Data scientists are often described as mythical creatures, seemingly impossible to find, largely due to the expansive skills sets required. However, if you look closer, careers in data science can be broken out into a wide range of titles, and associated skills. Quick searches on data science job postings consistently found titles like data scientist, analytics architect, business analyst, visualization specialist, BI analyst, data engineering, data architect, statistician, database administrator – and most recently, data hunter, data scout and data acquisition specialist.
Sought after skills sets and knowledge within data science cover some (but not all) of the following: artificial intelligence (AI), machine learning (ML), R Programing, Python, Hadoop, SQL database, Apache Spark, unstructured data, statistical analysis, data visualization, and of course, company domain knowledge, and strong communications skills.
The editors of InformationWeek.com wanted to make things easier for both data science job seekers and enterprise leaders who want to learn more. We’ve collected and organized our coverage on this topic in this quick guide to data science careers and team building.
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High demand for data science pros:
But will the flood of candidates solve your data problems? Here are a few issues to mull and tips on how to attract/retain data scientists in this hot market.
Several emerging technology jobs are ready for big growth in 2019, including those in the data science field. The Data Science Specialist role, for example, saw a 5x growth over last year with top skills including machine learning, data science, Python, R, and Apache Spark.
The data science and analytics job market is still really hot. What's more, after a year of salaries staying relatively flat, data science and analytics professionals can look forward to increases this year.
As data science leaps into the future, there will be less demarcation between data scientists per se and other roles, such as product managers. In other words, filling the positions might mean converting candidates from other fields.
Job description, education for data scientists:
The job description for data scientists is changing. The rise of artificial intelligence, machine learning, and automated data collection means the nature of their work is shifting at a rapid pace.
Whether you need to pursue higher education for a job in data science really boils down to your existing skill set and specific goals.
One data scientist tells you why all data scientists play a critical role in how data is used (or misused), and that it’s imperative that data scientists fairly represent everyone impacted by their work.
Citizens as data scientists:
Democratization through machine learning and knowledge-sharing tools is expanding data science capabilities to more and more "citizens" in the broader working community.
Once organizations accept that citizen data science is inevitable, it's time to ensure that it's implemented responsibly.
For IT pros looking to expand their knowledge about AI or to move their careers forward by shifting to an AI consulting role, training will be critical to a successful career move.
Can AutoML really live up to the hype -- making data science experts out of neophytes? Some think the answer is yes. That AutoML may be able to reduce the demand for data scientists by enabling domain experts to automatically build predictive models without much knowledge of statistics and machine learning (ML).
Team building, driving transformation with data science:
AI is a hot term in every industry, but don’t be a company that just blindly hires a group of data scientists who end up frustrated and unproductive. The value AI brings is very real, but companies have a responsibility to be strategically aligned to take advantage of it.
How can you get a team of qualified data scientists to understand the business and work with other teams effectively to focus on business outcomes, test hypothesis, etc.?
Some enterprises struggle to drive business value from data science efforts because the business and data scientists are not communicating or collaborating well. Here are five things you can do to improve the cross-functional relationships and ROI.
CIOs say that AI and machine learning are the top technologies that will drive transformation, but there aren't many enterprises who have them in production, yet. Here's how they are planning to get there.
While few business executives understand the intricacies of machine learning, they have an obligation to make sure that their data scientists are complying with industry best practices.