Making sense of Big Data can be as difficult as choosing the right hashtags, but some experts have figured out how to do both.
Humans and technologies generate 2.5 quintillion bytes of data a day, but these troves of information are only as good as the tools companies use to understand it all. Big Data is expected to significantly mature in 2018 and, coupled with advancements in cloud, artificial intelligence and other advanced technologies, is pushing the boundaries of possibility and understanding.
Here are 10 data specialists who offer a unique view into the front and back-end operations of data analytics, deriving and applying insight in sports, cities, tech companies and a host of other relevant areas:
1. Craig Brown: @DrDataScientist
Position: Big Data consultant
Why follow? Brown puts decades of expertise to use tackling business technology trends from a Big Data standpoint. He offers followers daily resources with Big Data insight on tech topics from cloud and AI to hardware and mobility.
ICYDK: Inside the black box 2: Following the original observations of Neural Networks in action; I decided a follow up was needed. In the original blog, ; the smallest neural net (NN) that learnt the data set was 2-6-3-1 but the… https://t.co/f0WrjDRmFC #DataScience #Cloud pic.Twitter.com/B2pCeJZfWv— Dr. Craig Brown (@DrDataScientist) January 16, 2018
2. Lillian Pierson: @BigDataGal
Position: Founder of Data-Mania
Why follow? Pierson uses her blog and Twitter to provide educational and training services for data science and analytics. She also offers a daily wealth of resources exploring Big Data applications in the enterprise.
New #DemoLive: I'm teaching the basics of #WebScraping with Python & BeautifulSoup. Today you'll look at objects that comprise BeautifulSoup. In follow-up demos, I'm going to teach you to work with parsed data & scrape a webpage #datascience #coding#ai https://t.co/gYc4wuSmXc— Lillian Pierson, PE (@BigDataGal) January 16, 2018
3. Ben Alamar: @BenAlamarESPN
Position: Director of Sports Analytics at ESPN
Bio: "Director of Sports Analytics at ESPN and author of Sports Analytics: A Guide for Managers, Coaches, and Other Decision Makers (http://amzn.to/Y4mvzL )."
Why follow? The insight derived from sports statistics is changing both the fan experience and how teams are managed and run. Alamar offers a unique look into the technical side of professional sports, including ways to get involved in the field. With March Madness looming, his expertise could help your bracket entry stick around just one round longer.
Applications close on Monday for ESPN's hackathon @SloanSportsConf Participants will use player tracking data and focus on storytelling with data: https://t.co/lZGGA0smpd (to my knowledge there is no truth to the rumor that @BarackObama will be a judge)— Ben Alamar (@BenAlamarESPN) January 11, 2018
4. Bill Schmarzo: @schmarzo
Position: CTO of Dell EMC Global Services Big Data Practice
Bio: "#BigData author, Dell EMC CTO, former VP Y! Analytics, USF Executive Fellow #DataScience #DataMonetization https://infocus.emc.com/author/william_schmarzo/ …"
Why follow? Schmarzo is worth watching by nature of his position at Dell EMC, but he also actively utilizes Twitter to drive discussion and understanding of Big Data in the enterprise. From more technical aspects, such as chaos theory, to how to be an effective data scientist, Schmarzo offers something for all types of data workers and specialists to learn.
5. Ronald van Loon: @Ronald_vanLoon
Position: Director of Adversitement
Why follow? Van Loon offers a variety of technical and nontechnical knowledge on data analytics, the Internet of Things and artificial intelligence in the enterprise. Reading recommendations and infographics color his feed, which is updated almost hourly.
6. Hilary Mason: @hmason
Position: VP of research at Cloudera
Why follow? As the VP of research at Cloudera, Mason is exposed firsthand to opportunities for those embarking on Big Data solutions. Her feed is in part candid excerpts from her experiences in software demos but is also earmarked by advice for those looking for an entry to junior-level positions in data science careers.
When you say "hiring junior data scientists is a risk" I hear "we don't have competent management of our data science team".— Hilary Mason (@hmason) December 29, 2017
Sadly, I've heard this a few times this week.
7. John Myles White: @johnmyleswhite
Position: Data scientist at Facebook
Bio: "I manage the NYC branch of Facebook's Core Data Science team. Tweets solely reflect my (clearly idiosyncratic) views."
Why follow? Facebook, among other social media sites, accumulates troves of personal data through users' site actions, payments and even information on devices used to access the platform. Myles White offers inside glimpses of Facebook's data center infrastructure while warning against ineffective ways of data mining. His posts are rarely interrupted by things unrelated to data.
We waste an enormous amount of time talking about data analysis and reanalyzing old datasets. But most statistical methods only work well when you work with very large samples collected by well-designed processes.— John Myles White (@johnmyleswhite) December 22, 2017
8. Kirk Borne: @KirkDBorne
Position: Principal data scientist and executive advisor at Booz Allen Hamilton
Why follow? Borne is a notable figure in the Big Data community, and his Twitter stitches together the natural hype surrounding AI and what it means for Big Data, predictions and the industries that benefit from it. His feed is appropriate for both the seasoned data scientist and the neophyte.
With massive amounts of Computational Power, #BigData, and #DeepLearning #Algorithms, artificial intelligence is finally getting smart: https://t.co/hxfFqfoDQr #AI #DataScience #MachineLearning pic.Twitter.com/SAndyiFDs9— Kirk Borne (@KirkDBorne) January 15, 2018
9. Monica Rogati: @mrogati
Position: Data scientist and AI advisor
Why follow? Rogati's Tweets are often coupled with in-depth looks at how AI and ML impact the role and management of Big Data. As an advisor, her account features Tweets and re-Tweets about how to scale and minimize the complexity of Big Data.
10. Doug Laney: @Doug_Laney
Position: VP and distinguished analyst at Gartner
Bio: "Gartner Analyst, Chief Data Officer research & advisory team. Data & Analytics Strategy, Infonomics, Big Data. Info Innovation. 10SNE1?"
Why follow? Laney is a prominent figure in Big Data research. His feed is a wealth of knowledge, but Laney is unabashed by dispelling emerging trends that may be misleading, such as the suggestion that "information is the new oil." While his Tweets are highly informative, he manages to give Big Data and all of its complexity a personality.
A machine learning algorithm walked into a bar.— Doug Laney (@Doug_Laney) December 19, 2017
The bartender asked, "What would you like to drink?"
The algorithm replied, "What's everyone else having?"#machinelearning #AI #analytics #datascience #algorithm #humor