Why I finally chose Udacity out of the top three is owing to these three distinct reasons:ġ) The introductory courses which act as pre-requisites for the main course (Machine Learning in my case) are very well explained and instils the concepts very clearly so that the same can be used in the future.Ģ) The course material is easy to follow and the Udacity community is pretty big and extremely helpful (In form of Udacity Forums/Communities, Nanodegree channels in SLACK, blogposts in Medium etc.)ģ) If the above 2 points were not good enough to convince me to immediately enroll, the next point certainly did.

I wanted to experience Machine Learning concepts and see whether I would like to continue with this as a career option. I have closely checked the courses in the top 3 MOOCs and looked into their course materials. More importantly, owing to the enrolments in MOOCs the students also get to connect with peers across geographies and hence expand their professional networks.

In recent years the rise of MOOCs has been phenomenal, offering learning material across multiple domains and discipline across geographies for a fraction of price. Read More trying to find something that would be simple enough for me to start from scratch as well as complex enough to teach me skills to be able to solve real world scenarios and problems that I could face in the industry. What the IT industry has taught me over the last 3 years is to constantly keep updating myself with latest trends and technologies to maintain employability & relevant. In the second half of 2018, I was looking for ways to upgrade my skill set. Lastly, the AI program is open to applicants with all degrees. The Data Engineering Program is open to applicants with all degrees. degrees with strong quantitative background. The Health Data Science Program is open to applicants with Ph.D. in a quantitative field, such as physics, astrophysics, mathematics, statistics, neuroscience, bioinformatics, engineering, computer science, or quantitative social science. The data science program requires applicants to have a Ph.D. Insight runs fellowship programs in four areas. Fellows demonstrate their projects at mentor companies and start interviews immediately upon completion of the Fellowship.

Insight Fellows learn cutting-edge data science and engineering technology through project-based learning and are mentored by industry leaders and data scientists from top teams. Read More has grown to over 1,000 alumni working at over 800 top data teams across the US, including Facebook, Apple, Google, LinkedIn, The New York Times, Biogen, Amazon, Airbnb, Yelp, Bloomberg, McKinsey, 23andMe, Wayfair, and many high-growth startups. Insight Data Science is an intensive seven-week post-doctoral training Fellowship bridging the gap between academia and data science in Silicon Valley, New York, Boston, and Seattle.

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