AI News, Data Science Career Question #1: “Is Data Science For Me?”
Data Science Career Question #1: “Is Data Science For Me?”
Last time I wrote about why it’s worth it to become a data scientist.
This time I’d like to answer another important and very common data science career question: is data science for you at all?
It’s really important to clarify these questions because many articles on the topic imply that a data science career is an easy way to become rich, happy and smart for good.
it requires continuous learning and practicing of difficult and complex concepts, technically during your entire career.
I’ve found that one can only work on a specific skill without losing motivation if she enjoys the process of learning and practicing.
I won’t cite cliches like “do what you love, and you’ll never work another day in your life”
When I have to understand a new machine learning model for a predictive analytics project, I get excited and I can easily spend hours studying the mathematical concept behind the model.
have to add that I am really, really lucky because I had great math teachers in school who always presented maths as the single most exciting discipline on Earth.
I know that many people are not that lucky and had bad and demotivating teachers… Even if that’s you, it doesn’t mean that you would not like mathematics now.
you can imagine yourself coding for hours and you can also imagine that you would actually love to do that, that’s another check mark on your list.
There are several ways to build up an efficient data team but by far the most common solution is to have a central data team and assign its members to other teams within the company (e.g.
means that you will actually have to spend a fairly large proportion of your time with non-data people: managers, developers, marketers, sales people, etc. And
But if working with a chatty marketer or a strong-willed leader is not your cup of tea, then most probably you wouldn’t enjoy your data science career, either.
Also, very often, you will have to simplify things for the sake of understanding (so you won’t say “classification with logistic regression machine learning model”).
count as evergreen knowledge, new methods, tools, tricks and solutions are coming out year by year.
As a data scientist you can easily find yourself at an IT startup, at a logistics company, at a trading company or wherever.
And every time you change domain (because you go to another company), you have to pick up the domain knowledge… Analyzing a viral marketing campaign requires understanding of how viral marketing works.
Turing award winner Jim Gray imagined data science as a 'fourth paradigm' of science (empirical, theoretical, computational and now data-driven) and asserted that 'everything about science is changing because of the impact of information technology' and the data deluge. When Harvard Business Review called it 'The Sexiest Job of the 21st Century' the term became a buzzword, and is now often applied to business analytics, or even arbitrary use of data, or used as a sexed-up term for statistics. While many university programs now offer a data science degree, there exists no consensus on a definition or curriculum contents. Because of the current popularity of this term, there are many 'advocacy efforts' surrounding it. The term 'data science' (originally used interchangeably with 'datalogy') has existed for over thirty years and was used initially as a substitute for computer science by Peter Naur in 1960.
Now the data in those disciplines and applied fields that lacked solid theories, like health science and social science, could be sought and utilized to generate powerful predictive models. In an effort similar to Dhar's, Stanford professor David Donoho, in September 2015, takes the proposition further by rejecting three simplistic and misleading definitions of data science in lieu of criticisms. First, for Donoho, data science does not equate big data, in that the size of the data set is not a criterion to distinguish data science and statistics. Second, data science is not defined by the computing skills of sorting big data sets, in that these skills are already generally used for analyses across all disciplines. Third, data science is a heavily applied field where academic programs right now do not sufficiently prepare data scientists for the jobs, in that many graduate programs misleadingly advertise their analytics and statistics training as the essence of a data science program. As a statistician, Donoho, following many in his field, champions the broadening of learning scope in the form of data science, like John Chambers who urges statisticians to adopt an inclusive concept of learning from data, or like William Cleveland who urges to prioritize extracting from data applicable predictive tools over explanatory theories. Together, these statisticians envision an increasingly inclusive applied field that grows out of traditional statistics and beyond.
For the future of data science, Donoho projects an ever-growing environment for open science where data sets used for academic publications are accessible to all researchers. US National Institute of Health has already announced plans to enhance reproducibility and transparency of research data. Other big journals are likewise following suit. This way, the future of data science not only exceeds the boundary of statistical theories in scale and methodology, but data science will revolutionize current academia and research paradigms. As Donoho concludes, 'the scope and impact of data science will continue to expand enormously in coming decades as scientific data and data about science itself become ubiquitously available.'
Data Scientist: The Sexiest Job of the 21st Century
When Jonathan Goldman arrived for work in June 2006 at LinkedIn, the business networking site, the place still felt like a start-up.
For one thing, he had given Goldman a way to circumvent the traditional product release cycle by publishing small modules in the form of ads on the site’s most popular pages.
Through one such module, Goldman started to test what would happen if you presented users with names of people they hadn’t yet connected with but seemed likely to know—for example, people who had shared their tenures at schools and workplaces.
Goldman is a good example of a new key player in organizations: the “data scientist.” It’s a high-ranking professional with the training and curiosity to make discoveries in the world of big data.
If your organization stores multiple petabytes of data, if the information most critical to your business resides in forms other than rows and columns of numbers, or if answering your biggest question would involve a “mashup” of several analytical efforts, you’ve got a big data opportunity.
Much of the current enthusiasm for big data focuses on technologies that make taming it possible, including Hadoop (the most widely used framework for distributed file system processing) and related open-source tools, cloud computing, and data visualization.
Greylock Partners, an early-stage venture firm that has backed companies such as Facebook, LinkedIn, Palo Alto Networks, and Workday, is worried enough about the tight labor pool that it has built its own specialized recruiting team to channel talent to businesses in its portfolio.
“Once they have data,” says Dan Portillo, who leads that team, “they really need people who can manage it and find insights in it.” If capitalizing on big data depends on hiring scarce data scientists, then the challenge for managers is to learn how to identify that talent, attract it to an enterprise, and make it productive.
In a competitive landscape where challenges keep changing and data never stop flowing, data scientists help decision makers shift from ad hoc analysis to an ongoing conversation with data.
More enduring will be the need for data scientists to communicate in language that all their stakeholders understand—and to demonstrate the special skills involved in storytelling with data, whether verbally, visually, or—ideally—both.
But we would say the dominant trait among data scientists is an intense curiosity—a desire to go beneath the surface of a problem, find the questions at its heart, and distill them into a very clear set of hypotheses that can be tested.
As Portillo told us, “The traditional backgrounds of people you saw 10 to 15 years ago just don’t cut it these days.” A quantitative analyst can be great at analyzing data but not at subduing a mass of unstructured data and getting it into a form in which it can be analyzed.
A data management expert might be great at generating and organizing data in structured form but not at turning unstructured data into structured data—and also not at actually analyzing the data.
Several universities are planning to launch data science programs, and existing programs in analytics, such as the Master of Science in Analytics program at North Carolina State, are busy adding big data exercises and coursework.
The Insight Data Science Fellows Program, a postdoctoral fellowship designed by Jake Klamka (a high-energy physicist by training), takes scientists from academia and in six weeks prepares them to succeed as data scientists.
As one of them commented, “If we wanted to work with structured data, we’d be on Wall Street.” Given that today’s most qualified prospects come from nonbusiness backgrounds, hiring managers may need to figure out how to paint an exciting picture of the potential for breakthroughs that their problems offer.
One described being a consultant as “the dead zone—all you get to do is tell someone else what the analyses say they should do.” By creating solutions that work, they can have more impact and leave their marks as pioneers of their profession.
As the story of Jonathan Goldman illustrates, their greatest opportunity to add value is not in creating reports or presentations for senior executives but in innovating with customer-facing products and processes.
At Intuit data scientists are asked to develop insights for small-business customers and consumers and report to a new senior vice president of big data, social design, and marketing.
New conferences and informal associations are springing up to support collaboration and technology sharing, and companies should encourage scientists to become involved in them with the understanding that “more water in the harbor floats all boats.” Data scientists tend to be more motivated, too, when more is expected of them.
The challenges of accessing and structuring big data sometimes leave little time or energy for sophisticated analytics involving prediction or optimization.
People think I’m joking, but who would’ve guessed that computer engineers would’ve been the sexy job of the 1990s?” If “sexy” means having rare qualities that are much in demand, data scientists are already there.
In those days people with backgrounds in physics and math streamed to investment banks and hedge funds, where they could devise entirely new algorithms and data strategies.
One question raised by this is whether some firms would be wise to wait until that second generation of data scientists emerges, and the candidates are more numerous, less expensive, and easier to vet and assimilate in a business setting.
Why not leave the trouble of hunting down and domesticating exotic talent to the big data start-ups and to firms like GE and Walmart, whose aggressive strategies require them to be at the forefront?
If companies sit out this trend’s early days for lack of talent, they risk falling behind as competitors and channel partners gain nearly unassailable advantages.
- On Monday, August 19, 2019
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