{"id":8562,"date":"2019-03-29T16:12:07","date_gmt":"2019-03-29T20:12:07","guid":{"rendered":"https:\/\/blog.brainstation.io\/?p=8562"},"modified":"2020-12-02T13:54:00","modified_gmt":"2020-12-02T18:54:00","slug":"a-day-in-the-life-of-a-data-scientist","status":"publish","type":"post","link":"https:\/\/brainstation.io\/blog\/a-day-in-the-life-of-a-data-scientist","title":{"rendered":"A Day in the Life of a Data Scientist"},"content":{"rendered":"

Of all the disciplines examined in Brainstation\u2019s <\/span>2019 Digital Skills Survey<\/span><\/a>, <\/span>data science<\/span> may encompass the widest range of applications. But although data science has existed for decades, it has only recently come into full bloom. \u201cAs the availability of data has expanded, companies have realized how important data science can be,\u201d says Briana Brownell, Founder and CEO of <\/span>Pure Strategy<\/span>, and a Data Scientist for 13 years. \u201cEvery company now needs to have a partial focus on technology.\u201d Just this week, for instance, McDonald\u2019s <\/span>paid<\/span><\/a> an estimated $300 million USD to acquire its own big data firm. <\/span>
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\n<\/span>It\u2019s no wonder, then, that competition for Data Scientists is incredibly high. In just two years, the demand is expected to grow by 28 percent, equivalent to about 2.7 million new jobs. That\u2019s more openings than new graduates will be able to fill\u2014meaning tech workers in other fields will have to brush up their skills and transition into data to meet this demand.<\/span><\/p>\n

In fact, our survey suggests this is already happening. Roughly four out of five data pros began their career doing something else, and 65 percent of all Data Scientists have been working in the field for five years or less. This huge influx of new minds has a double-edged effect, says Brownell; on one hand, \u201cthere are a lot of new ideas coming in,\u201d she says. \u201cWhen I look at some of the content coming out of the data science community, I\u2019m surprised how much innovation there is.\u201d The flipside, though, is a tendency to reinvent the wheel.<\/span><\/p>\n

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Become a Data Scientist in just 12 weeks!<\/p>\n

BrainStation’s Data Science Diploma Program<\/a> is a full-time, 12-week program that provides professionals with the skills and experience to start a new career in data.<\/p>\n

Speak to a Learning Advisor<\/a><\/p>\n<\/div>\n<\/div>\n<\/div>\n

Recruiting and Reskilling<\/b><\/h3>\n

High demand for Data Scientists is great if you are one (or thinking about becoming one), but for employers, recruitment can be a daunting challenge. Here, reskilling is an obvious solution; it may be more cost-effective to retrain a current employee in data science than to headhunt a new one. <\/span><\/p>\n

But even if you plan to hire a new data science team, your organization as a whole may need to brush up on its data literacy, Brownell cautions. \u201cEverybody wants to work on something that has an impact on their workplace, that makes people\u2019s lives better,\u201d she says. \u201cIf your company culture isn\u2019t such that [your Data Scientists] can make an impact, it\u2019s almost impossible to hire.\u201d Leadership must be capable not only of communicating to potential hires how they\u2019ll be able to contribute\u2014but of comprehending the proposals their data science team eventually puts forward as well.<\/span><\/p>\n

Unfortunately, Brownell says, \u201cthe uncomfortable majority are the companies that haven\u2019t figured things out.\u201d Our survey backs this up: most respondents (52 percent) described the level of data literacy across their organizations as \u201cbasic,\u201d with \u201cintermediate\u201d the next most common response (31 percent). This suggests that some foundational data science training could be useful for a large majority of companies\u2014especially in leadership.<\/span><\/p>\n

This need for improved data literacy\u2014and communication\u2014is heightened by the way most data science teams are structured: as a discrete team, usually with 10 people or fewer (according to 71 percent of respondents), and often five or fewer (38 percent). These close-knit teams can\u2019t afford to be isolated. \u201cIndividuals who work within larger companies are usually within a small data science\u2013specific group, and their clients are internal\u2014other parts of the organization,\u201d Brownell explains, \u201cso it\u2019s a team that has to operate across many different areas of the organization.\u201d<\/span><\/p>\n

So What <\/b>is<\/i><\/b> Data Science?<\/b><\/h3>\n

The common perception (that Data Scientists crunch numbers) is not too far off the mark, Brownell says. \u201cThere are a lot of datasets that need to have insights revealed from them, and that involves a lot of steps like model building and data cleanup, and even just deciding what data you need.\u201d Ultimately, though, this effort is goal-oriented: \u201cAt its core, you need to do something with the data.\u201d<\/span><\/p>\n

For that matter, data isn\u2019t always numbers. While a majority of respondents (73 percent) indicated they work with numerical data, 61 percent said they also work with text, 44 percent with structured data, 13 percent with images and 12 percent with graphics (and small minorities even work with video and audio\u20146 percent and 4 percent respectively). These survey results hint at the ways data science is expanding far beyond financial tables, enlisting people for such projects as maximizing customer satisfaction or gleaning valuable insights from the fire hose of social media.<\/span><\/p>\n

As a result, \u201cthere\u2019s enormous variety within the data science field,\u201d Brownell says. \u201cEvery industry has its own take on what types of data the Data Scientists work on, the types of outcomes they\u2019re expecting, and how that fits into their company\u2019s leadership structure.\u201d <\/span><\/p>\n

In every case, though, the goal is to leverage data to help a company make better decisions. \u201cThat could be making products better, understanding the market that they want to go into, retaining more customers, understanding their labor force usage, understanding how to make good hires\u2014all kinds of different things.\u201d<\/span><\/p>\n

The Nuts and Bolts of a Data Science Job<\/b><\/h3>\n

In some areas of tech, becoming a generalist can be your best foot in the door\u2014<\/span>not so with data science<\/span>. Employers typically look for skills specialized to their industry. Because data science comes in so many different flavors, our survey probed deeper, examining five main job categories: data analyst, researcher, business analyst, data and analytics manager, and data scientist proper.<\/span><\/p>\n

Across all these job titles, \u201cdata wrangling and cleanup\u201d takes up the bulk of one\u2019s time\u2014but to what end? Most often, the goal is to optimize an existing platform, product or system (45 percent), or to develop new ones (42 percent). Digging deeper, we found that \u201coptimizing existing solutions\u201d tends to fall to Business Analysts and Data Analysts, while \u201cdeveloping new solutions\u201d more often falls to Data Scientists and researchers.<\/span><\/p>\n

The techniques Data Scientists use vary across specializations, too. Linear regression was a common tool across all categories, cited by 54 percent of respondents, but there were a few surprises when we looked at the software people are using.<\/span><\/p>\n

Excel\u2014that workhorse of dataset manipulation\u2014is virtually ubiquitous, cited by 81 percent of all respondents, and the most popular tool in every category except Data Scientists proper (who most frequently rely on Python\u2014and also cited a larger toolkit than other categories). What makes Excel so inescapable, even in 2019?<\/span><\/p>\n

\u201cThe thing that I love about Excel is how it allows you to see the data and get an intuitive feel for it,\u201d Brownell explained. \u201cWe also use a lot of Python, and in that case, when you\u2019re doing analytics on a data file, it\u2019s hidden; unless you specifically program part of your code to do some visualization of the raw data that you\u2019re analyzing, you don\u2019t see it. Whereas with Excel, it\u2019s right in front of you. That has a lot of advantages. Sometimes you can spot issues with the data file. I don\u2019t see Excel disappearing from analysis ever.\u201d<\/span><\/p>\n

That said, there\u2019s still a long list of other programs used in the field\u2014unsurprising given its diversity. SQL (43 percent) and Python (26 percent) lead in popularity, with Tableau (23 percent), R (16 percent), Jupyter Notebooks (14 percent), and a handful of others clocking up significant numbers\u2014not to mention the whopping 32 percent of respondents who cited \u201cother\u201d tools, even given this already long list. <\/span><\/p>\n

What is the Future of Data Science?<\/b><\/h3>\n

Finally, we asked what trends will shape the digital landscape over the next five to 10 years. Machine learning and AI\u2014both of which have applications within data science\u2014were overwhelmingly the developments respondents expect to have the biggest impact, at 80 percent and 79 percent respectively. This despite the fact that less than a quarter (23 percent) of them currently work with AI.<\/span><\/p>\n

Artificial intelligence can \u201cabsolutely\u201d transform data science, confirms Brownell, whose company develops AI products. \u201cThat\u2019s really the glory of unsupervised learning methods. We only have so much time to look at these datasets, and especially with large ones, it\u2019s very difficult to do everything. AI tools can help reveal something that maybe you wouldn\u2019t have thought to look for. We\u2019ve definitely had that happen.\u201d<\/span><\/p>\n

Other trends Data Scientists expect to dominate in the near future: the Internet of things (51 percent), blockchain (50 percent) and eCommerce (36 percent), augmented reality and virtual reality (38 percent and 27 percent), and even voice-based experiences (25 percent)\u2014all significant showings, and all areas where data science can be put to good use.<\/span><\/p>\n

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Become a Data Scientist in just 12 weeks!<\/p>\n

BrainStation’s Data Science Diploma Program<\/a> is a full-time, 12-week program that provides professionals with the skills and experience to start a new career in data.<\/p>\n

Speak to a Learning Advisor<\/a><\/p>\n<\/div>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

Roughly four out of five data professionals began their career doing something else, and they’re bringing new ideas with them. <\/p>\n","protected":false},"author":7,"featured_media":8566,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":[],"categories":[330,343],"tags":[405,556,557],"yoast_head":"\nA Day in the Life of a Data Scientist | BrainStation\u00ae Blog<\/title>\n<meta name=\"description\" content=\"Roughly four out of five data professionals began their career doing something else, and they're bringing new ideas with them. 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