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  • For the last 3 years we at 365 Data Science have been trying to answer one big question:

  • What makes a data scientist?” Since we are talking data science, the only

  • logical way to approach the question is to ask the data. And that's what we've done

  • for 3 consecutive years. Since 2018 we have explored 1001 data scientist LinkedIn profiles

  • to uncover the most interesting trends in the data science field.

  • In this video we will go through the most important findings from the last 3 years.

  • In fact, we have created a very cool and interactive PowerBI dashboard which you can use to analyze

  • the data yourself. Link in the description. If you prefer to digest our own analysis just

  • carry on with the video. According to the data, the average data scientist

  • from 2018 to 2020 is a male with a second-tier degree, coming from a quantitative background,

  • which is not necessarily data science or computer science. Their preferred programming language

  • is Python, but they'd often know R and SQL. Many of the new data scientist positions are

  • being filled by people who are already data scientists, so the field feels much more saturated.

  • Getting into data science still looks like a great opportunity, but the 'data scientist'

  • position becomes more and more exclusive. For sure, the current COVID-19 pandemic would

  • have a say on the demand for data science professionals, too. However, this is maybe

  • the right time for you to dedicate yourself to starting a career in data science, once

  • the pandemic is over. And if you've set your sight on a career

  • in data sciencewe've got you covered. We developed the '3-6-5 Data Science Program'

  • to help people of all backgrounds enter the field of data science. We have trained more

  • than 450,000 people around the world and are committed to continue doing so. If you are

  • interested to learn more, you can find a link in the description that will also give you

  • a special offer on all of our plans. That said, let's dive deeper into the education,

  • years of experience and programming skills of a data scientist from 2018 to 2020.

  • Education Our sample shows that at least 80% of the

  • people held a minimum of a Master's degree. This isn't as surprising, considering data

  • science is a field that expects advanced know-how from the personusually achieved by graduate

  • or postgraduate types of education, or independent advanced research in other cases.

  • And while specialization is important, a Ph.D. is not really a requirement for breaking into

  • data science. Indeed, over the years, the number of PhD holders has remained consistent,

  • making up about 27 per cent of our study. On the contrary, starting from 2018 there

  • was a rise of about 20% in the professionals with a Master's degree compared to the 2019

  • cohort. Area of Studies

  • In 2018 and 2019, “Economics and Social Sciences”, “Computer ScienceandStatistics

  • and Mathematicswere filling up the top 3 most popular fields of study of data scientists.

  • 2020 was the first year ever that featuredData Science and Analysis” (22%) as the

  • top degree. Therefore, we can assume that universities have started to catch up with

  • the demand for data science education. Graduates form the Engineering, Natural Sciences,

  • and Other fields constitute approximately 11% of our data each. This indicator has remained

  • stable throughout the years. Interestingly, in 2020, most women in our

  • sample have earned a 'Statistics and Mathematics' related degree (24% of the female cohort).

  • In comparison, men most likely earned a degree in Data Science and Analysis (22%), with Computer

  • Science (19%) being a close second. Years on the job

  • If you are changing jobs, or working through your data analyst years, you must be wondering

  • whether you've got the right experience for the position. In terms of tenure, in 2018

  • almost all data scientists were 'newcomers' to the 'data scientist' position. Some

  • of this was driven by name changes to their occupation, but mostlythe supply was

  • so little that it was way easier to enter the field.

  • Currently, we are observing a much tougher playing field. The majority of data scientists

  • have more than 2 years on the job and it seems like a very small proportion of the total

  • data scientist pool is new. In fact, in 2020, 52% of the cohort, held the title 'data

  • scientist' at their previous position.

  • Programming languages

  • The programming skills a data scientist needs are arguably the most interesting area of

  • research (at least for us). For many years, R was the preferred language a data scientist

  • was expected tospeak”. In 2018 and 2019 Python started 'eating away at R'. And

  • it did so at a very fast pace. In 2020, we have reached the point where Python is by

  • far the preferred programming language in the data science community with 74% adoption!

  • R is not completely overthrown but becomes less and less favored among professionals.

  • An interesting development is the rapid year-to-year growth of SQL users. In 2020, more than 50%

  • of data scientists actively use the language. One common assumption is that companies expect

  • from a data scientist to solve all their data related problems, no matter if they are related

  • to data engineering or data architecture. On another note, the adoption of BI software

  • such as PowerBI and Tableau has also demanded a higher understanding of databases. Inevitably,

  • SQL had to be added to the data scientist toolbelt for the sake of 'getting the job

  • done'. Alright.

  • So, looking at the data, the answer toWhat makes a data scientist?” becomes clearer.

  • Professionals are paving the way and universities are starting to provide a more tailored education.

  • From a career point of view, it seems that it is getting harder to become a data scientist

  • as data scientists tend to stay on their job for a longer period of time. However, different

  • opportunities to get into the field remain, as demand still varies across countries and

  • industries. One thing is for surelearn Python, if

  • you are to become a data scientist! And if you'd like to become an expert in

  • all things data science, subscribe to our channel.

  • Thanks for watching and good luck!

For the last 3 years we at 365 Data Science have been trying to answer one big question:

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2020年與2019年與2018年成為數據科學家需要什麼? (What Do You Need to Become a Data Scientist in 2020 vs 2019 vs 2018?)

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    林宜悉 發佈於 2021 年 01 月 14 日
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