LinkedIn Top Voices 2019: Data Science & Analytics

Today we’re unveiling our 5th annual Top Voices list, highlighting professionals in a variety of industries and regions who are building communities and starting thoughtful conversations on LinkedIn through their articles, posts, videos and comments. These are the people you should be following to get inspired and stay informed. 

To find standout voices, we used a combination of quantitative and qualitative signals, starting with a custom algorithm from our LinkedIn Data Science team and then curated by our LinkedIn Editors. You can learn more about how we compiled the list at the bottom of this article and can check out Top Voices around the world and across industries — from finance to education — here. 

I dug into the data to find standout voices in data science & analytics. These are the professionals who can go deep on machine learning or the value of R versus Python. They are sharing both tips for up-and-coming data scientists and job opportunities in the field as well as highlighting interesting ways data is being leveraged inside companies and beyond, from Amazon’s new open data sources to using data to fight chronic absenteeism inside schools.

Here are this year’s 10 #LinkedInTopVoices in data science and analytics.

What she talks about: Kozyrkov aims to “make data science, AI, decision intelligence, and data-driven leadership easy to understand for everyone,” she says. “Colorful language and humor makes the boring unboring!” She applies this enticing approach to in-depth articles (like a dive into machine learning) and thought-provoking conversation starters, including a lively discussion about best communication approaches for data scientists.

Her best professional hack: “When creating a technical presentation, replace every piece of jargon and every mathematical symbol with a random character from an alphabet you don't know. Try giving your talk. If you struggle, that means it's too dense and you need to simplify.”

Follow Cassie Kozyrkov.

What he talks about: Faber, a father of four children and a new puppy, shares important resources for the expanding data science and machine learning community on LinkedIn. He digs into the basics, like a beginner’s cheat sheet for Python, as well as initiates important industry discussions, such as the need for skills in both R and Python coding.

His top productivity tip for getting ahead: “Everyone needs one hour of deep (non-distracted) work every day,” he says. “Write some code, write an article, or read something productive. No zero deep work days. You will out-produce your peers in no time.”

Follow Isaac Faber.

What she talks about: Strachnyi, a two-time Top Voice and dedicated marathoner, takes to the screen to regularly engage the data science community. Her videos, which now include live segments where she talks with other data and business experts, cover topics as diverse as common Python libraries to how data is being used to fight chronic absenteeism in U.S. public schools. She’s also been working with fellow 2018 Top Voice, Kristen Kehrer, on an upcoming “Mothers of Data Science” book.

Her best career advice: “Your network is your net worth. Start early and build connections before you need them,” she says.

Follow Kate Strachnyi.

What he talks about: Tran initiates wide-ranging discussions among the data community through practical and actionable posts, ranging from a data science curriculum to the current employment challenges and opportunities in the industry. He also takes mentoring seriously and helps beginner data scientists develop a strong analytical and skills-based foundation.

His sharing process: “Whenever I have random ideas, I immediately share to Linkedin as if I'm thinking out loud,” he says. “I always think from my audience’s perspective. When I feel that my random ideas add value to their career or even life, then I post. I really want to share to help others.”

Follow Kevin Tran.

What he talks about: Kretz, a self-described data engineering evangelist based in Germany, regularly shares about data engineering tools, techniques and skills, often via live videos that cover topics like the breadth of job options in data science or a real-time Apache Spark coding session. He also regularly shares insights from his data engineering “cookbook,” a resource to help more people become data engineers.

What he’ll be following in 2020: Trends in the data science platform market. “My guess is Hadoop is going to continue losing its relevance while Cloud platforms and Software as a Service tools keep growing,” he says.

Follow Andreas Kretz.

What she talks about: Stuve, a data expert in the health care industry, shares how analytics can improve the industry as well as best practices for data teams. She’s generated robust conversations by covering topics like whether analytics should live as an IT or business function and how to use data dashboards to tell an actionable story. 

What she’ll be following in 2020: The evolution of AI and machine learning in health care. The industry could benefit from these technologies but “the methodical application is key,” she says. They “will never replace humans, so it will be interesting to see where technology and humans intersect in such a dynamic field.”

Follow Rachel Stuve.

What he talks about: Vashishta, an engineer who “loves problems,” as he puts it, shares about applied machine learning and how business leaders can understand its value. “I cover how to hire talent, how to run projects, how to break into the field, and how to make machine learning profitable,” says Vashista. 

His viral post: Earlier this year, Vashishta offered to provide feedback on people’s resumes or LinkedIn profiles to help aspiring data scientists break into the industry. He received over 400 profiles — and committed to review all of them. “I've built a model which does the review? and generates feedback that I'll be offering up early next year,” he says.

Follow Vin Vashishta.

What she talks about: Galli, a recent transplant to Berlin, regularly shares about data and machine learning methodologies, technologies and procedures, covering high-level topics like how to interpret machine learning models and sharing in-depth resources like new feature engineering packages for Python.

What she’ll be following in 2020: “Machine learning and data analysis is being used more and more to map energy consumption using electricity grids to try and anticipate demand and energy utilisation,” she says. “I think there is more to come on this topic in the next year as more countries rollout the widespread use of smart meters and data becomes more readily available.”

Follow Soledad Galli.

What she talks about: Yusof, a Kuala Lumpur-based data scientist, shares about how to become and progress as a data scientist as well as insights into her daily work, including stories about how she overcame professional challenges and landed her current role to what it really takes to move from raw data to visualization.

Her top conversation starter: Yusof, who made the leap from engineer to data scientist, shared the details of how she made the career pivot. Other professionals followed up with questions from how to enroll in similar certification programs to what her current work focus is — and she thoughtfully answered each person.

Follow Umi Kalsom Yusof.

What he talks about: Manaileng, a Johannesburg-based data scientist who often gets his best ideas while traveling for business, shares about applied data science projects he sees in the market or is working on himself, such as his new focus on algorithms that “learn to learn”, how AI is developing both in China and Africa and even some data jokes mixed in.

His best career advice: “Always strive to go beyond being a consumer of knowledge and become a producer of knowledge,” he says. And for those looking to start sharing their own knowledge, he advises, “Be authentic, and remain relevant.”

Follow Mabu Manaileng.

You’ve read about the Top Voices in data science & analytics this year. Now, check out the #LinkedInTopVoices in software development, technology, finance and more.

How we compiled the Top Voices list

First, our editors partnered with the LinkedIn Data Science team to measure the actions a member is able to generate when they engage on the platform. Specifically, we looked at the volume of responses a person’s contributions sparked and the secondary spread of those responses. These signals are proxies for conversation and community development. That said, engagement metrics aren’t enough. Our next filter was qualitative. Editors refined the list by looking at the member’s body of work: Are the contributions insightful, conversational and timely? Do they seek to give and get help vs being self-promotional? Finally, does this list reflect the world we work in today? 

All sharing activity measured took place over a 12-month period, from September 2018 to September 2019. As with all LinkedIn Lists, we exclude LinkedIn and Microsoft employees from consideration.