By now you have definitely heard about data science and big data. In this one-week class, we will provide a crash course in what these terms mean and how they play a role in successful organizations. This class is for anyone who wants to learn what all the data science action is about, including those who will eventually need to manage data scientists. The goal is to get you up to speed as quickly as possible on data science without all the fluff. We've designed this course to be as convenient as possible without sacrificing any of the essentials.
This is a focused course designed to rapidly get you up to speed on the field of data science. Our goal was to make this as convenient as possible for you without sacrificing any essential content. We've left the technical information aside so that you can focus on managing your team and moving it forward.
After completing this course you will know.
1. How to describe the role data science plays in various contexts
2. How statistics, machine learning, and software engineering play a role in data science
3. How to describe the structure of a data science project
4. Know the key terms and tools used by data scientists
5. How to identify a successful and an unsuccessful data science project
3. The role of a data science manager
Course cover image by r2hox. Creative Commons BY-SA: https://flic.kr/p/gdMuhT
A Crash Course in Data Science This one-module course constitutes the first "week" of the Executive Data Science Specialization. This is an intensive introduction to what you need to know about data science itself. You'll learn important terminology and how successful organizations use data science.
MOOCs stand for Massive Open Online Courses. These arefree online courses from universities around the world (eg. StanfordHarvardMIT) offered to anyone with an internet connection.
How do I register?
To register for a course, click on "Go to Class" button on the course page. This will take you to the providers website where you can register for the course.
How do these MOOCs or free online courses work?
MOOCs are designed for an online audience, teaching primarily through short (5-20 min.) pre recorded video lectures, that you watch on weekly schedule when convenient for you. They also have student discussion forums, homework/assignments, and online quizzes or exams.
A Crash Course in Data Science is a succinct, one-week overview of the field of data science produced by the same team from John Hopkins University that produced Coursera’s data science specialization. It is the first course in the “Executive Data Science” specialization, a data science track aimed at non-technical peo
A Crash Course in Data Science is a succinct, one-week overview of the field of data science produced by the same team from John Hopkins University that produced Coursera’s data science specialization. It is the first course in the “Executive Data Science” specialization, a data science track aimed at non-technical people like business managers. The course defines data science and then discusses different aspects of data science like statistics, machine learning and the structure, output and success metrics for data science projects. Grading is based on a handful of short multiple-choice comprehension quizzes.
A Crash Course in Data Science is good for what it is: a brief overview of a field taught at a high level so that anyone can follow along. The professors have plenty of face time, explain concepts well and the video quality is good. The content quality is a definite step up from the original John Hopkins data science track.
The only real knock against this course is its brevity and the fact that it costs the full $49 to get a verified certificate if you want to complete the specialization. A course that you can complete in an hour or two should not cost the same as a month-long course. Students looking to dig their teeth into something substantial for the first month of the Executive Data Science specialization may be disappointed.
A Crash Course in Data Science is a well-made primer on the data science field, but its brevity may leave paying students wanting.
For freeware students I give this course 4 out of 5 stars: Very Good.
This course provides a peek into what data science is like, but only gives a general picture in relation to some aspects. The topics were fine, but the quality of lecturing is bad with the lecturers being unclear with vague phrases. I would probably have learnt more spending the time reading an introductory book to data science.