AI News, Data Science Bootcamps

Data Science Bootcamps

: Contrary to the popular notion that a data scientist is a statistician who lives in San Francisco, I believe a data scientist is someone who uses a combination of programming, statistics and domain knowledge to flexibly apply diverse quantitative methods to generate  value from data.

 From full-time, in-person “bootcamps” for the career switcher to part-time courses in specific data science areas for the skill-builder, our portfolio of data science courses offer project-based training using real data and taught by world-class industry practitioners.

One that is ever-evolving and provides a variety of incredible learning experiences for every customer, from the individual trying to determine “what is data science?” to the industry veteran who wishes to continue to build her skills.

Of course, given the rubric we use to evaluate candidates, and to keep pace with the class and ensure career readiness in 12 weeks, we look for a baseline of aptitude and experience in both programming and statistics or other applied quantitative methodologies.

In terms of technical skills, we need applicants to have worked with code on independent or work-related projects, and the required depth-of-skill varies with the degree of match to the tools used in the bootcamp.

: For the cohort (of 21 students) that graduated three weeks ago, two have accepted job offers, four additional students have received job offers, and the remainder are actively interviewing with multiple companies with the support of our Careers team.

Omitted from the rate are students Metis has been unable to contact​ ​and students who voluntarily waived employment support​, which in total represents ​11% of our students who graduated at least six months ago.

: Every bootcamp culminates with an in-person Career Day, when the students do 3-minute presentations on their passion project to a room full of hiring companies, alumni, future students, and friends of Metis.

Ultimately, however, we believe our curricular approach, which couples an emphasis on the design of data science projects with an introduction to a wide array of technologies, tools, and algorithms that they can apply to their portfolio of 5 different data science projects, is what makes them extremely competitive in the data science market.

The iterative project-based nature of our curriculum ensures that our graduates have not only the technical abilities that they need, but also have already gone through the process of finding and applying the correct tools to the correct question in order to make a valuable contribution with their work.

They do this five times over the course of twelve weeks, each time accepting a greater proportion of the management of their own projects, until the final capstone assignment, where they are basically given free reign to “Do something interesting in four weeks.”

In some cases, they should also look for domain expertise in that industry, though many firms can tell you that this often not necessary as a “pre”-requisite if there is enthusiasm and some kind of perceptible or demonstrated domain aptitude Too often job postings for data scientists throw every language, package, and skill they have heard of into the “requirements” section.

A bit of allowance for post-hire skillbuilding, apprenticeships, or “thick onboarding” blows the doors open very wide for intensely talented applicants to come in and become loyal and custom-trained data workers.

In terms of psychological discouragement, our instructors and speakers are open with students about the gap between the public perception of data scientists as superhuman unicorns who know everything that computer science and machine learning could ask of them off the top of their head, and the reality, which is that practicing data scientists have some deep experience, broad awareness, and they are not immune to bouts of intense impostor syndrome and intimidation by the expanse of things to learn and do in a data science career.

For instance, at  5:00, a data scientist from one of Metis’ hiring partners might come to speak to the class, providing a “lightning talk” on a particular facet of data science, as well as an overview of the company and the types of data science roles they are recruiting for, or we might host a local meetup chapter’s event in the common area with refreshments, open to the public.

Depending on the placement, new data scientists might find themselves in an environment where they’ve developed deep competency on their company’s data stack, and the temptation will be there to rest a bit too long and start to stiffen up.

Our instructors warn the students throughout the course of the bootcamp that they should not be looking forward to the day where they are “done” and have attained a breadth of competence and mastery that will serve them until retirement.

: If they haven’t internalized the tools, they won’t understand what they’re doing in their project work, and they will hit a brick wall in preparing their project presentations and come to the instructors to figure it out.

When everything the students are doing is leading up to a finished piece of work that they will either stand up and present in front of their instructors and peers or put on the internet for employers to see, the practicality of the material becomes obvious to the students, and any mis-applications or inappropriate assumptions become obvious to the instructors.

However, if we teach six clustering methods, and the students use one at a deep and practical level to understand something about the tweets and users involved in a particular hashtag, they have banked specific knowledge about that algorithm, as well as transferrable knowledge about how they’d go about using the other clustering techniques, which may have been only used for them in the challenge exercises.

They can come back to the code and output they’ve done in the homework, to their notes, and create an analogous experience for themselves with material they used in less depth during the bootcamp, or even with future algorithms they come across later in their work.

In terms of psychological discouragement, our instructors and speakers are open with students about the gap between the public perception of data scientists as superhuman unicorns who know everything that computer science and machine learning could ask of them off the top of their head, and the reality, which is that practicing data scientists have some deep experience, broad awareness, and they are not immune to bouts of intense impostor syndrome and intimidation by the expanse of things to learn and do in a data science career.

 Whether it is the “sexiest job in the 21st century,” the constant comparison of data scientists to unicorns, superheroes, master chefs, top athletes, supermodels of the business world, etc., creates an impossible expectation for both the hired and the hirer.

: We are currently focused on the consumer career changer market (through the bootcamp), the consumer skill-builder market (through our professional development evening courses), and the corporate data science training market.

Claudia Perlich, Chief Data Scientist at Dstillery, is deliciously practical in her talks, singing the praises of regression when everyone wants to talk about the day’s hot models like random forest or neural networks, and advising practical caution and common sense when it comes to troublesome data types like geocoding.

Jake Porway, founder and executive director of DataKind, is truly motivating in his unwavering commitment to “doing good with data science.” Ben Wellington, the blogger behind I Quant NY, is constant reminder for me of the power of effective storytelling with data, and the need to open up public data to improve living for everyone.

The best data science is done by people working together, having a really good time working on very hard problems that would be excruciating without others to rub brain cells together and share the load.

Data Science Bootcamps

We are committed to creating a culture of inclusion within the exciting and growing field of data science.

There are 20% fewer LGBT individuals in government STEM-related jobs than should be expected5 and 43% of the STEM workforce is closeted.6 We must reverse these trends and create more avenues for talented individuals from these groups and communities to enter, remain, and thrive in the field of data science.

We recently caught up with Dr. Andy Martens, former professor and researcher in social psychology and physiology who is in the process of moving from academia to data science.

We were keen to learn more about his background, his move to data science, his choice of going to a data science bootcamp, and why he chose Metis Data Science Bootcamp in particular...

I researched things like the escalation of violence, prejudice, stress, and how various patterns in the heart's EKG signal match up with emotion.

(1) Many of the social psychology experiments I like put participants/subjects in emotional situations that examine morality and how it warps more easily than we would like it to (e.g., the Milgram Obedience Studies).

It's a quirky series of experiments about something that's hard to think about and that was hard to figure out how to measure in a reasonably ethical way.

I wrote and recorded a handful of low-fi songs inspired by big and tricky topics in psychology: happiness, death, the scientific method, humor, repression.

That involved doing lots of experiments (mostly on intro psychology students) and then analyzing the data from those experiments.

But I was definitely drawn to the idea that splitting up a data set in different and creative ways might reveal insights that weren't obvious to the typical person.

- I was working overseas in academia and wanted to move back to the U.S. and to find work where I'd get to keep working with data.

I realized that there was a big gap in the tools I'd been using to analyze data and the programming tools that data scientists were using.

- I spoke to an academic friend of mine the other day about Metis, the data science bootcamp I'm taking part in.

When I described to him the machine learning and cross validation approach, he tried to sum up what I was saying with: 'So academics are good at explaining why things work but they can't predict s$%#.

I know that's not true and that's a gross oversimplification, and that good data scientists are trying to both predict and build theories about why things work.

- To smooth and speed the transition from the academic world I needed help figuring out what tools to focus on, help actually learning the tools, and help building a network and making connections to people in the data science world.

- I've been obsessed for the past year with the new-ish technology that allows for extracting the heart signal from a simple video recording of a person's face.

Really enjoyed learning more about your background, your move to data science, your choice of going to a data science bootcamp, and why you chose Metis Data Science Bootcamp in particular.

If you are interested in attending the bootcamp, check them out here for their final March 9 application deadline and apply now here - Metis Data Science Bootcamp.

To keep up with our interview series as well as the latest curated news and articles in Data Science, sign-up for our free weekly newsletter -> Data Science Weekly ...

Data Science Bootcamps

: Contrary to the popular notion that a data scientist is a statistician who lives in San Francisco, I believe a data scientist is someone who uses a combination of programming, statistics and domain knowledge to flexibly apply diverse quantitative methods to generate  value from data.

 From full-time, in-person “bootcamps” for the career switcher to part-time courses in specific data science areas for the skill-builder, our portfolio of data science courses offer project-based training using real data and taught by world-class industry practitioners.

One that is ever-evolving and provides a variety of incredible learning experiences for every customer, from the individual trying to determine “what is data science?” to the industry veteran who wishes to continue to build her skills.

Of course, given the rubric we use to evaluate candidates, and to keep pace with the class and ensure career readiness in 12 weeks, we look for a baseline of aptitude and experience in both programming and statistics or other applied quantitative methodologies.

In terms of technical skills, we need applicants to have worked with code on independent or work-related projects, and the required depth-of-skill varies with the degree of match to the tools used in the bootcamp.

: For the cohort (of 21 students) that graduated three weeks ago, two have accepted job offers, four additional students have received job offers, and the remainder are actively interviewing with multiple companies with the support of our Careers team.

Omitted from the rate are students Metis has been unable to contact​ ​and students who voluntarily waived employment support​, which in total represents ​11% of our students who graduated at least six months ago.

: Every bootcamp culminates with an in-person Career Day, when the students do 3-minute presentations on their passion project to a room full of hiring companies, alumni, future students, and friends of Metis.

Ultimately, however, we believe our curricular approach, which couples an emphasis on the design of data science projects with an introduction to a wide array of technologies, tools, and algorithms that they can apply to their portfolio of 5 different data science projects, is what makes them extremely competitive in the data science market.

The iterative project-based nature of our curriculum ensures that our graduates have not only the technical abilities that they need, but also have already gone through the process of finding and applying the correct tools to the correct question in order to make a valuable contribution with their work.

They do this five times over the course of twelve weeks, each time accepting a greater proportion of the management of their own projects, until the final capstone assignment, where they are basically given free reign to “Do something interesting in four weeks.”

In some cases, they should also look for domain expertise in that industry, though many firms can tell you that this often not necessary as a “pre”-requisite if there is enthusiasm and some kind of perceptible or demonstrated domain aptitude Too often job postings for data scientists throw every language, package, and skill they have heard of into the “requirements” section.

A bit of allowance for post-hire skillbuilding, apprenticeships, or “thick onboarding” blows the doors open very wide for intensely talented applicants to come in and become loyal and custom-trained data workers.

In terms of psychological discouragement, our instructors and speakers are open with students about the gap between the public perception of data scientists as superhuman unicorns who know everything that computer science and machine learning could ask of them off the top of their head, and the reality, which is that practicing data scientists have some deep experience, broad awareness, and they are not immune to bouts of intense impostor syndrome and intimidation by the expanse of things to learn and do in a data science career.

For instance, at  5:00, a data scientist from one of Metis’ hiring partners might come to speak to the class, providing a “lightning talk” on a particular facet of data science, as well as an overview of the company and the types of data science roles they are recruiting for, or we might host a local meetup chapter’s event in the common area with refreshments, open to the public.

Depending on the placement, new data scientists might find themselves in an environment where they’ve developed deep competency on their company’s data stack, and the temptation will be there to rest a bit too long and start to stiffen up.

Our instructors warn the students throughout the course of the bootcamp that they should not be looking forward to the day where they are “done” and have attained a breadth of competence and mastery that will serve them until retirement.

: If they haven’t internalized the tools, they won’t understand what they’re doing in their project work, and they will hit a brick wall in preparing their project presentations and come to the instructors to figure it out.

When everything the students are doing is leading up to a finished piece of work that they will either stand up and present in front of their instructors and peers or put on the internet for employers to see, the practicality of the material becomes obvious to the students, and any mis-applications or inappropriate assumptions become obvious to the instructors.

However, if we teach six clustering methods, and the students use one at a deep and practical level to understand something about the tweets and users involved in a particular hashtag, they have banked specific knowledge about that algorithm, as well as transferrable knowledge about how they’d go about using the other clustering techniques, which may have been only used for them in the challenge exercises.

They can come back to the code and output they’ve done in the homework, to their notes, and create an analogous experience for themselves with material they used in less depth during the bootcamp, or even with future algorithms they come across later in their work.

In terms of psychological discouragement, our instructors and speakers are open with students about the gap between the public perception of data scientists as superhuman unicorns who know everything that computer science and machine learning could ask of them off the top of their head, and the reality, which is that practicing data scientists have some deep experience, broad awareness, and they are not immune to bouts of intense impostor syndrome and intimidation by the expanse of things to learn and do in a data science career.

 Whether it is the “sexiest job in the 21st century,” the constant comparison of data scientists to unicorns, superheroes, master chefs, top athletes, supermodels of the business world, etc., creates an impossible expectation for both the hired and the hirer.

: We are currently focused on the consumer career changer market (through the bootcamp), the consumer skill-builder market (through our professional development evening courses), and the corporate data science training market.

Claudia Perlich, Chief Data Scientist at Dstillery, is deliciously practical in her talks, singing the praises of regression when everyone wants to talk about the day’s hot models like random forest or neural networks, and advising practical caution and common sense when it comes to troublesome data types like geocoding.

Jake Porway, founder and executive director of DataKind, is truly motivating in his unwavering commitment to “doing good with data science.” Ben Wellington, the blogger behind I Quant NY, is constant reminder for me of the power of effective storytelling with data, and the need to open up public data to improve living for everyone.

The best data science is done by people working together, having a really good time working on very hard problems that would be excruciating without others to rub brain cells together and share the load.

Metis Data Science Bootcamp Pre-work

FAQs Review the computer requirements on hardware needed for the bootcamp.

Completing the pre-work is essential to obtaining the foundational knowledge necessary to succeed in the Metis data science bootcamp.

Each student should expect to spend 60+ hours of tutorials to become familiar with software installation, editors, command line, Python (numpy, pandas, etc.), linear algebra and statistics.

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