Doors Open Wide for Data Scientist Opportunities
If you work with data in any analysis capacity within your organization, you’re probably aware of the need for data science approaches to better communicate the meaningfulness of information to decision makers. However, data scientists are in short supply, and with the rapidly evolving state of artificial intelligence and machine learning, it’s unclear what skills will be needed and which will be commoditized in the months and years to come.
Data scientists are needed to comb through increasingly massive data sets, attempting to address more and more subject areas, so there simply won’t be enough of them to cover all bases. So it’s up to anyone with a vested or passionate interest in data analytics to boost their skills and assume greater data science responsibilities.
So, what does it take to become a data scientist, or take on the duties of one? Education and educational credentials are important, of course — and programs in data science courses and programs are popping up all across the land. Leading universities are now offering masters-level programs in data science, including Arizona State University, Carnegie-Mellon University, Georgia Tech, Illinois Institute of Technology, Northwestern University, Stanford University, Syracuse University, University of California-Berkeley, and the University of Wisconsin.
For those without the funds or time to pursue graduate degrees, there are many other methods of learning. There are some high-quality courses offered online for free, including ones from Coursera, EdX, Udacity, IBM, California Instituter of Technology, Dataquest and KDNuggets. “Employers are waking up to the fact that employees with the ability to use data and analytics to solve business problems are increasingly valuable, whatever their background or position in an organization,” says Bernard Marr, an industry expert and author.
The door is wide open, then, for those interested in pursuing data science approaches within their organizations or to build their own repertoires in this area. Data science itself can be narrowed down to a few essential steps, according to Mike de Waal, president and founder of Global IQX, and formerly an executive Manulife Financial. Essentially, the role of the data scientist is to do the following:
- Frame the problem
- Collect raw data
- Process the data
- Explore the data
- Perform in-depth analysis
- Communicate the results
In a recent post, Dru Wynings, head of business development at Diffbot, a provider of machine learning and computer vision APIs, provided some guidelines for those interested in building data science skills. “Many companies that don’t have the resources or ability to hire a full-blown data scientist are taking advantage of web scraping tools to sort and analyze that data themselves. This means that almost anyone within an organization — especially those with programming knowledge or an understanding of data — can collect and analyze data like a data scientist, even if they’re not one.”
Wynings provides the following advice for quickly building one’s data science skills with an organization:
Know what data is important. “Data scientists can usually tell you what data is valuable and what data is just hay in the haystack. Your web scraper should be able to tell the difference.”
Make a list of goals that you want to achieve so you know what data can be pulled. “Focus on solving problems that have real and immediate business value.”
Make sure your data gathering is easy. “You also want to make sure that it can pull data as often as you need it. Data can become stale very quickly, so scraping or crawling for new data will be an important part of the process.”
Leverage external data. “Both internal data and external data have value, but external data can provide you with a bigger picture. External data can give you real-time updates on industry insights, customer activity, and product trends that you may miss with internal data alone.”