Imagine you are rushing for an important meeting, and waiting in an elevator to take you to the 21st floor, and the elevator suddenly broke down. This is one of those days that you wish would never happen to you. But what if this day can be averted because we could predict when the elevator needs maintenance and optimize its operation by collecting data from its sensors, engines and computers? Applying data science through predictive maintenance techniques could make it possible! This is what I’ve learned at the Data Science Boot Camp organized by the MIS department at SFU and I will be sharing some of the insights gleaned from participating in this boot camp.
The Data Science Boot Camp lined up two speakers from Microsoft who have significant expertise and extensive experience on data science and machine learning. The two speakers, Val Fontama (Principal Data Scientist) and WeeHyong Tok (Senior Program Manager), shared with us their wealth of knowledge on what data science is and how data science is being applied in the real world.
So what exactly is “data science”? Data Science is a multi-disciplinary field, encompassing mathematics, statistics, signal processing, operations research, machines learning, linguistics and etc. There are many data-driven applications and businesses around us, and more businesses are using data to make data-driven decisions. But merely using data isn’t really what we mean by “data science”.
In the last few years, there has been an explosion in the amount of data that’s available. Whether we are talking about web server logs, online transaction records, government data or some other data source, the problem isn’t finding data, it’s figuring out what to do with it. The question facing every company, startup and non-profit today, is how to use data effectively – not just their own data, but all the data that’s available and relevant. That is why data science is becoming increasingly popular. Data can become a competitive asset with raising awareness among big players. The trend of big data and cheaper computer power enables companies to have easy access to data.
With all the data available, what could data science achieve? It can answer four questions: what happened, why did it happen, what will happen and what should I do. These questions can be answered by each of the four types of analytics: descriptive, diagnostic, predictive and prescriptive. Besides the major technology companies like Microsoft, Google, Amazon, Facebook and LinkedIn, which have successfully harnessed the value of data in their companies, we also learned about other companies who embarked on data science:
- Telecom companies utilize prescriptive analytics in action including churn, segmentation and propensity models to maximize value generated from each customer.
- ThyssenKrupp Elevator employs data science by tapping on Internet Of Things where data from sensors, engine acoustics, engine telemetry, and on-board computers were used to predict when their elevators need maintenance. This enables the company to gain a competitive edge by providing reliable service to its customers.
The speakers’ passion on data science clearly transpired in all their presentations and it was inspiring to hear very interesting stories and experiences from the speakers first hand. They also presented a live demo of using AzureML for data science. Not only did the speakers share their expertise in data science, they also provided valuable insights about their professional interests, career path, and especially, how to be prepared to ride this new wave.
The Data Science Boot Camp has allowed me to learn all about this topic and also build my professional network. Clearly, “data science” is much more than its name. For those who are interested in learning more about the subject, I am happy to have a chat with you, reach out to me on LinkedIn!
Layla Ma is a final year BBA student pursuing MIS, Finance and Business Analytics and Decision Making. She has gained diverse experiences from her analytics consulting projects, case competitions, co-op and management consulting engagements. She is starting her next co-op term as a Digital Experience Intern on the Mobile App and Interactive Team at Adidas in Germany. Layla is particularly passionate about business and data analytics in disruptive technology. Conned with Layla on LinkedIn.