{"id":6525,"date":"2016-06-14T09:03:59","date_gmt":"2016-06-14T16:03:59","guid":{"rendered":"https:\/\/blogs.msdn.microsoft.com\/msr_er\/?p=6525"},"modified":"2016-08-17T15:27:35","modified_gmt":"2016-08-17T22:27:35","slug":"firing-up-next-generation-data-scientists","status":"publish","type":"post","link":"https:\/\/www.microsoft.com\/en-us\/research\/blog\/firing-up-next-generation-data-scientists\/","title":{"rendered":"Firing up next-generation data scientists"},"content":{"rendered":"

\"MSR_DataScienceStudentChallenge_Banner_Blog_900x300_1\" (opens in new tab)<\/span><\/a><\/p>\n

By Kristin Tolle<\/a>, Principal Program Manager, Machine Learning\u00a0and Kenji Takeda<\/a>, Solutions Architect, Microsoft Research<\/em><\/p>\n

Data science can be a superpower. While we can\u2019t solve all the ills of the world with machine learning, applying data science to just about any problem nudges us a little closer to a solution. Helping students discover that truth is a prime motivator for the challenges and hackathons that Microsoft Research supports around the world.<\/p>\n

Inspiration for students<\/h2>\n

The best way to learn is by doing, which is why Microsoft started the global Data Science Student Challenge, supporting events at Columbia University (opens in new tab)<\/span><\/a>, ENSAE ParisTech (opens in new tab)<\/span><\/a>, University College London (opens in new tab)<\/span><\/a> (UCL), Indian Institute of Science (opens in new tab)<\/span><\/a> (IISc), National University of Singapore, and University of Melbourne. This is where students\u2014undergrads and graduate, computer science majors and students from other disciplines\u2014are given access to large datasets and cloud computing, and are let loose to tackle real problems. The issues these students address are as local as identifying buildings at risk of under-heating in New York City, and as global as helping France\u2019s Red Cross maximize the effectiveness of their programs.<\/p>\n

Patricia Culligan, Associate Director of the Columbia University Data Science Institute (opens in new tab)<\/span><\/a>, expressed what all researchers in the field know: \u201cWe need to educate more people with data science tools, and expose them to the type of problems that those tools can address.\u201d Professor John Shawe-Taylor, head of the Computer Science Department at UCL, explains further, \u201cThe key for a student to really understand and feel enthused about what they\u2019re studying, [is that] they need to see how it is relevant to the real-world job they might end up with, and real-world experiences of people. What we need to do is not just teach them the theory of computer science, we need to show them how that theory is relevant and impacts on real-world problems.\u201d<\/p>\n

Participants in the hackathons found Microsoft Azure Machine Learning to be powerful and easy to use, combining their own knowledge of R and Python with the built-in algorithms using ML Studio. \u201cI found that Azure ML was great because of how smooth the workflow got, and the ability to swap between languages,\u201d Morgan Schmitz, data science student at ENSAE, enthused. Hyojong Justin Kim from UCL goes further, comparing Azure Machine Learning to MIT\u2019s popular kids\u2019 programming environment: \u201c\u2026for me [it\u2019s] like Scratch (opens in new tab)<\/span><\/a> for data science, it makes everything so much easier, and [allows me to] visualize everything. So I just have to drag-and-drop and boom<\/em> it\u2019s done!<\/p>\n