No matter what field you’re considering entering, it’s important to understand what a line of work involves before setting out on an irreversible career path. Data science is a hot topic in our digital age, and jobs in this field are growing at a rapid pace. But what do data scientists really do? If you’re considering a becoming a data scientist, read on for more information about what your life will be like in this evolving industry.
Put simply, data scientists look at compiled information and extract meaning. The daily duties of the job typically follow a process: first, a client or employer brings up a problem that data analysis could solve, and then a data scientist frames the issue to determine what information must be extracted.
Data collection and processing come next. The processing segment involves getting rid of errors and/or corrupt information that could potentially pollute later steps. Data scientists then explore the data with powerful tools such as algorithmic software to discover meaning in the statistics. Finally, data scientists are tasked with communicating their findings in a manner that laypersons can understand.
Although there is not a uniform set of ideal characteristics, some traits are particularly helpful for data scientists to possess. For example, the best data scientists are analytical thinkers who work well under pressure. In addition, they are team players and strong collaborators who can stay motivated while working independently.
If the companies that hire data scientists are not accustomed to using data to make business decisions, they may not be familiar with the various ways in which they might benefit from this information. So it's also valuable for data scientists to have a natural curiosity; their inquiring nature will help them figure out how to take advantage of new findings in innovative ways.
Learning quickly and being willing to stay abreast of new software and other tools also sets data scientists apart. Since most data scientists use highly advanced interfaces while digging through data, people who pursue this career path must expect to grasp the functionality of those programs, expand their knowledge as new features or editions come out, and prepare to teach other team members about such tools.
When surveying career possibilities, individuals understandably want to know whether there will be a long-term demand for the work they do, or if they might be left jobless at some point in their careers. Fortunately for aspiring data scientists, the career they want seems here to stay.
Companies are increasingly recognizing that data scientists are integral parts of their workforce, especially when those businesses want to put their growing collections of data to use to gain additional insights. Sophisticated data analysis is essential for smart forecasting and the boosting of profits.
Data can assist companies in understanding what their target audiences want; rather than guessing, they can look to the data for a clear, unambiguous answer. The results of strong data analysis are clear; statistics indicate that the most-loved companies are those that prioritize applying data science to the customer experience.
All of this exists as proof that data scientists should expect to enjoy job security for the foreseeable future. A prediction from IBM says that data science jobs will increase 28 percent by 2020, accounting for about 700,000 new job openings.
One of the most frequently asked questions from people who want to become data scientists is what kind of training they will need to be maximally competitive in the job market. According to a 2017 Kaggle report, that answer is not straightforward. Most data scientists achieved master's degrees, but in cases where the respondents earned at least $150,000 annually, they were just as likely to hold doctorates.
A more detailed breakdown of the Kaggle data showed that almost 69 percent of data scientists majored in math or computer-oriented subjects, demonstrating that while graduates don't necessarily need data science degrees, some tracks lend themselves to the sector more easily than others. In addition, many data scientists are at least partially self-taught. There is an abundance of resources to help people learn foundational skills without getting data science degrees.
Another way of looking at data science training is to focus on the tools used. For example, the insights from Kaggle indicate that it could be advantageous (or even necessary) for future data scientists to master the Python programming language. More than 76 percent reported that Python was the top data analysis tool used in their workplace, and more than 61 percent suggested that Python is the first programming language aspiring data scientists should learn. R was the second most useful analysis tool cited, with 24 percent of individuals recommending that new data scientists learn R before any others.
Job satisfaction is determined by a company’s culture, how far people have to commute to and from the office, the benefits packages offered, time spent travelling for work, professional development opportunities, flexibility for those who want to work remotely, and other factors that contribute to work-life balance. In many of these areas, data science careers earn high ratings. In fact, Glassdoor’s 2018 listing of the 50 best jobs in America named data science as the top-ranked career, with job satisfaction garnering a score of 4.2 out of 5.
While the majority of data scientists report very high job satisfaction, it is still important to keep your particular goals and values in mind when entering this field. For example, if you thrive in an intimate work environment and want to do work that is directly related to your interests, you might prefer a data science job at a smaller company. If you enjoy a corporate culture and hope to work for a company with wide influence, a job at a larger firm might fit the bill.
A rising demand for data scientists over the past decade has led to six-figure salaries for those employed in the field long-term. Data collected by Indeed.com shows that the average amount earned is $132,952 annually. Numerous other sources, including Glassdoor’s 50 best jobs report, also list average data science salaries of over $100,000.
If you are trying to determine whether the effort you must put into become data scientists will have a significant financial payoff, research indicates that it will. That said, it takes time to build up to a six-figure salary. According to ZipRecruiter, the nationwide average salary for entry-level data scientists in the U.S. is $68,999.
It's also worth realizing that some job markets in the U.S. pay data scientists higher-than-average salaries. Portland, Oregon and Phoenix, Arizona are two cities in which the average salaries of data scientists reach more than $140,000. Factors to consider before relocating include cost of living in a given city, the transportation options for getting to and from work, local schools, and the crime rate.
Once you have developed the necessary skills to become a data scientist, you might be hired as full or part-time employee and receive a regular salary and benefits. Increasingly, however, employers are offering contract roles to deal with a substantial skills shortage in the industry.
James Sandoval is the CEO and co-founder of Measure Match, a marketplace platform that connects data professionals with companies that want to hire them as contractors. Sandoval says the average contract value is $10,000 and that there are currently about 1,500 people seeking contract work on the platform.
Sandoval notes that most employers who hire workers on Measure Match prefer contractors who can work on-site rather than remotely. Physical presence in the office environment makes it easier for data science contract workers to get engrossed in the company culture and absorb knowledge.
But remote work is not out of the question. Yanir Seroussi is a data scientist who works remotely and blogs in detail about the how he got to that point. His experience involved both a pretrial test and a trial project, in addition to having a conversation with the company's lead data scientist, before being granted a chance to show off his expertise. A look at Seroussi's bio shows a rich background in data science, suggesting he built his career thoughtfully and with intention.
Because employers are so eager to bring data scientists onto their teams, individuals who are eyeing mid-career switches might consider making the jump to data science. But data science experts warn that it's not realistic to expect to get the necessary expertise simply by doing on-the-job training or taking free online courses. Moreover, data science is not as simple as learning another programming language.
People who are serious about pursuing data science as a new career should expect to invest more than a year towards receiving the training that will help them succeed in this different role. That said, they needn’t necessarily put their old career paths completely behind them. Some careers lend themselves exceptionally well to data science. Those individuals who have university-level educations focused on math or computer-related fields, and/or who have worked as IT managers or computer programmers, are extremely well suited to shift paths into data science.
While data scientists are in-demand, data science is also an incredibly competitive sector. Standing out in the field involves more than getting an education; real-world experience and personal connections are invaluable. That means people who want to work as data scientists should regularly look for networking opportunities and seize them as often as they can.
Getting to know people could be as simple as going to a local Meetup group at a coffee shop and being as outgoing as possible. Alternatively, many universities have clubs geared toward people in data science programs or other relevant subjects areas.
Those who find a lack of ways to meet other data scientists face-to-face shouldn't feel discouraged. Data Science Learning Club is an online community launched by Renee Teate, the host of the "Becoming a Data Scientist" podcast. She wanted to create a platform where data scientists could work on projects together, especially during the early stages of their careers. The site now hosts tens of thousands of posts and can be a go-to to meet others in the field.
Through networking, beginner data scientists may have the opportunity to team up with more seasoned professionals and participate in projects that give them tremendous amounts of experience. In addition, networking can help data scientists build long-term relationships in their field and can inform them about job openings before those openings become public knowledge.
Brands ranging from PetSmart to Target highlight a need for data scientists in their job listings. That means that people in this career path can use their skills within a seemingly endless assortment of possible workplaces. Data science is still an emerging field, and some of the employers hiring for the role are still relatively new to applying data to their business practices. Staying marketable as a data scientist means being mindful of the flexibility and adaptability required for this digitally-driven career.