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Top 5 Must-Read Answers – What does a Data Scientist do on a Daily Basis?

来源:分析大师 | 2019-06-27 | 发布:经管之家

I’m a curious person by nature. Whenever I come across a concept I haven’t heard of before, I can’t wait to dig in and find out how it works. This has come in quite handy in my own data science journey.But before I landed my first break in data science, I was always curious about what data scientists actually did every day. Was I supposed to simply build models all the time? Or was the oft-quoted saying about spending 70-80% of our time cleaning data actually true?I’m sure you have asked (or at least wondered) about this too. The role of a data scientist might be the “sexiest job of the 21st century”, but what does that entail on a day-to-day basis?I decided to research this. I wanted to expand my horizons and understand how data scientists look at their role in different domains (such as NLP). This helped me gain a broader understanding of our role and why we should always read different perspectives when it comes to data science.So, here is a list of the top 5 answers to help you get a sense of what the typical routine of a data scientist is. Prepare to be surprised – building models isn’t the primary (and only) function in a data scientist’s day-to-day tasks!I also encourage you to take part in a discussion on this question here. This will enrich your current understanding of what a data scientist does and your thoughts will foster a discussion among our community!Note: I have taken the answers verbatim from Quora and added my thoughts right at the beginning of each answer. This will help you get a good perspective of what the answer covers without diluting the author’s thoughts. Enjoy!I like this answer because it’s crisp, to-the-point and simple. The author has even designed a flow diagram and explained his thought process in a wonderfully illustrated way. Here is his answer in full:Machine learning is very process oriented. Therefore, Im always somewhere in one of the pictures below:Machine learning engineers spend a ton of time in the first two pictures (or stages). The fun part is really in the third stage but its only a small part of what happens in the real world.Some key things to keep in mind about data science in the real world:I really like the use of visualization by Vinita. The percentage-wise description of each data science task is helpful and insightful. Vinita has also leaned on her experience to explain the step-by-step work a data scientist does. It’s a must-read answer!Contrary to popular belief, Data Science is not all glamour. The following survey results by CrowdFlower accurately sum up a typical day for a Data Scientist:There is a lot of backtracking involved. Sometimes you even need to be able to predict what consequences removing/adding a variable might have.This is a superb answer and one I can relate to. Note that machine learning, the most anticipated aspect of a data scientist’s job, only occupies 5% of the total time! Just like Vinita, he has also explained his tasks in terms of percentage. Here is Justin’s view:The author, Tim Kiely, uses a Venn diagram to explain what data science is. Just take a look at this Venn diagram below – it will blow your mind. Tim additionally talks about what data scientists are supposed to be by taking a somewhat contradictory view of the general definition. Here is Tim’s answer:The “Data Scientist” is a bit of a myth, in my opinion. Not to say they aren’t out there but they are far rarer than is popularly understood and are more of the exception than the rule.I liken it to the “Web Master” title of the dot-com bubble – these supposed people who could do full stack programming, front end development, marketing, everything. All of those roles/skills were always specialized and remain so today.“Data Scientists” are supposed to be database architects, understand distributed computing, have a deep understanding of statistics AND some area of business or field expertise. That’s asking a lot when any one of those skill sets can take a career to build.

The Data Scientists I’ve worked with typically have a Ph.D. in A.I. or Machine learning and are effective communicators, which gives them the ability to direct the analysts, DevOps people, programmers and DBA’s at their disposal to solve problems with data-driven solutions. They outline the desired solution and leave it to their teams to fill in the gaps.Let’s drill down into a particular specialization of machine learning. One of my favorites – Natural Language Processing (NLP)! I wanted to bring out a machine learning engineer’s view here (a role every data scientist should become familiar with). Check out Evan’s full response:Currently working on NLP, for the most part, including intent classification

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