important vocabulary.

Measuring Rates of Change
This module begins a very gentle introduction to the calculus concept of the derivative. The first lesson, "This is About the Derivative Stuff," will give basic definitions, work a few examples, and show you how to apply these concepts to the real-world problem of optimization. We then turn to exponents and logarithms, and explain the rules and notation for these math tools. Finally we learn about the rate of change of continuous growth, and the special constant known as “e” that captures this concept in a single number—near 2.718.

Introduction to Probability Theory
This module introduces the vocabulary and notation of probability theory – mathematics for the study of outcomes that are uncertain but have predictable rates of occurrence. We start with the basic definitions and rules of probability, including the probability of two or more events both occurring, the sum rule and the product rule, and then proceed to Bayes’ Theorem and how it is used in practical problems.

2 Student
reviews
Cost Free Online Course (Audit)
Pace Upcoming
Subject Data Science
Institution Duke University
Provider Coursera
Language English
Certificates Paid Certificate Available
Calendar 4 weeks long

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## Reviews for Coursera's Data Science Math Skills 3.0 Based on 2 reviews

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4.0 2 weeks ago
by completed this course, spending 15 hours a week on it and found the course difficulty to be medium.
Summary of Course Review:

I was fairly satisfied with the course as an introduction to the math for data science specifically and given there aren't many math courses built specifically for an introduction to Data Science provided by a credible University and professor, I'm glad this one exists as I suspect many of the concepts studied will come up again in my studies of data science.

Course Intended Audience:

Note that this course is specifically targeted at business persons working with data scientists and people who lack the math background who would like an introduction to the math in data science so as such the explanations of the math are left fairly high level which is fair enough for its target audience. To put things in context, I am a non-math person looking to study data science so the detail level of this course feels fairly basic.

Key Criticisms of Course:

The firs three weeks are fairly easy to follow but on
Summary of Course Review:

I was fairly satisfied with the course as an introduction to the math for data science specifically and given there aren't many math courses built specifically for an introduction to Data Science provided by a credible University and professor, I'm glad this one exists as I suspect many of the concepts studied will come up again in my studies of data science.

Course Intended Audience:

Note that this course is specifically targeted at business persons working with data scientists and people who lack the math background who would like an introduction to the math in data science so as such the explanations of the math are left fairly high level which is fair enough for its target audience. To put things in context, I am a non-math person looking to study data science so the detail level of this course feels fairly basic.

Key Criticisms of Course:

The firs three weeks are fairly easy to follow but once we get into Week 4, the material leading into Bayes' Theorem, Bayes' Theorem and Binomial Theorem were quite hard to follow and understand.

This is in comparison to how perhaps someone like Sal Khan from Khan Academy or Kalid Asad from Better Explained would explain how "M choose N" works etc. Basically the entire week 4 had me asking why? or how? which required further explanations from the above two sources.

E.g. Bayes' Theorem (Part 2) the formula to updating probabilities is not explained but rather just provided which is a bit hard to follow.

I do note that the entire Bayes equation for inverse probability is brought together quite nicely at the end of Bayes' Theorem (Part 2) but it would have been good to perhaps hint that the last part of part 1 and the entire part 2 would be brought together at the end of part 2.

E.g.2 Bayes' Theorem (Part 1) the final equation to solve for Urn 1 given Event 3 Whites in a row had two conversions first using sum rule, then product rule which would be completely new to students at this point, it would have been handy if Dr. Egger reminded of these two key conversions and went through it step by step. I found myself going back to the earlier topics and having to break it down on my own which took more time.

Key Strengths of Course:

Provides a credible source of math foundations for Data Science in a messy Internet world that is easy for math beginners to understand.

Video companion PDFs are extremely very useful in scanning the material each week before hand and for revision.

Dr. Egger makes many of the topics in the four weeks easy to understand and showcases them in context to data science.

Areas of Possible Growth:

If the programme provided ideas of where students could go next in their data science studies that would be helpful.

Some supplementary videos on how key methods are used in actual data science outside of the examples provided.

Changes to Course in 2018:

A previous reviewer noted the 'second lecturer' may have been less easy to follow, doing this course in Jan 2018, it appears to be entirely run by Dr. Daniel Egger now.