The field of data science is moving fast. People are claiming to be data scientists; yet the knowledge, experience, and backgrounds of those people can be very different. Different is not bad. However, there a little standards around what exactly a data scientist is.
Sticking with this week’s theme of “What is a Data Scientist”, an organization titled, Initiative for Analytics and Data Science Standards (IADSS) has kicked-off a research study at global scale. The study aims to gain insight about the analytics profession in the industry and help support the development of standards regarding analytics role definitions, required skills and career advancement paths. This will help set some industry standards which in turn could support the healthy growth of the analytics market.
If you want to be a part of this initiative and help collectively define industry standards, I encourage you to take part in the research. The survey takes approximately 5 minutes and answers for the survey will be kept anonymous. More details are provided at introduction pages of the survey at Data Science Industry Standards Research Survey
The profile of a data scientist is changing slightly as the profession becomes more solidified. Data Science 365 conducts a study to determine some of the characteristics of a “typical data scientist.” The below infographic covers a wealth of information from programming languages used to educational backgrounds to locations. It is definitely worth looking at to understand the attributes of a data scientist in 2019.
The year 2019 has begun and many people have plans to become a data scientist. That is because data scientist has been ranked as one of the top jobs for the last several years. After learning the necessary skills, preparing and completing the interview can be an intimidating task. That is why interview practice is so important, and Pramp provides a free online environment for practicing a data science interview.
What is Pramp?
Pramp is a free peer-to-peer matching platform that enables you to practice a technical interview. Aftersigning up, here is how the process works:
You schedule an interview by choosing a date and time for when you would like the interview to occur.
You then prepare for the interview with the materials Pramp provides. Pramp will supply interview questions and guidelines for being best prepared.
Finally, you conduct the interview where you and the other person take turns interviewing each other.
If desired, the process can be repeated multiple times.
Pramp takes two people preparing for a data science interview and matches them together.
Being on both sides of the interview is surprisingly very helpful. It allows you to practice your responses, and it allows you to understand what is important to the person asking questions. It is often more about understanding the problem and thinking through a solution, rather than identifying a right or wrong answer.
As a bit of a bonus, if you enjoyed interviewing with your peer and you’d like to practice with him/her again, Pramp has a feature for that. Who knows, that peer may become a friend or a coworker in the future.
Why Practice the Interview?
Even the best data scientists and engineers struggle to pass technical interviews. Let’s face it, technical interviews are challenging and intimidating. For many, the biggest challenge isn’t the coding question, but rather staying focused while solving a problem out loud and under time pressure in front of an interviewer.
Data from over 180,000 interviews scheduled on Pramp has shown that those who completed face-to-face mock interviews performed significantly better than those who just practiced alone. Plus, Pramp users have already found jobs at companies like: Google, Amazon, Facebook, Twitter, Microsoft, Spotify, and many others.
More About Pramp
Pramp, a Y Combinator-funded company, has tackled the challenge of technical interviews by offering a free peer-to-peer mock interview platform helping data scientists and engineers practice technical interviews. In addition to data science, Pramp also offers interviews for:
The textbook for the UC Berkeley Data Science course is available for free online at Computational and Inferential Thinking.It is an online textbook and appears to be created as a collection of Jupyter notebooks. Here are some of the topics covered:
Amazon just launched ‘Machine Learning University’ to all developers. It is the same training available for internal Amazon Employees. If you are looking to learn about Amazon Web Services (AWS) offerings for data science, now might be a great time to learn. Plus, Amazon announced some new certifications for machine learning. There are 4 different learning paths available, depending upon your goals and future job aspirations:
Reinforcement Learning: An Introductionby Rich Sutton and Andrew Barto was recently released on October 15, 2018. The authors were kind enough to put a late draft version of the book online as a PDF. If you are hoping to learn about Reinforcement Learning, this is a great place to start.