Fisher, Doug (Vanderbilt University.) | Isbell, Charles (Georgia Institute of Technology) | Littman, Michael L. (Brown University) | Wollowski, Michael (Rose-Hulman Institute of Technology) | Neller, Todd W. (Gettysburg College) | Boerkoel, Jim (Harvey Mudd College)
All courses are available online and on demand – so whether you plan to spend a couple of hours per day or a couple of hours per week, you can work on these skills at the pace that is right for you. And since the curriculum consists of massive open online courses (MOOC) on edX, you can watch the videos on your tablet or phone.
In four intensive courses, you will learn what you need to know to begin assembling and leading a data science enterprise, even if you have never worked in data science before. You'll get a crash course in data science so that you'll be conversant in the field and understand your role as a leader. You'll also learn how to recruit, assemble, evaluate, and develop a team with complementary skill sets and roles. You'll learn the structure of the data science pipeline, the goals of each stage, and how to keep your team on target throughout. Finally, you'll learn some down-to-earth practical skills that will help you overcome the common challenges that frequently derail data science projects.
The high level of dependency on the internet and technology today has resulted in new revenue streams and business models for organizations, but with this arises new gaps and opportunities for hackers to exploit. Cybercriminals have become increasingly complex and are attempting to steal valuable data like financial data, health records, personal identifiable information (PII) and intellectual property, and are resorting to highly profitable strategies like disrupting the overall operations of a business via DDoS attacks, or monetizing data access via the utilization of advanced ransomware techniques. So, will blockchain technology be a cybersecurity help?
About this course: Mathematical thinking is crucial in all areas of computer science: algorithms, bioinformatics, computer graphics, data science, machine learning, etc. In this course, we will learn the most important tools used in discrete mathematics: induction, recursion, logic, invariants, examples, optimality. We will use these tools to answer typical programming questions like: How can we be certain a solution exists? Am I sure my program computes the optimal answer? Do each of these objects meet the given requirements?