AI News, BOOK REVIEW: The Open Source Data Science Masters
The Open Source Data Science Masters
...by 2018 the United States will experience a shortage of 190,000 skilled data scientists, and 1.5 million managers and analysts capable of reaping actionable insights from the big data deluge.
The core aptitudes – curiosity, intellectual agility, statistical fluency, research stamina, scientific rigor, skeptical nature – that distinguish the best data scientists are widely distributed throughout the population.
We’re likely to see more uncredentialed, inexperienced individuals try their hands at data science, bootstrapping their skills on the open-source ecosystem and using the diversity of modeling tools available.
While I agree wholeheartedly with Raden’s statement that “the crème-de-la-crème of data scientists will fill roles in academia, technology vendors, Wall Street, research and government,” I think he’s understating the extent to which autodidacts – the self-taught, uncredentialed, data-passionate people – will come to play a significant role in many organizations’ data science initiatives.
Course Data Science with Open Source Tools Book $27 This is an introduction geared toward those with at least a minimum understanding of programming, and (perhaps obviously) an interest in the components of Data Science (like statistics and distributed computing). Out
What is a Data Scientist?
Data scientists are a new breed of analytical data expert who have the technical skills to solve complex problems – and the curiosity to explore what problems need to be solved.
It’s a virtual gold mine that helps boost revenue – as long as there’s someone who digs in and unearths business insights that no one thought to look for before.
It’s key information that requires analysis, creative curiosity and a knack for translating high-tech ideas into new ways to turn a profit.
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 'data science' became a buzzword, and is now often applied to business analytics, business intelligence, predictive modeling, or any arbitrary use of data, or used as a glamorized term for statistics. In many cases, earlier approaches and solutions are now simply rebranded as 'data science' to be more attractive, which can cause the term to become 'dilute[d] beyond usefulness.' While many university programs now offer a data science degree, there exists no consensus on a definition or suitable curriculum contents. To its discredit, however, many data science and big data projects fail to deliver useful results, often as a result of poor management and utilization of resources. The term 'data science' has appeared in various contexts over the past thirty years but did not become an established term until recently.
In 2005, The National Science Board published 'Long-lived Digital Data Collections: Enabling Research and Education in the 21st Century' defining data scientists as 'the information and computer scientists, database and software and programmers, disciplinary experts, curators and expert annotators, librarians, archivists, and others, who are crucial to the successful management of a digital data collection' whose primary activity is to 'conduct creative inquiry and analysis.' Around 2007, Turing award winner Jim Gray envisioned 'data-driven science' as a 'fourth paradigm' of science that uses the computational analysis of large data as primary scientific method and 'to have a world in which all of the science literature is online, all of the science data is online, and they interoperate with each other.' In the 2012 Harvard Business Review article 'Data Scientist: The Sexiest Job of the 21st Century', DJ Patil claims to have coined this term in 2008 with Jeff Hammerbacher to define their jobs at LinkedIn and Facebook, respectively.
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.
Specifically, myself and my team have worked with industry leaders to identify a core set of eight data science competencies you should develop.
Programming SkillsNo matter what type of company or role you’re interviewing for, you’re likely going to be expected to know how to use the tools of the trade.
This will also be the case for machine learning, but one of the more important aspects of your statistics knowledge will be understanding when different techniques are (or aren’t) a valid approach.
Statistics is important at all company types, but especially data-driven companies where stakeholders will depend on your help to make decisions and design / evaluate experiments.
Machine LearningIf you’re at a large company with huge amounts of data, or working at a company where the product itself is especially data-driven (e.g.
Linear AlgebraUnderstanding these concepts is most important at companies where the product is defined by the data, and small improvements in predictive performance or algorithm optimization can lead to huge wins for the company.
This will be most important at small companies where you’re an early data hire, or data-driven companies where the product is not data-related (particularly because the latter has often grown quickly with not much attention to data cleanliness), but this skill is important for everyone to have.
CommunicationVisualizing and communicating data is incredibly important, especially with young companies that are making data-driven decisions for the first time, or companies where data scientists are viewed as people who help others make data-driven decisions.
It is important to not just be familiar with the tools necessary to visualize data, but also the principles behind visually encoding data and communicating information.
At some point during the interview process, you’ll probably be asked about some high level problem—for example, about a test the company may want to run, or a data-driven product it may want to develop.
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).
The Life of a Data Scientist
They take an enormous mass of messy data points (unstructured and structured) and use their formidable skills in math, statistics and programming to clean, massage and organize them.
For example, a person working alone in a mid-size company may spend a good portion of the day in data cleaning and munging.
A high-level employee in a business that offers data-based services may be asked to structure big data projects or create new products.
$163,132 Broadly speaking, you have 3 education options if you’re considering a career as a data scientist: Academic qualifications may be more important than you imagine.
To avoid wasting time on poor quality certifications, ask your mentors for advice, check job listing requirements and consult articles like Tom’s IT Pro “Best Of”
This includes the framing of business and analytics problems, data and methodology, model building, deployment and life cycle management.
Requirements: The EMCDS certification training will enable you to learn how to apply common techniques and tools required for big data analytics.
Related SAS certifications include: Some data scientists get their start working as low-level Data Analysts, extracting structured data from MySQL databases or CRM systems, developing basic visualizations or analyzing A/B test results.
In a 2014 Mashable article, Roy Lowrance, the managing director of New York University’s Center for Data Science program, is quoted as saying “anything that gets hot like this can only cool off.”
But even as demand for data engineers surges, job postings for big data experts are expected to remain high.
data scientists may find themselves responsible for financial planning, ROI assessment, budgets and a host of other duties related to the management of an organization.
- On Monday, August 19, 2019
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