The emergence of a new data science hero
The explosion of big data and its opportunities for uncovering new insights is shaping the emerging data scientist role into the superhero of this generation, and possibly generations to come. Many organizations are creating data-driven strategies to innovate, compete and capture value from massive amounts of information they collect and store. They have a critical need for technically strong analytical professionals who can engage in knowledge discovery and convert data to business value.
While not tied exclusively to big data projects, data scientists complement these initiatives because of the growing breadth and depth of the data they examine. But what exactly does a data scientist do? For people such as me who are fairly new to the field—I am pursuing a Master of Science degree in Predictive Analytics—the concept of a data scientist sometimes gets shrouded in a semi-vigilante haze. You can almost envision a lone, caped hero fighting against time while uncovering precious gems of information from an endless ocean of data to save the town of Enterpriseopolis from making costly mistakes and sacrificing growth and revenue.
Where a traditional data analyst may look only at data from a single source, such as a customer relationship management (CRM) system, a data scientist explores and examines data from multiple, disparate sources. The data scientist sifts through all incoming data with the goal of discovering a previously hidden insight, which in turn can provide a competitive advantage or address an urgent business problem. A data scientist does not simply collect and report on data, but also looks at it from many angles, determines what it means and recommends ways to apply the data.
Acquire the skills of a data scientist
To become a successful data scientist, obviously a strong, quantitative background and analytical and reasoning capabilities are required. In addition, a combination of several business and technical skills is preferable:
- Communicate analytical results clearly to both technical and nontechnical audiences.
- Expand on modern statistical learning methods.
- Identify and correct for problems in imperfect data.
- Translate business objectives into actionable analyses, and act on them independently.
- Understand and perform statistical modeling and machine learning.
The one critical point to remember, however, is that no matter how strong your skills are, the data scientist role is not a lone-wolf activity. Technology is moving faster than a speeding bullet, which is why data scientists can no longer limit themselves when it comes to professional growth and learning. To keep skills sharp and stay on top of changes in the field, you need to be part of a large community in which you can continuously collaborate and interact with other data scientists, share best practices and learn from the successes and failures of others.
Now more than ever, aspiring and seasoned data scientists need to look for internal and external opportunities for knowledge sharing and development, such as Data Science Central and Kaggle. And forward-thinking organizations should encourage their data scientists to be active participants in these communities.
Explore a new resource for data scientists
Stay on top of developments in analytics, hone your skills and share your wisdom with other predictive analytics experts in the new IBM Predictive Analytics Community. Created by data scientists for data scientists, this community offers a one-stop shop for resources to help gain a competitive edge and continuously improve analytical skills. Access blogs, videos, product documentation and tutorials, software trials, predictive extensions, and source code for the open source community—all in one place. Join the community today.
Data scientists require power tools, especially predictive analytics and data mining solutions. In its recent evaluation of the predictive analytics solution market, Forrester Research said, "IBM assembles an impressive set of capabilities, putting predictive at the center. No matter how an organization wants to get started with predictive analytics, IBM has an option for them. The solution offers one of the most comprehensive set of capabilities to build models, conduct analysis, and deploy predictive applications: both on-premises and in the cloud. With customers deriving insights from data sets with scores of thousands of features, IBM's predictive analytics has the power to take on truly big data and emerge with critical insights. Read the complete report to learn:
- Why predictive analytics is a game changer
- The six steps of predictive analytics
- Which vendors are Leaders, which are Strong Performers and which are Contenders