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Data Science: A Guide to Careers and Team Building
Here's a collection of curated articles to help IT professionals learn how to make a career out of data science or how to build a team of data scientists.
James M. Connolly
February 9, 2022
8 Min Read
ZoonarGmbH via Alamy Stock
[Editor’s note: This curated guide was updated in February 2022 to reflect new trends today and to include our most recent content.]
The lengthy wish list from upper management, the corporate board, customers, and employees for new, digital projects is woven with a common thread -- the work of the data science team. It's data science that underlies online customer support, AIOps, DevOps, automation, artificial intelligence and more.
Before your enterprise rolls out new technologies to fulfill that wish list, the work starts with building a data science team that ensures data quality, identifies new data sources, and implements the right technology so the digital experience is efficient and seamless.
If you are responsible for recruiting, training, deploying, and managing that data science team, this Enterprise Guide is the first step in building out a data science knowledge base. If you are an IT or data professional hoping to carve out your niche on a data science team, you can learn what skills are needed for the various roles.
Our guides are a compendium of InformationWeek articles that offer insight and advice on establishing and managing a data science team, whether the staff count is a dozen or hundreds of professionals.
Check out these articles and the hundreds of other informative content pieces -- about IT management, careers, analytics, DevOps, cloud, and other topics.
Understand the State of Data Science
Becoming data driven requires easy, secure access to any type of data across any platform, cloud, or application. These six steps will help you reach that goal.
Here are five effective approaches for CIOs to expand their skillsets and build a resilient culture that can succeed in today’s enterprise organizations.
As data science evolves, key challenges are driving organizations to seek innovative solutions to compete in the AI-driven economy.
As data science leaps into the future, there will be less demarcation between data scientists and other roles, such as product managers. In other words, filling the positions might mean converting candidates from other fields.
CIOs say that AI and machine learning are the top technologies that will drive transformation, but not many enterprises who have them in production, yet. Here's how they are planning to get there, according to a speaker at a Gartner event.
Data Science Roles
As the heads of the typical data science team, Chief Data Officers are prioritizing data quality, ROI from data and analytics investments, and data sharing.
With greater competition in a digital world, it's urgent for organizations to prioritize their projects to become data driven.
Productivity can skyrocket when people and robots work together, yet so can human frustration and fury.
Enterprises have made a lot of progress in becoming data driven over the past several years, but organizations continue to cite one major impediment.
The CDO role continues to change with competitive pressures. Where are you on your journey and what do you need to do to get to the next level?
In an ever-changing work environment, organizations are exploring new ways to keep tech experts happy in a non-management role.
As IT continues to expand into new areas, there are lots of potential career moves. Some lie outside the IT department.
Once organizations accept that citizen data science is inevitable, it's time to ensure that it's implemented responsibly.
Build a Diverse Data Team
A founder of Deloitte’s women in data science and analytics group highlights the importance of inclusion in technology.
One data scientist explains 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.
Those data scientists who use their talents in the cybersecurity field can help to change the image and perception of cybersecurity and attract more diverse talent to the field.
Studies show that gender diversity improves profits, revenue growth, stock price performance, and more. Yet women make up only 31% of IT organizations.
Giant companies such as Target, Accenture and Johnson & Johnson have partnered to form Data Science For All, which offers free education, and mentorship in the data science field. The organization's discuss diversity today.
Panel participants at the Axway Summit Americas discussed elements that may help improve gender parity in the technology scene.
Compensation for Data Scientists
These technology certifications not only command higher-than-average salaries, but their value also increased substantially in recent months.
After a year of uncertainty, the employment market for data scientists and analytics pros is heating up again.
Three technology jobs – security analyst, software developer and data scientist -- rank in the top 10 of U.S. News and World Report list of 2022 Best Jobs, evaluated on the basis of salary, work-life balance, and job outlook.
Get ready to pay six figures for your new data engineer. Quantitative recruiting firm Burtch Works' first salary survey reveals the salaries and trends impacting these data infrastructure experts.
Data Science Skills and Training Opportunities
Today’s data scientists need more than proficiency in AI and Python. Organizations are looking for specialists who also feel at home in the C-suite.
While organizations amass data they want to leverage, Purdue University is working to marry data science academics to those real-world needs.
It's easy to identify the IT sectors experiencing a major shortage of skilled talent: all of them, including data science.
Skilled data scientists are in high demand as businesses adopt data-driven strategies. Certain skills will make them indispensable.
Will your job end up being automated or eaten by an AI or robot? Here's a deeper look at careers that will continue to be in high demand in the decade ahead.
Recruit a Data Science Team
Data and analytics leaders looking to expand their AI teams should understand how to recruit, hire, organize, train, and retain AI talent to ensure long-term AI success.
As firms across all industries seek to turn data, the greatest asset of the digital age, to their advantage, attracting and retaining the right talent is essential
An effective ML team is constantly evolving based on many different factors. Assess your specific needs and use cases before putting a team into action.
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.
Key Data Science Technologies
As enterprises continue to respond to the changing conditions created by the pandemic, IT will need to embrace new approaches and new technologies. Data science will be the enabler for most of those trends.
Last year we started down the new road of uncovering enterprise analytics with machine learning. This year will see an acceleration. Here are some trends to watch.
Software development via cloud-native resources continues to gain traction among enterprises looking for scale, security, and accessibility of business intelligence. The cloud and DevOps mean more data analytics activities will create a virtual data science team in the business sector.
Even in a simple development environment, machines and algorithms are still powered by human intelligence.
In Burtch Works' annual survey of the preferred tools of advanced analytics, data, machine learning, AI, and other quantitative professionals, Python pulled ahead of the pack.
About the Author(s)
Contributing Editor and Writer
Jim Connolly is a versatile and experienced freelance technology journalist who has reported on IT trends for more than three decades. He was previously editorial director of InformationWeek and Network Computing, where he oversaw the day-to-day planning and editing on the sites. He has written about enterprise computing, data analytics, the PC revolution, the evolution of the Internet, networking, IT management, and the ongoing shift to cloud-based services and mobility. He has covered breaking industry news and has led teams focused on product reviews and technology trends. He has concentrated on serving the information needs of IT decision-makers in large organizations and has worked with those managers to help them learn from their peers and share their experiences in implementing leading-edge technologies through such publications as Computerworld. Jim also has helped to launch a technology-focused startup, as one of the founding editors at TechTarget, and has served as editor of an established news organization focused on technology startups at MassHighTech.
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