What are MOOCs?

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.

10 out of 10 people found the following review useful

3 years ago
**completed** this course.

Intro to data science is an intermediate level course that assumes basic Python programming skills and knowledge of statistics. The course focuses on gathering, manipulating, analyzing and visualizing data using Python and various Python packages such as numpy, scipy and pandas. One of the best parts about this course
Read More

Intro to data science is an intermediate level course that assumes basic Python programming skills and knowledge of statistics. The course focuses on gathering, manipulating, analyzing and visualizing data using Python and various Python packages such as numpy, scipy and pandas. One of the best parts about this course is getting some exposure to some Python packages in the scipy stack, although I wish more time was devoted to explaining what the various modules in the scipy stack do, how to set them up at home and when to use them.

The first lesson was fairly gentle introduction with an interesting homework project dealing with data from the Titanic disaster. Lesson 2 goes into more detail about gathering and cleaning data using Pandas and an additional module that lets you make SQL queries to extract data from Pandas data frames. Lesson 3 jumps into data analysis with a T test and linear regression using gradient descent. Going from basic data manipulation into these topics was a bit jarring in terms of difficulty and more time could have been spent explaining how the functions worked. I left without a great appreciation of what gradient descent is really doing. Lesson 4 is focused on making visualizations using a module that attempts to port the functionality R language’s ggplot2 plotting package. Finally, lesson 5 introduces the concept of big data and MapReduce as a solution to deal with large data sets. Each homework assignment after the first has students dealing with New York subway turnstile data, which allows students to get some level of familiarity with the data throughout the course. This was a very good decision, since it lets students focus on learning new concepts rather than spending time familiarizing themselves with new data sets over and over again.

The first lesson was fairly gentle introduction with an interesting homework project dealing with data from the Titanic disaster. Lesson 2 goes into more detail about gathering and cleaning data using Pandas and an additional module that lets you make SQL queries to extract data from Pandas data frames. Lesson 3 jumps into data analysis with a T test and linear regression using gradient descent. Going from basic data manipulation into these topics was a bit jarring in terms of difficulty and more time could have been spent explaining how the functions worked. I left without a great appreciation of what gradient descent is really doing. Lesson 4 is focused on making visualizations using a module that attempts to port the functionality R language’s ggplot2 plotting package. Finally, lesson 5 introduces the concept of big data and MapReduce as a solution to deal with large data sets. Each homework assignment after the first has students dealing with New York subway turnstile data, which allows students to get some level of familiarity with the data throughout the course. This was a very good decision, since it lets students focus on learning new concepts rather than spending time familiarizing themselves with new data sets over and over again.

3 out of 4 people found the following review useful

3 years ago

It brings introduction in many areas, but it does not go into depth to any area. For more advanced classes look for other courses on Udacity. Good as introduction.

1 out of 1 people found the following review useful

a year ago
**partially completed** this course, spending **5 hours** a week on it and found the course difficulty to be **easy**.

Though the course uses interesting examples for teaching concepts in relation to data science, the over reliance of the online grader for practice often makes learning redundant. Big part of learning programming is experimentation which the grader does not allow for.

0 out of 2 people found the following review useful

0 out of 2 people found the following review useful

0 out of 1 people found the following review useful