Starting out in Data Science? Top tips and advice from DataScienceGO Speakers
DataScienceGO returns to San Diego Sep 27-29, for a three-day career-focused conference designed to unite newcomers, practitioners, managers and executives under one umbrella, speakers weigh in on how to forge the best teams, increase your hiring chances, and prepare for the future.
It’s no secret that data science plays a vital role in most businesses. Many leaders are straining to find talent while industry newcomers are vying to level up on the skills that matter. At DataScienceGO, a three-day career-focused conference (San Diego, Sept 27-29) designed to unite newcomers, practitioners, managers and executives under one umbrella, speakers weigh in on how to forge the best teams, increase your hiring chances, and prepare for the future.
Sparking a Career in Data Science
According to Laura Noren, VP of Privacy and Trust at Obsidian, a PhD in data science, computer science, math or statistics is the greatest resource an emerging data scientist can have. But there are other avenues to pursue beyond traditional academia. Andrew Ng’s online Machine Learning course on Coursera is one of many resources that Peyman Hesami, Data Scientist at Qualcomm, recommends. “Various online mediums such as Towards Data Science will help you to stay on top of the latest tools in ML,” he said. “In addition, DataScienceGO is a great place for aspiring data scientists to get insight on how to navigate their career paths!”
Ready to hit the books? Mark Meloon, Senior Data Scientist at ServiceNow, speaks highly of Foster and Provost’s book Data Science for Business. “It will give you a strong, intuitive foundation in many of the core concepts of our field,” he explained. “It also helps you understand the process of taking a business problem and turning it into a data science one.”
The Key to Getting Hired
If you feel you’ve mastered the basics, it’s time to land a job. Different companies will vary in background preferences, but there are few areas where most employers agree.
Hesami values a strong understanding of industry basics over specific knowledge of deep learning. “I’m looking for a candidate with a good statistical foundation, a solid understanding of machine models and good software engineering skills,” he said.
Meloon agrees. “Deep learning may be critical for some fields and completely useless for others,” he explained. “In most cases, significant improvements are obtained from more and better data rather than new modeling techniques or optimization procedures.”
Some employers, like Michelle Kiem, Director of Program Management and Data Governance at Bio-Rad Laboratories, have clearly defined what to look for in an impact-producing data scientist. “Problem definition and business acumen alongside maturity are important skills we look for,” she said. “We need our data scientists to be able to select and apply appropriate methodology, considering both technical and business constraints.”
Soft skills can differentiate a great candidate from a good one.
“We need to see some evidence of communication skills,” said Meloon. “Don’t just write ‘good communicator’ on your resume. Make sure anything you present gets your point across clearly and powerfully.”
Focus on the Future
Project by project, data science increases in value to business operations and experts can see the change. “It’s been a journey, but each successful project or application increases an organization’s data understanding and competency,” explained Heim. “This snowballs into more and more people understanding and seeing new areas where data can impact business.”
It Doesn’t Stop Here!
If you like what you read, join these speakers, practitioner colleagues and more at DataScienceGO 2019 in San Diego from September 27-29.
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