Best Math Courses on Coursera in 2021
Math courses you should take
Math is ubiquitous in all economic activity around us: from computers to day-to-day operation, we at least need to know basic arithmetic to cope with reality. But there's more to maths that arithemitcs and it’s worth learning logic, statistic, algebra, analysis and probability theory to understand the data and the world around us.
9 most popular Math Courses on Coursera
Here’s the list of 9 most popular math courses on Coursera in 2021:
- Introduction to Mathematical Thinking
- Introduction to Calculus
- Mathematics for Machine Learning
- Mathematics for Data Science
- Introduction to Logic
- Introduction to Discrete Mathematics for Computer Science
- Data Science Math Skills
- Business and Financial Modeling
- Probability and Statistics: To p or not to p?
Let’s now describe them one by one. Each one is pretty different and will build different math skills.
Introduction to Mathematical Thinking
Learn how to think the way mathematicians do — a powerful cognitive process developed over thousands of years.
Mathematical thinking is not the same as doing mathematics — at least not as mathematics is typically presented in our school system. School math typically focuses on learning procedures to solve highly stereotyped problems. Professional mathematicians think a certain way to solve real problems, problems that can arise from the everyday world, or from science, or from within mathematics itself. The key to success in school math is to learn to think inside-the-box. In contrast, a key feature of mathematical thinking is thinking outside-the-box — a valuable ability in today’s world. This course helps to develop that crucial way of thinking.
Introduction to Calculus
The focus and themes of the Introduction to Calculus course address the most important foundations for applications of mathematics in science, engineering and commerce. The course emphasises the key ideas and historical motivation for calculus, while at the same time striking a balance between theory and application, leading to a mastery of key threshold concepts in foundational mathematics.
Mathematics for Machine Learning
For a lot of higher level courses in Machine Learning and Data Science, you find you need to freshen up on the basics in mathematics — stuff you may have studied before in school or university, but which was taught in another context, or not very intuitively, such that you struggle to relate it to how it’s used in Computer Science. This specialization aims to bridge that gap, getting you up to speed in the underlying mathematics, building an intuitive understanding, and relating it to Machine Learning and Data Science.
Mathematics for Data Science
Behind numerous standard models and constructions in Data Science there is mathematics that makes things work. It is important to understand it to be successful in Data Science. In this specialisation we will cover wide range of mathematical tools and see how they arise in Data Science. We will cover such crucial fields as Discrete Mathematics, Calculus, Linear Algebra and Probability. To make your experience more practical we accompany mathematics with examples and problems arising in Data Science and show how to solve them in Python.
Introduction to Logic
This course is an introduction to Logic from a computational perspective. It shows how to encode information in the form of logical sentences; it shows how to reason with information in this form; and it provides an overview of logic technology and its applications — in mathematics, science, engineering, business, law, and so forth.
Introduction to Discrete Mathematics for Computer Science
Discrete Mathematics is the language of Computer Science. One needs to be fluent in it to work in many fields including data science, machine learning, and software engineering (it is not a coincidence that math puzzles are often used for interviews). We introduce you to this language through a fun try-this-before-we-explain-everything approach: first you solve many interactive puzzles that are carefully designed specifically for this online specialization, and then we explain how to solve the puzzles, and introduce important ideas along the way. We believe that this way, you will get a deeper understanding and will better appreciate the beauty of the underlying ideas (not to mention the self confidence that you gain if you invent these ideas on your own!). To bring your experience closer to IT-applications, we incorporate programming examples, problems, and projects in the specialization.
Data Science Math Skills
Data science courses contain math, no matter what you think they do. For learners who don’t have pre-calculus but still need to learn basic math in order for them to be successful, this course is created. Data science requires no extra complexity and introduces unfamiliar ideas 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.
Business and Financial Modeling
Wharton’s Business and Financial Modeling Specialization is designed to help you make informed business and financial decisions. These foundational courses will introduce you to spreadsheet models, modeling techniques, and common applications for investment analysis, company valuation, forecasting, and more. When you complete the Specialization, you’ll be ready to use your own data to describe realities, build scenarios, and predict performance.
Probability and Statistics: To p or not to p?
We live in an uncertain and complex world, yet we continually have to make decisions in the present with uncertain future outcomes. Indeed, we should be on the look-out for “black swans” — low-probability high-impact events.
To study, or not to study? To invest, or not to invest? To marry, or not to marry? While uncertainty makes decision-making difficult, it does at least make life exciting! If the entire future was known in advance, there would never be an element of surprise. Whether a good future or a bad future, it would be a known future. In this course we consider many useful tools to deal with uncertainty and help us to make informed (and hence better) decisions — essential skills for a lifetime of good decision-making.
Beyond Math Courses on Coursera
If you’re looking for other learning materials on mathematics you might want to have a look at:
Happy learning!
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