AI News, Data Science Bootcamps : On Transparent Outcomes

Data Science Bootcamps : On Transparent Outcomes

Outcomes reports have not been widely embraced by the Data Science Bootcamp community compared to their programming bootcamp brethren.

A few of the bootcamps doing it right tend to spend a good amount of time on some aspects of the bootcamp experience you may not be able to easily replicate while self- studying.

We know for a fact that some Data Science Bootcamps remove individuals who aren’t able to find jobs after a certain amount of time from their rolls and don’t include them in their placement statistics.

On the surface, there is actually nothing wrong with doing this and you can make a good argument for this but when Data Science Bootcamps release placement numbers without indicating how they came up with the numbers, you can see how the numbers could be made to look much better than they really are.

We feel some of the Data Science Bootcamps operating today have shoehorned themselves and essentially set the narrative that the only positive outcome from a bootcamp experience should be a Data Science job.

We believe the greatest challenge Data Science Bootcamps face going forward is how to ensure they continue to deliver value in very quantifiable and measurable ways (placements or positive outcomes) and also somehow figure out a way to stay relevant.

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Metis is a data science educator that accelerates the careers of data scientists by providing full-time immersive bootcamps, evening professional development courses, online training, and corporate programs.

This is largely driven by the exponential growth of available data (2.5 quintillion bytes of data are created daily and 90% of the world’s data was created in the past two years) and the narrow set of specific skills required to extract value from that data.

As a result, the number of data-related job postings has surged and median salaries have risen as well, leading to “data scientist” becoming the best job in America in 2016 and 2017 according to Glassdoor.

A job posting for a Data Scientist might describe a role identical to others calling for “data analyst,” though there is usually more diverse coding skills needed for a data scientist job.

They help identify opportunities for companies to use data, while also finding, collecting, and integrating relevant data sources, performing analyses of varying degrees of complexity, writing code and creating tools that teams and businesses can use over time, and telling the story of what they’ve done to company stakeholders.

On each of the five projects, we teach students how to identify the problem, extract and clean data, analyze and interpret data, and communicate the results, both visually and orally in a presentation.

You’ll have one-on-one meetings with your career advisor, exposure to industry experts via our in-class speaker series, workshops focused on resume writing, LinkedIn, and salary negotiations, mock technical interviews with professional data scientists, and more.

However, we do guarantee to: While our hiring network is largest within our home bootcamp cities of Chicago, New York City, Seattle, and San Francisco (and therefore many local companies attend our Career Day events), we're also dedicated to helping those who wish to connect with companies based outside of these areas.

Women, members of underrepresented demographic groups*, members of the LGBTQ community, and/or veterans or members of the U.S. military are eligible to receive a $3,000 scholarship toward their Metis Data Science Bootcamp tuition.

Try scoring yourself using this brief self-assessment: Statistics total = _____ Programming total = _____ Personality total = _____ If you scored a six or greater in each of the above categories, you may be the kind of person we’re looking for.

Of course, the bootcamp itself will be much more challenging, involved, and technical, but this assessment highlights the combination of skills, interests, and personality we think are necessary for a seriously considered application.

Our bootcamp focuses on applications, so the computer science material covered within the bootcamp will be narrowly focused on topics in data structures, algorithms, input/output, and Python language that are pertinent to the data science workflow.

Students build a strong foundation in the Python language, the unix/linux command line, machine learning packages such as scikit.learn, other statistics modules including scipy and pandas, web scraping packages like BeautifulSoup and Selenium WebDriver, PostgreSQL and mongodb databases, collaborative coding under version control with git, working on remote cloud servers such as rackspace, custom visualization tools, especially matplotlib and D3.js with support in HTML, CSS, JavaScript, and web hosting.

Each student should expect to spend approximately 60 hours on tutorials as they become familiar with Python, take a Command Line Crash course, go through a number of package installation tutorials (i.e., Numpy, Scipy, pandas, Scikit.learn), and do some preliminary linear algebra and statistics work.

The pre-work is intended to provide students with the essential background and foundational knowledge they’ll need in order to start the bootcamp and hit the ground running.

You soon make your own choices when tackling data science problems, and with each project, you get concrete, shareable results like blog posts, graphs, and/or reports, and you will conclude with a story of what the problem was, how you approached it and solved it, and what the results look like.

Unlike some other online course options out there, which might consist of pre-recorded lectures, our courses allow for interaction with the instructor, teaching assistants, and other students, and because these are on a set schedule, you’ll be held accountable to actually attend, do the work, and learn the material (which is what you’re really here for anyway!).

The Flatiron district in Manhattan is bustling – filled with many eateries, bars, comedy clubs, theaters, parks, and central to a number of subway lines that provide access to the nearly endless things to do in New York City (including tons of data and tech Meetups and events!).

Pioneer Square is a vibrant part of Seattle with lots of different places to eat, things to do, and bars to check out – additionally, we’re located within walking distance of Seattle’s professional sports venues, Quest and Safeco Fields.

Preparing for a Master’s in Data Science Through Intensive Bootcamps

Strap on your jump boots and get ready to get smoked: an intensive bootcamp is the fastest way to get your brain up to speed and ready for a career or further education in the field of data science.

Candidates aren’t rousted out of bed at dawn for a two-mile run and calisthenics at a data science bootcamp, but the use of the military nomenclature is deliberate: a lot of information is being drilled into a lot of people in a short period of time, and the process can be hectic and overwhelming.

But they really took off in 2014, when Kaplan, an established test preparation and educational materials firm, acquired Dev Bootcamp, a two-year old programming bootcamp startup.

Inevitably, bootcamps started to open up to cater to the demands in these specialty fields, and as of 2015, there were at least 14 bootcamps dedicated to data science, with more opening all the time.

Bootcamps can be a great way to get hands-on experience leading to immediate employment in the field—indeed, a selling point for many bootcamps is their integral job placement element.

In general, because data science is a hybrid field, bootcamp applicants will need to have some grounding in either math or computer science, or both, to be accepted into a program.

A particular class or group going through the bootcamp is often called a “cohort.” Most cohorts are expected to work together closely, in small groups, to complete practical projects throughout the term.

Most bootcamps have some sort of job placement assistance designed to help candidates find a position in the industry after graduation.

This can range from holding a hiring day at the end of the course, to offering a full-time career counselor, to engaging corporate partners who expect to hire much of the graduating class.

MOOCs evolved primarily from college courses, offering the same instructor, materials, and class outline over the Internet as regular matriculating students experienced in person.

Over time, MOOCs have taken on a less collegiate cast, but the principle remains: a MOOC is designed to teach a relatively narrow subject as a theoretical framework rather than as a practical application.

MOOCs are a good solution for candidates who may be generally familiar with the principals and precepts behind data science programs, but who would benefit from a narrow, focused course of instruction in one or a few areas where their knowledge is lacking.

Bootcamps are a good fit for candidates who need a little external pressure to help them complete assignments, or who benefit from peer support and intensive one-on-one time with course instructors.

A bootcamp tends to deliver up-to-date practical knowledge related to the tools and techniques actively in use in the industry, where a MOOC may offer more abstract knowledge.

A good Google search, using the topics you are most interested in, the locations you are willing to attend at, along with the search term “data science bootcamp” may be your best bet.

And, of course, a number of schools now offer online bootcamps that can be taken anywhere, or hybrid programs that combine online learning with a relatively short in-person stint.

From the Kaplan connection, the school has obtained accreditation to offer continuing education credits, so is popular among established professionals looking to expand their career options.

However, the university has also partnered with a number of private corporations, including Humana and Starbucks, and its capstone projects are genuine efforts that those companies will put into regular use– an added feature for the resume of graduates.

In addition to its broad reach, the school is unusual in that it offers a combination of full-time, on-site intensive programs with part-time and online courses.

Due to the nature of the hands-on, project-oriented pedagogy, it’s true that students won’t likely get a chance to apply everything they’re taught in real-world scenarios.

Data science bootcamps, unlike coding bootcamps, are focused on advanced uses of coding skills, not the basics of the code itself, so instructors aren’t going to spend a lot of time helping you with your Python syntax.

CIRR: Job Placement Data for Bootcamps

Coding bootcamps are achieving what few other educational models have seen: a high return on investment (64% salary lift, anyone?).

We want to give you the deets and - at a higher level - stress the importance of helping prospective students understand the potential return on their investment of time, money and effort in a bootcamp.

CIRR schools must report their outcomes every six months,and their data must be backed up by documentation (meaning schools must collect written confirmation from students and employers or offer letters) and verified by a third-party.

Add in the truth in advertising component, and students of CIRR members schools are able to see data in all forms that can be tried, tested and trusted,” explains Dr. Joseph Kozusko, co-founder of Skills Fund, a student financing platform that facilitated the CIRR before it became an independent non-profit.

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