Specializations and courses in math and logic teach sound approaches to solving quantifiable and abstract problems. You'll tackle logic puzzles, develop computational skills, build your ability to represent real-world phenomena abstractly, and strengthen your reasoning capabilities.

Rutgers University

Enroll by June 15 to begin earning the Supply Chain Excellence MasterTrack™ Certificate.

University of Michigan

Launch your data science career with a degree from Michigan.

The study of mathematics and logic as a discipline adds up to a lot more than what you learned in high school algebra. According to the Oxford Dictionary, math is "the abstract science of number, quantity, and space." This system of logic and quantitative reasoning may be abstract in its nature, but its use is fundamental to solving some very concrete problems - it literally structures our world.

The study of math and logic combines the abstract science of numbers with quantitative reasoning that is fundamental in solving concrete problems.

For instance, engineers rely on geometry, calculus, physics, and other mathematical tools to ensure buildings are constructed safely. Computer programmers who create the mapping apps we use to navigate our cities apply problem-solving logic, algorithms, data, and probability to recommend the best route to take at a given time of day. And even "soft science" disciplines like sociology rely on sophisticated statistical regression techniques to draw out insights about the workings of our human world.

Thus, math and logic is important to us all in our daily lives, whether we use it directly in our own work or simply live in the modern world that it makes possible.

If you have great math skills and a deep passion for mathematics in its purest, most abstract form, the answer to this question is obvious: you can become a mathematician. Given the critical importance of math to so many fields, this is an exceptionally in-demand role, and the Bureau of Labor Statistics estimates that mathematician jobs will grow by 30% between 2018 and 2028 - one of the fastest rates of any field!

However, you don't have to become a mathematician to use math and logic skills in your career. Virtually all jobs in computer science rely heavily on these skills, since programming is fundamentally about the creation of systems of logic and application of algorithms. So whether you want to go into software development, data science, or artificial intelligence, you'll need a strong background in logic and discrete math as well as statistics.

Math skills are becoming increasingly important for other jobs in both the hard and soft sciences as well. This is due in part to growing opportunities to leverage computer science approaches, particularly data science, to answer pressing questions with findings from larger datasets than ever before. For example, skills in statistical analysis are increasingly central to the work of natural scientists looking for patterns in the growth of certain species populations -- or for epidemiologists studying the spread of public health threats.

Online courses are a popular way to learn about many different topics in computer science, and this format also lends itself well to building your math and logic skills. In fact, many students use online courses to fulfill mathematics prerequisites for advanced computer science degrees.

As with computer science and other areas of study, taking courses online gives you a flexible option to develop the skills you need while continuing to work, study, or raise a family. Online versions of courses are also often significantly less expensive than on-campus counterparts, even in cases where the course material is identical.

Coursera offers a wide range of classes in math and logic, all of which are delivered by instructors at top-quality institutions such as Stanford University and Imperial College London. You can find courses that fit your specific career goals, whether that's broad skills in logic, problem solving, or mathematical thinking, or more specialized areas like mathematics for machine learning or actuarial science.

- AI for Everyone
- Introduction to TensorFlow
- Neural Networks and Deep Learning
- Algorithms, Part 1
- Algorithms, Part 2
- Machine Learning
- Machine Learning with Python
- Machine Learning Using Sas Viya
- R Programming
- Intro to Programming with Matlab
- Data Analysis with Python
- AWS Fundamentals: Going Cloud Native
- Google Cloud Platform Fundamentals
- Site Reliability Engineering
- Speak English Professionally
- The Science of Well Being
- Learning How to Learn
- Financial Markets
- Hypothesis Testing in Public Health
- Foundations of Everyday Leadership

- Deep Learning
- Python for Everybody
- Data Science
- Applied Data Science with Python
- Business Foundations
- Architecting with Google Cloud Platform
- Data Engineering on Google Cloud Platform
- Excel to MySQL
- Advanced Machine Learning
- Mathematics for Machine Learning
- Self-Driving Cars
- Blockchain Revolution for the Enterprise
- Business Analytics
- Excel Skills for Business
- Digital Marketing
- Statistical Analysis with R for Public Health
- Fundamentals of Immunology
- Anatomy
- Managing Innovation and Design Thinking
- Foundations of Positive Psychology