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A Data Scientist’s Guide to Communicating Results

In this contributed article, technology writer and blogger Kayla Matthews discusses the field of data science is as unclear and vague as a muddy lake and it is critical to properly communicate results. While most terms and concepts include a legitimate definition, it’s all too easy to get bogged down in technical jargon. As such, some ideas mean different things from company to company and even — in some cases — from project to project.

Data Science Job Postings Are Growing Quickly

As more businesses look to data driven technologies like automation and AI, the need for talented workers who can interpret the data is only expected to rise. In fact, IBM predicts that the demand for data scientists will soar 28% by 2020. To dive into this trend further, our friends at Indeed, the well-known job site, took a deeper look at the industry growth.

How Data Scientists Are Wasting Their Time

In this contributed article, Abhi Yadav, Co-founder & CEO at ZyloTech points that while data scientists are flawed and there are lots of ways in which they could improve, so too are machines. It would seem that the best way forward is to work side-by-side, fleshy-arm-in-robotic-arm with the new race of machines and robots that will undoubtedly make our lives easier.

Separating Great Data Scientists From OK Data Scientists: Statistics

In this contributed article, technology writer and blogger Kayla Matthews discusses the importance of a strong foundation in statistics and probability theory for practicing data scientists. Data scientists, thanks to their background in statistics, can look at a set of information and come up with important trends and patterns.

The Data Scientist’s Guide to Apache Spark

Looking to dive deeper into the more cutting edge machine learning use cases in Apache Spark? To successfully use Spark’s advanced analytics capabilities including large scale machine learning and graph analysis, check out The Data Scientist’s Guide to Apache Spark, from our friends over at Databricks.

The Data Scientist’s Guide to Apache Spark™

For data scientists looking to apply Apache Spark’s advanced analytics techniques and deep learning models at scale, Databricks is happy to provide The Data Scientist’s Guide to Apache Spark. Download this eBook to: Learn the fundamentals of advanced analytics and receive a crash course in machine learning. Get a deep dive on MLlib, the primary […]

Interview: Shalini Agarwal, Director, Engineering and Product at LinkedIn

I recently caught up with Shalini Agarwal, Director, Engineering and Product at LinkedIn, to discuss how we need more data scientists to make our applications smarter; however we can make them more efficient and accomplish more with data scientists by having automated workflows and tools. These tools can be used by non-data scientists to leverage the established workflows and remove the repetitive tasks from the mountain of tasks expected from a data-scientist.

Research of 1,001 Data Scientist LinkedIn Profiles

Data science is a super-hot topic and the data scientist job is the sexiest job of the 21st century according to the Harvard Business Review. But how does one actually become a data scientist? 365 DataScience gathered data from 1,001 publicly listed LinkedIn profiles of data scientists and prepared a compelling report “Studying 1,001 Data Scientist LinkedIn Profiles.”

Research of 1,001 Data Scientist LinkedIn Profiles

Data science is a super-hot topic and the data scientist job is the sexiest job of the 21st century according to the Harvard Business Review. But how does one actually become a data scientist? For you it doesn’t really matter how others became data scientists. What you are interested in is whether you can become […]

The Role of the Modern Data Scientist — And How Everyone Has the Power to Become One

In this contributed article, Arijit Sengupta, Head of Einstein Discovery at Salesforce, explains – given the role of the modern data scientist, everyone has the power to become one. We aren’t that far off from a future when everyone will have the tools to be considered a citizen data scientist.