According to industry analyst Gartner, data science, analytics and machine learning applications are “the engines of the future”. Amazon, Netflix and Google set the stage, and now it seems every organization is realizing how the data we collect can be used to profoundly impact and improve an organization’s performance.
The growing pressure for organizations to turn data into business value, combined with the proliferation of data, is driving demand for data scientist jobs. But while the need for data scientists continues to grow, unfortunately, the skills gap is also widening. A study by McKinsey predicts that by 2018, the U.S. alone may face more than a 50% gap between supply and requisite demand of deep analytic talent.
To address this challenge, MIT Professional Education is offering “Data Science: Data to Insights,” a digital course aimed at helping current and aspiring data scientists better understand the concepts behind many of the advanced tools and techniques being used by leading companies today, so they can apply the knowledge to benefit their own organizations (and careers).
“This is the only course that helped me understand the underlying math and foundations behind data science and machine learning,” said Shyaam Srinivasan. “Every other course covers it very briefly and then jumps into programming. The problem there is that, without strong foundations, I am definitely not going anywhere with this.”
Data Science: Data to Insights contains five different modules that, when combined, offer a comprehensive look at the various fields of data science. One participant said the course “demystified” complex subjects, such as deep learning and neural networks. Others, like Mariagrazia Zottoli, appreciated learning about statistical methodologies, in particular, those related to new clustering techniques and network modeling.
“I now have a clearer idea about the toolkit of skills which a data scientist needs,” said Zottoli. “Working on projects which involve big amounts of data would be nearly impossible without this kind of knowledge.”
“My favorite part was the recommendation systems module,” said William Arias. “It helped me to transform the way I perceive these systems and expanded my understanding of how this can be constructed.”
But across the board, it is the various real-world case studies that participants say they are struck by most. There are well over a dozen total, each with its own data sets and programming section. For example, there is one that looks at how data analytics can be applied to identify new genes that cause autism. Another offers data on the 2016 President campaign and invites students to explore how to cluster news stories based on topics. In addition, there’s a case study on the Challenger Space Shuttle explosion that examines how anomaly detection and hypothesis testing may have been used to prevent the catastrophe using analysis of statistics and launch data.
“I found the Shuttle example to be very powerful – being able to predict the likelihood of an event given very little data. The ability to detect correlations between variables is amazing,” said John Nicodemus.
“My favorite was the Fraud Detection, using hypothesis testing with examples like Credit Card fraud,” said Dipjyoti Kumar Das. “This helped me understand concepts regarding regression, hypothesis testing, and anomaly detection, and how they can be applied in real life scenarios to solve business problems.”
Other students noted the case studies were beneficial because they helped “apply the theories” and relate concepts to real-world applications.
“The case studies were extremely insightful and brought the concepts to life. They were not only well explained and presented through fun and eye catching graphics, they have also spurred me to look for opportunities to apply the techniques in my everyday surroundings,” said one recent participant.
It’s no secret, big data is everywhere. And data science is now being integrated into every function of the modern enterprise as well. This professional course will help students refine the fundamentals so they can develop a higher-level understanding of the new tools and techniques that can now be tapped to make better business decisions. The knowledge gained may even help future data scientists design solutions and solve complex problems we never thought possible.
If you are a business leader or technology professional looking to use your data more effectively and turn big data into even bigger results, MIT’s next Data Science: Data to Insights program launches May 30, 2017. Learn more here.
14 Feb 2019
29 Jan 2019