- On Monday, June 4, 2018
- By Read More
I discussed how I transitioned from primarily using python for data science to primarily using Go for data science.
There seemed to be a huge interest in using Go for data science, and it was awesome to see so many people asking great questions after the talk.
Answer: There is always opportunity to add/improve functionality in these respects, but, as I mentioned above, I truly believe that there is no ecosystem-based blocker for data scientists transitioning to Go.
That being said, we should definitely put some effort into visualization, either by expanding things like gonum/plot or providing tutorials on powering things like D3js or dashing via Go.
The same rules of training, testing, and validation still apply, but as gophers we should also keep in mind things like clarity, simplicity, graceful handling of errors, etc.
There is tooling for regression (e.g., here, here and here), classification (e.g., here, here and here), dimensionality reduction (e.g., here), and much more, and, even if you don't find what you need there, you can enable any ML via connectors to H2O, Tensorflow, Apache Beam, or a number of other frameworks.
By developing you data science applications/services in Go you can have amazing confidence that your application will behave as expected and will be able to be deployed and maintained.
I think the main advantages that Go provides a data scientist relate to the production of usable, clear code that has integrity.
All common tasks in data science are handled quite well with Go, but a newcomer to the community might have trouble finding data analysis/science related resources.
As I mentioned above, PLEASE open issues here explaining what types of data related tutorials or information you would like to see curated.
The two sides of Getting a Job as a Data Scientist
Not an easy question but here’s my short answer to that: What data science is not: The Ways a Data Scientist Can Add Value to Business: This is an extract of an amazing article by Avantika Monnappa 1.
I recommend that you read these articles on the subject, From those, an important quote I can take is: Remember this words: A bad data scientist is way worse than don’t have a data scientist at all.
Before asking for a PhD, ask for knowledge, projects they have worked on, open source projects they built or collaborate, Kaggle kernels they created, related job experience, how did they solve an specific problem.
Data science is not just an IT area, is IT+Business, you need to be sure that the data scientist you hire can adapt to the company, understand the business, have meetings with stakeholders and present their findings in a creative and simple way.
Here’s why so many data scientists are leaving their jobs
Many junior data scientists I know (this includes myself) wanted to get into data science because it was all about solving complex problems with cool new machine learning algorithms that make huge impact on a business.
The data scientist likely came in to write smart machine learning algorithms to drive insight but can’t do this because their first job is to sort out the data infrastructure and/or create analytic reports.
In reality, if the company’s core business is not machine learning (my previous employer is a media publishing company), it’s likely that the data science that you do is only going to provide small incremental gains.
The first few sentences from that article pretty much sum up what I want to say: If you seriously think that knowing lots of machine learning algorithms will make you the most valuable data scientist then go back to my first point above: expectation does not match reality.
That may mean that you have to constantly do ad hoc work such as getting numbers from a database to give to the right people at the right time, doing simple projects just so that the right people have the right perception of you.
It reeks of a job spec from a company that has no idea what their data strategy is and they’ll hire anyone because they think that hiring any data person will fix all of their data problems).
Now if a data scientist spends their time only learning how to write and execute machine learning algorithms, then they can only be a small (albeit necessary) part of a team that leads to the success of a project that produces a valuable product.
On the other hand, if the goal is to optimize provide intelligent suggestions in a bespoke website building product then this will involve many different skills which shouldn’t be expected for the vast majority of data scientists (only the true data science unicorn can solve this one).
So if the project is taken on by an isolated data science team it is most likely to fail (or take a very long time because organizing isolated teams to work on collaborative project in large enterprises is not easy).
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 Saturday, December 7, 2019
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