Udacity now offers a Data Science Track. It is a series of courses to provide a person with data science skills. The courses are:
- Intro to Computer Science
- Intro to Statistics
- Intro to Data Science
- Exploratory Data Analysis
- Intro to Hadoop and MapReduce
- Data Wrangling with MongoDB
The Introduction to Data Science course just opened.
Note: Udacity offers basic courseware such as lecture videos and auto-graded quizzes for free. In order to obtain a certificate, a fee is required. Along with the fee comes personalized coaching and specialized projects.
This is a nice infographic about gamification in education. I am curious to watch how gamification plays a role in the online education scene at places like Udacity and Coursera. I think gamification is going to lead to many new and interesting problems in bigdata.
Created by Knewton and Column Five Media
Recently, I read an article titled, Why Online Education Won’t Replace College–Yet. The article is most likely a response the recent success of Massive Open Online Courses (MOOCs) such as those offered by Udacity and Coursera. The author, David Youngberg an Assistant Professor of Economics, presents 5 reasons why online education won’t replace college. I disagree with his reasons, so I thought I should share more details. I will go through each of his 5 reasons.
- It’s too easy to cheat. Cheating has always been an issue in education, and I think it always will be. Students even manage to cheat in ivory tower institutions. Online Colleges such as University of Phoenix have been very successful and cheating can easily exist in that scenario. I do think online classes make cheating easy, but I don’t really see that stopping the success of online education.
- Star students can’t shine. This is just simply not true. The star students are the ones answering questions in the forums and getting the assignments done first. This is very similar to the star students in a regular college setting. The brightest students have their work done first and are frequently found helping their peers. Udacity has even hired one of the former star students.
- Employers avoid weird people. Just because a person takes an online course does not make him/her weird. Taking an online course means a person is willing to find cheaper and easier ways to solve old problems. It also means the person has the initiative to go out and complete something. All of those traits are attractive to companies. The problem here is credentials. MOOCs have not yet solved the credential problem. MOOCs don’t offer degrees or widely-acknowledged certifications yet. Companies want to hire people with degrees, not people with a piece of paper stating “I completed an online course.” I think MOOCs will quickly figure out this problem. Also, many of the Coursera and Udacity students are former college graduates. Why are they now weird for taking an online course?
- Computers can’t grade everything. Not so fast. Earlier this year, Kaggle and the Hewlett Foundation sponsored a competition to see if technology could be created to automatically grade standardized test essays. Well, the competition was a big success. See the full press release. The competition results will probably not generalize to all essays, but the technology to automatically grades papers is not that far away. Also, Coursera is experimenting with crowd-sourced grading of papers. One student grades the papers of 4 unknown classmates, then a final score is calculated by a computer. See the Peer Assessments section on the Coursera website. This technique may even be more effective than grading by a single highly-trained person.
- Money can substitute for ability. The author argued that students will pay for tutors, buy dishwashers or anything else to help get better grades. I do not think banks are going to start handing out loans for dishwashers, so students can have more time for homework. I think MOOCs will allow students to learn without building massive amounts of debt.
Now, I cannot say with certainty whether or not MOOCs will replace traditional colleges. I just did not believe the above reasons are what will determine the outcome.
On a side note, this blog is focused on material about learning to become a data scientist. I think MOOCs are going to be hugely helpful for people wishing to obtain data science skills.
Many aspects of computer science are fundamental to data science. A good data scientist has to be able to transform/extract/manipulate lots of data. Computer programming is the main technique for such operations. Here are numerous resources to help you learn the fundamentals of computer science.
Online Computer Science Courses: Introductory Level
If you are not familiar with computer programming, this list is a good place to start.
Online Computer Science Courses: More Advanced
Two More Helpful Resources
Stack Overflow is a great site for answering all of your programming questions. It is good for beginners as well as more advanced programmers. Also, if you start writing a lot of code, Github is a great place to store that code.
Statistics is an important component of data science. Thus, it would be nice to have some resources available.
Learn Statistics For Free Online
Well, here is a list of free statistics resources available online. All of these are fairly introductory, but I am guessing more advanced topics will be coming from these same organizations.
In addition to the free resources online, there are other options as well.
- Statistics.com – courses are about $400-$500 but programs lead to certificates
- Most all local colleges will offer courses in statistics
What other resources are available for learning statistics?
Last week, Udacity started a course on Introduction to Statistics, Making Decisions Based on Data. This is a beginners level course on statistics, so it should be accessible to everyone. The course consists of seven units, which are intended to last about one week each. Udacity does not enforce any time limits though. Homework problems are also a part of the course, so you will get a chance to practice what you learn.
Udacity is a learning environment similar to Coursera. I would say the presentation is more focused on the web and the experience is a bit more enjoyable. Courses at both sites are taught by professors from top universities and other leading experts in the field. Both sites offer lots of knowledge for free, and I say try them both. Then let you own personal preference decide which you like better.
What do you think about Udacity? Have you tried it?
Yesterday, I posted about some traditional strategies to acquire data science skills. Today, I will post a nontraditional strategy.
There is hoards of data science information available on the internet for free. With enough personal motivation, a person could learn all the skills necessary for free (or cheap) online. Coursera is probably a great place to start. There are also other good sites such as Udacity, the Kaggle Wiki, other blogs and websites.
The problem with this approach is knowing exactly what to learn. A course in machine learning is great, but data science is more than just machine learning. How do you know what to learn? It would be really nice to have a collection of data science topics and the associated online training materials.
Would this strategy work for you?