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Guide to Data Science Bootcamps in 2019

In our new, data-driven landscape, it’s not uncommon for people who work in tech or looking to switch to a different industry to reskill on their own and learn how to work more productively. Data Science bootcamps are sometimes intended to fill knowledge gaps, but many programs teach basic fundamentals and build up to advanced topics. As a data scientist, you’ll be using a variety of programming languages, such as Python or R, and visualization tools like Tableau to help organizations make data-driven decisions.

Looking for a quick way to compare programs? Check out our list of data science bootcamps. We cover everything you need to know – from location and education requirements, to job placement figures and curriculum details.

Learn More About Bootcamps

Featured Bootcamps

Sponsored Programs

Learn MoreUC Berkeley

Data Analytics Boot Camp
Turn data into actionable insights. Berkeley Data Analytics Boot Camp is a dynamic, part-time program that covers the in-demand tools and technologies for data analytics and visualization through rigorous, project-based classes.
Sponsored Programs

Learn MoreColumbia University

Data Analytics Boot Camp
Are you ready to become a data-driven professional? Columbia Engineering Data Analytics Boot Camp is a challenging, part-time boot camp that equips learners with the specialized skills for data analytics and visualization through hands-on, in-person classes.
Sponsored Program

Learn MoreCase Western Reserve University

Data Analytics Boot Camp
CWRU Data Analytics Boot Camp is a rigorous, part-time program that prepares students with the fundamental skills for data analytics and visualization. Through hands-on, in-person instruction, you’ll cover a wide range of topics and graduate ready to apply your skills in the workforce.
Sponsored Programs

Sponsored Programs

What are Data Science Bootcamps?

Bootcamps are intense educational programs that pack critical data science skills and technologies into a short period of time. Established bootcamps, like Flatiron School and General Assembly, offer in-person, online, or a hybrid approach that combines online and campus learning, whereas Thinkful provides a completely tailored online learning experience. Many schools will work with your schedule or offer part-time programs. Part-time data science bootcamps may take more time to complete, but in some cases, courses are offered in the evening, which can be very appealing to students with full-time jobs.

Some data science bootcamps (e.g. Metis, NYC Data Science Academy, and Galvanize are aimed at those with experience in object-oriented programming language, statistics, databases, or math. The free Insight Health Data Science: Fellows Program is offered to PhD or MD graduates transitioning to big data jobs at leading healthcare organizations. Other bootcamps offer programs for all skill levels. For example, Microsoft’s DS3 summer school hopes to convert curious college students into data scientists.

Are Data Science Bootcamps Worth it?

Before you commit to a specific program, consider the bootcamp’s instructor and mentor quality, cohort makeup, curriculum, portfolio work, and outcomes rates and support. Data science is skills-based, so you should be looking for instructors who have serious, hands-on experience in your preferred fields. You may want to look for bootcamps that focus on group learning as well and admit a full cohort of students with diverse backgrounds.

It’s also important to decide how much structure you want. Are you comfortable with self-paced learning? Or do you prefer deadlines, assignments, and homework? If you’re new to data science, check out beginner and intermediate levels, which often include lectures and projects in fundamentals (e.g. Python). If you’re looking at advanced fellowships, you’ll have more independence to focus on what you’d like to work on and build your portfolio.

For data science jobs, your portfolio is your real resume – employers want to know what you’ve done, why you did it and how it’s original. For example, Thinkful helps its Python learners develop an active GitHub portfolio; NYC Data Science Academy asks students to spend the last two weeks of camp on a capstone project. With the ASI Data Fellowship, you have the chance to work with industry partners on real-world problems. A strong bootcamp will help you build a diverse portfolio that’s also geared towards what employers look for in their candidates.

Many bootcamps have set themselves up as talent pipelines, funneling trained data scientists to eager partner companies. If you’re hoping to land a job after you graduate, look for programs that provide plenty of career training. For example, Metis sets up mock interviews, company site visits and consulting projects. General Assembly provides an in-house career coach and instruction in salary negotiations. You may also want to look for a bootcamp that provides alumni with post-graduation support, which sometimes includes alumni networking or emails with relevant jobs for recent graduates.

Most bootcamps advertise job placement rates (some boast rates of 100%), but these percentages are only part of the story. You may only receive job offers from the bootcamp’s partner companies, not end up in a “true” data scientist role, or wait three months before receiving an offer. It’s also possible the starting salary is a lower median salary than you expected. Take a careful look at what job placement entails and make sure the career they’re preparing you for is the career you want.

Choosing the Right Bootcamp

1. Establish Your Career Goals

Where do you want to be in five years? Do want to get a data science job straight out of bootcamp or do you just want a skill set that will allow you to work on independent projects? Knowing your goals will help you narrow your options. If you’re lost in a dead-end data role and not sure what your next step is, you might want to explore the uses of data science in different industries and check out our profiles of data science careers.

2. Contact Data Scientists in Your Chosen Field

LinkedIn, Twitter, and meet-ups are a great place to start! Ask them what they do during a normal day. Get recommendations on skill sets (e.g. R vs. Python). Talk to them about your education options. You may not need a bootcamp to land a job. A few months with Coursera or other MOOCs and a good textbook could do the trick.

3. Research Requirements in Job Listings

But don’t take them too much to heart. Sometimes, HR departments dump a huge list of requirements into the skills section and hope for the best. When in doubt, ask your mentors what knowledge is critical to have.

4. Ruthlessly Assess Your Skill Level

This will help when it comes to deciding whether you need a part-time course in Python (e.g. General Assembly), a crash course in Hadoop, or a full three-month immersion in major data science technologies (Metis, NYC Data Science Academy, Galvanize, etc.). Think about soft skills too. Do you need practice developing your own projects and public speaking? Do you need some experience leading a team?

5. Contact Bootcamp Graduates for an Honest Opinion

Many bootcamp organizations list their alumni on the website, but you can also do a LinkedIn and Twitter search. Ask for an inside take on coursework, instructors, job preparation and career support after graduation.

6. Create a Budget

That $16,000 fee is just the beginning; you’ll also need to think about food, transport, lost wages and accommodation. In a place like San Francisco or New York City, housing can be very expensive. Some bootcamps offer merit, need-based scholarships, and scholarships for women; be sure to ask about options.

7. Draw Up a Shortlist

You can start with our Mega List of Data Science Bootcamps, but you may also want to do your own research. What kind of reputations do the instructors have? Can you commit to full-time? Some bootcamps are highly selective – what are your realistic chances of getting in?

Graduate Degrees vs. Bootcamps

Let’s say you have a bachelor’s degree in a quantitative science (or a related field) and you’re thinking of becoming a data scientist. You’ve done a few online courses (e.g. Coursera, Udemy, etc.) and are ready to invest in more education. You’re considering three options – data science bootcamp, data science masters, or PhD in data science. Which one do you choose?

We don’t have the definitive answer. Unlike, say, medicine, there is no tried and true path to a career in data science. Some gurus hold a PhD in statistics and have built up an arsenal of data tools; others only have a B.S. and an incredible portfolio of projects. Hard-hitting entrepreneurs have created start-ups after minimal time in an academic setting.

Bootcamp Masters PhD
Typical Time to Complete:
A few hours to three months
Typical Time to Complete:
One and a half to two years
Typical Time to Complete:
Four to seven years
Target Audience:
Aimed at folks who want a data science job after graduation
Target Audience:
Aimed at students interested in exploring the field
Target Audience:
Aimed at true lovers of research and data challenges
Instruction:
Instructors often have industry experience designing real data science solutions
Instruction:
Professors may be a mix of academics with theoretical chops and industry professionals
Instruction:
Thesis supervisors are typically serious academics interested in complex problems
Curriculum:
Coursework is usually focused on applied skills & practical projects
Curriculum:
Coursework typically includes theory as well as applied skills
Curriculum:
Coursework is heavily focused on theory and personal research
Interaction:
Team-based projects and experiential learning
Interaction:
A mix of team-based learning and individual research
Interaction:
Opportunity for teamwork depends on the thesis
Tip:
Focus on mastering key skill sets; gaps in knowledge could be disastrous down the track
Tip:
Amass as much practical experience and job training as possible; the market is typically competitive
Tip:
Be sure your thesis incorporates data analysis, programming, and practical experience

Interview with DDL Graduate

Mehdi El-Amine, DDL GraduateMehdi El-Amine

Bootcamp: District Data Labs – Data Science Incubator
Title: Data Analytics Manager
Company: www.circleback.com

“You can take all the online courses in the world – you can buy books, read blogs, do code exercises – but eventually you need to get your hands dirty with a real project.”

Why did you choose to apply to District Data Labs? What influenced your decision?

Location was a factor – the DC Metro area is not as rich in data science education offerings as, let’s say, the Bay Area or NYC. Also, the incubator was free of charge; you just had to submit an application and get selected. The format (mostly group self-directed) was a great fit for my schedule as a full-time professional, and it offered me the chance to do hands-on data science work outside of the paradigm of “class-based education.”

What kind of data science skills and experience did you have before you started?

I have a Bachelor’s in Computer Engineering. At the beginning of my career, I was a full stack .NET guy, with good knowledge of SQL databases. I’d done some amateur web development with the LAMP stack, too. Around the late 2000s, I did some cool stuff with OLAP databases (or, at least, it was cool in my head). So I did have a technical foundation, albeit a dated one.

However, in the few years leading up to the DDL Incubator program, I was far removed from all kinds of technical work. Eventually, I quit my job and some old professional connections helped me land a new gig as a Data Analyst. I had just been introduced to Python and I started using it at work, along with a lot of SQL and some exploratory data analysis work (the single-machine-csv-type, not the multi-node-cluster-hadoop type). From there, I put my hands into every data-related project I could find, at work or outside of it.

What goals did you have for the bootcamp?

Most of all, I wanted to do some hands-on data science in the “wild.” You can take all the online courses in the world – you can buy books, read blogs, do code exercises – but eventually you need to get your hands dirty with a real project or your learning will be incomplete.

Deep down, I knew this. While I was taking online courses, I would think to myself: “this thing I’m working on is very directed; if someone were to come to me and hand me the problem statement with no additional instructions, I wouldn’t know where to start, where to get data, what modeling technique to use, etc.”

What was the application process like? Were any parts difficult?

You would go to Tony’s office (Tony Ojeda is the DDL founder), he’d give you one ingredient, and you had to make a meal out of it in under 20 minutes. Then you serve the meal in three plates, and you juggle the three plates while riding a bicycle.

Okay, maybe that’s not entirely accurate. You don’t have to ride a bicycle. The application process is actually a form with some questions – some more elaborate than others – but I don’t recall anything being especially difficult.

What was the coursework like?

The incubator program did not involve much coursework, if any. There were one or two tutorial-like sessions at the very beginning on the expectations of the program – how to report on progress, the schedule of deliverables, and dealing with the github repo. And there was instruction on how to build a data product.

But the act of skill-building was mostly your own responsibility; you could reach out to staff for guidance or help whenever you needed it. I ended up learning a ton of new skills, a lot of things that were intangible – yet crucial – to managing and executing a data project.

Did you work on a portfolio or capstone project? What was it?

The program was project-based. A few sponsoring organizations came in Week 1 and presented projects that we could work on. We were split in teams and each team would choose one of the projects, or come up with their own.

If you did work on a sponsored project, you were able to collaborate – to some degree – with folks at the organization itself (obtaining data, understanding the knowledge domain, getting direction on which questions to try to answer, etc.).

My team and I chose to work on a project by the Bureau of Labor Statistics. They proposed some work on identifying geographical regions with significant demand for (or supply of) particular job skills. But I decided that we should do something bigger.

Mind you, I had no data science skills, but that did not deter my ego; I somehow convinced my team that we could figure out how to forecast national employment growth, using BLS historical labor data. The idea was to take some economic variables such as the employment-population ratio, or labor participation rate, throw them into a multi-variate regression and forecast the monthly unemployment rate.

How would you judge the quality of the instructors and/or mentors?

Excellent. Benjamin Bengfort was the master data scientist with all the answers to both low-level questions (what library to use? how to scale this or that, etc.) and high-level ones (Is linear regression the right approach? are these findings useful?).

Laura Lorenz helped us engineer better code, structure it, and design a proper data product. She also made sure we stayed on track every two weeks with our deliverables, which was definitely needed!

What kind of career training and job preparation did the bootcamp provide?

Even though the program wasn’t structured as a training program, it still provided a huge amount of learning that was directly applicable to the professional work environment. Executing a data project and putting together a data product is a different beast from the standard exercise of “get some data, get an ML library, get predictions.”

In no particular order, you need to:

  • Figure out the right questions to ask
  • Break the work down into tasks
  • Schedule and manage the tasks between yourself and your teammates
  • Automate data retrieval and wrangling
  • Test and validate your model(s)
  • Deploy your model(s) into a production-type environment
  • Build a web front (app, website, api) for others to consume data from your model(s)

We worked on all of these pieces over the course of the project.

How many people were in your cohort? Was there a lot of diversity?

Off the top of my head, the number was somewhere between 30 and 35 people, split roughly 60% male and 40% female. The folks in my cohort had different, but complementary, professional skills; so many of the projects presented at the end of the incubator were damn impressive! My own team was composed of an African American, an Indian, and a Middle Eastern Arab.

Were you guaranteed a job after graduation?

No, but I heard one or two people were planning to change jobs after the bootcamp. I wouldn’t be surprised if they did.

What are you up to now? Do you feel the bootcamp helped get you to this stage?

I am still with CircleBack, working on new and cool data projects. The bootcamp helped me almost immediately; I now make use of several tools I picked up (IPython notebook, pandas, matplotlib). I evaluate the performance and accuracy of data models, which I did not know how to do before the bootcamp. And I also interact with a lot of data APIs (our own or third-party vendors), which is something I got better at after working on the BLS project.

Would you recommend DDL? Who would be the best candidates for it?

Absolutely. The incubator itself is invaluable, but plenty of DDL’s standard workshop-type course offerings are also highly educational, too. If you were to apply to the incubator program, I would say you need:

  1. A motivation to learn
  2. One technical skill, which could be anything (SQL, or R, or statistics)

Do you have any other advice for folks interested in a data science bootcamp?

Don’t rely on being fed the information; you have to want to figure it out yourself. It doesn’t matter what the tool is – Python, R, or other – the most important skills you’ll learn are centered around data science “thinking.” Is this a good training set? Am I overfitting? How would I interpret the predictions of this model?

Finally, choose to work on something small and narrow-focused; otherwise, you won’t be able to get all the data you need, or figure out which features are relevant, or have useful results. The big questions can only be answered after the small questions.

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Data Science Bootcamp Directory

Below is a list of data science bootcamps in three categories: Beginner, Intermediate, and Advanced. To narrow down the list, simply use the box below:

DataCamp

 

Location

Online

Mode

Part-time

Length

4 hours

Cost

Introductory courses: Free. Full Access: $25/month or $250 annually.
 

Summary

DataCamp provides short, self-paced courses in R, Python, machine learning, data visualization, and more.

Educational Requirements

No requirements are listed, although intermediate & advanced courses required a certain level of skill. Most users are working professionals.

Job Placement

No promises on job placement.

Curriculum

You'll have your pick of courses, especially ones focused on skills in R and data visualization. DataCamp has developed partnerships with a range of companies (Microsoft, IBM, Kaggle, Pluralsight. and RStudio) and sources its instructors from some big universities (Princeton, Duke, and University of Washington).

DS3 (Microsoft Research Data Science Summer School)

 

Location

New York City, NY

Mode

Full-time

Length

8 weeks

Cost

Free
 

Summary

DS3 is a hard-core summer school for college students in the New York City area. The cohort size is small

Educational Requirements

Upper level undergraduate students (including graduating seniors) who wish to break into data science and/or who are keen on graduate work in computer science or a related field.

Job Placement

No promises on job placement.

Curriculum

Your instructors will be research scientists from Microsoft Research and your coursework will cover the fundamentals of data science, including machine learning, statistics, Python, and R. During the summer, you'll be expected to select a real-world, data-driven project and work on it with 3 fellow students.

Springboard

 

Location

Online

Mode

Part-time

Length

Approximately 100 hours or 3 months

Cost

$1,497 for 3 months. The price is reduced if you complete the coursework in less time.
 

Summary

Formerly known as SlideRule, Springboard offers mentor-led workshops in data science. Workshops are self-paced and online.

Educational Requirements

Depends on the workshop. The initial Foundations of Data Science workshop is targeted at business & marketing analysts, developers, and grad students who want to make the move from academia.

Job Placement

Job placement is not assured, but each workshop also contains a unit on career prep, including training for interviews.

Curriculum

Curricula vary, but you'll be able to work on real-world projects and build a portfolio for employers. Need to talk to an expert? After enrollment, you'll be matched with a mentor and participate in weekly 1-on-1 video chats. Mentors are typically data scientists in major companies.

Data Society

 

Location

Online

Mode

Part-time

Length

Varies

Cost

$49/month for access to all courses, data science resources, and forums.
 

Summary

Data Society provides a series of quick & clean courses for people who want to dip their toes into data science.

Educational Requirements

Applicants don't need to have any particular experience.

Job Placement

No promises on job placement.

Curriculum

The curriculum and instructors will vary depending on the class. Many courses incorporate open-source R software. Popular classes include Introduction to R & Visualization and Clustering & Finding Patterns.

Case Western Reserve University Data Analytics Boot Camp

 

Location

Cleveland, OH

Mode

Part-time

Length

24 Weeks

Cost

$10,995
 

Summary

CWRU Data Analytics Boot Camp is a rigorous, part-time program that prepares students with the fundamental skills for data analytics and visualization. Through hands-on, in-person instruction, you'll cover a wide range of topics and graduate ready to apply your skills in the workforce.

Educational Requirements

No data analytics experience required. It is recommended that applicants hold a Bachelor's Degree or have at least two years of experience in business, management, finance, statistics, or a related field.

Job Placement

While the Ohio data analytics boot camp does not secure direct employment, students are equipped for the job search through comprehensive career services including technical interview training, resume and portfolio reviews, virtual tech panels, and 1:1 coaching to be competitive in the job market.

Curriculum

The data bootcamp curriculum gives students real-world experience through hands-on projects and strategic lessons taught by industry professionals. From statistics, databases, and Python, to front-end web visualization, Tableau, and Machine Learning, students learn how to extract, analyze, and visualize data to solve problems and define solutions. Each project contributes to an impressive professional portfolio for students to showcase their knowledge to employers.

Columbia Engineering Data Analytics Boot Camp

 

Location

New York, NY

Mode

Part-time

Length

24 Weeks

Cost

$12,995
 

Summary

Are you ready to become a data-driven professional? Columbia Engineering Data Analytics Boot Camp is a challenging, part-time boot camp that equips learners with the specialized skills for data analytics and visualization through hands-on, in-person classes.

Educational Requirements

No data analytics experience required. It is recommended that applicants hold a Bachelor's Degree or have at least two years of experience in business, management, finance, statistics, or a related field.

Job Placement

While the New York data analytics boot camp does not secure direct employment, learners are equipped for the job search through comprehensive career services including technical interview training, resume and portfolio reviews, virtual tech panels, and 1:1 coaching to be competitive in the job market.

Curriculum

The data boot camp curriculum gives students real-world experience through hands-on projects and strategic lessons taught by industry professionals. From statistics, databases, and Python, to front-end web visualization, Tableau, and Machine Learning, individuals learn how to extract, analyze, and visualize data to solve problems and define solutions. Each project contributes to an impressive professional portfolio for students to showcase their knowledge to employers.

UC Berkeley Data Analytics Boot Camp

 

Location

Berkeley, San Francisco & Belmont, CA

Mode

Part-time

Length

24 Weeks

Cost

$11,995
 

Summary

Turn data into actionable insights. Berkeley Data Analytics Boot Camp is a dynamic, part-time program that covers the in-demand tools and technologies for data analytics and visualization through rigorous, project-based classes.

Educational Requirements

No data analytics experience required. It is recommended that applicants hold a Bachelor's Degree or have at least two years of experience in business, management, finance, statistics, or a related field.

Job Placement

While the San Francisco boot camp does not secure direct employment, students are equipped for the job search through comprehensive career services including technical interview training, resume and portfolio reviews, virtual tech panels, and 1:1 coaching to be competitive in the job market.

Curriculum

The data curriculum gives students real-world experience through hands-on projects and strategic lessons taught by industry professionals. From statistics, databases, and Python, to front-end web visualization, Tableau, and Machine Learning, students learn how to extract, analyze, and visualize data to solve problems and define solutions. Each project contributes to an impressive professional portfolio for students to showcase their knowledge to employers.

Data Science Dojo: Bootcamp

 

Location

Wide variety of locations

Mode

Full-time

Length

5 days

Cost

$3,000. Student fellowships, no-interest payment plans, and group discounts are available.
 

Summary

Short but intense, Data Science Dojo tackles fundamental skills in data science and engineering.

Educational Requirements

The only prerequisite is knowledge of at least one programming and/or scripting language. Typical applicants include software engineers, data/business analysts, database administrators, researchers, and the like.

Job Placement

No promises on job placement.

Curriculum

The curriculum begins with introductions to the basics (R, data mining, algorithms, etc.) before digging into areas such as Hadoop, Hive, and NoSQL. At the end of the bootcamp, you'll participate in a Hack Day Project. Here you'll work with other students on a real-world data science program. Each group is paired with an experienced mentor.

Thinkful: Flexible Data Bootcamp

 

Location

Online | Washington D.C. | Atlanta, GA | Los Angeles, CA | Raleigh NC

Mode

Flexible (20-25 hours per week)

Length

6 months (recommended)

Cost

$8,550
 

Summary

Thinkful is a self-paced coding bootcamp that provides 1-on-1 mentorship from an experienced data scientist 3x per week.

Educational Requirements

Applicants should have some experience with Python or scripting. Completely new to programming? Thinkful also has a Prep Course for Data Science Bootcamp to help you get started.

Job Placement

While job placement is not currently guaranteed, Thinkful provides extensive career support in the form of mock interviews, technical challenges, modeling and visualization exercises, and resume/cover letter review to helps its graduates get hired.

Curriculum

Coursework is built around projects that echo real-world problems. Although the course is self-paced, you'll meet with an experienced data scientist every week, 3x per week, to get feedback and discuss problems. The curriculum can also be personalized to your specific goals. For instance, want to spend more time with experimentation or dive deeper into a specific kind of modeling? You can work with your mentor to tailor each phase of the course.

Bit Bootcamp

 

Location

New York City, NY

Mode

Full-time

Length

4 weeks/6 weeks

Cost

$1,500. If you are hired by a partner company, Bit Bootcamp will refund your fee.
 

Summary

The brainchild of Wall Street data scientists, Bit Bootcamp is designed to supply the essentials. It has 2 tracks: Hadoop & Big Data (4 weeks) and Data Science & Machine Learning (6 weeks). If you're just getting out of college or grad school and need a crash course in coding and big data skills, this might be one to consider.

Educational Requirements

Applicants for the Hadoop Track should have solid math skills and experience in SQL and an object oriented programming like Java, C#, C++ etc. Applicants for the Data Science Track should have experience in SQL, Python, or an object oriented programming language.

Job Placement

Bit Bootcamp offers job placement assistance with select partner companies. Alumni have found jobs in companies such as Google, Comcast, American Express, GE Capital, etc.

Curriculum

You'll begin by taking classes in fundamentals (essential training in SQL, Python, machine learning, etc.) before you dig into big data tools (MapReduce, Hadoop, Hive, Pig, clustering techniques, etc.). During this time, you have the option to work on your own project, apply for data certification, or contribute to a learning project. There is one class in interview and resume preparation.

Data Science for Social Good: Summer Fellowship

 

Location

Chicago, IL

Mode

Full-time

Length

14 weeks

Cost

Free (fellowship-based). Depending on your experience and level of education, stipends range from $11,000 to $16,000. This includes a housing stipend (~$1000/month).
 

Summary

Data Science for Social Good is a summer program aimed at aspiring graduate and undergraduate students who want to work on data mining, machine learning, big data, and data science projects with social impact. View a list of fellows & instructors.

Educational Requirements

Applicants are typically graduate students (and some advanced undergrads and post-docs) from the fields of computer science, statistics, math, physical sciences, social sciences, and public policy. You should have some quantitative skills, proficiency in at least one programming language (e.g. Python), experience with statistics and data analysis.

Job Placement

Data Science for Social Good doesn't address job placement. This makes sense, since the program is focused on altruism.

Curriculum

Fellows are grouped into teams of 3-4 and partner with non-profits and government agencies to tackle a relevant data science project. These projects are intended to address a high impact problem (e.g. predictive analytics to prevent lead poisoning in children).Project work is supplemented with lectures, workshops, seminars, happy hours, meet-ups, and field trips. Teams are also in constant contact with technical mentors and project managers.

Data Science Retreat (DSR)

 

Location

Berlin, Germany

Mode

Full-time

Length

12 weeks

Cost

AC;10,000. The website notes that the cost of living in Berlin is significantly lower than 3 months in San Francisco or New York City.
 

Summary

DSR has set itself up as a direct competitor to Metis and others. Like other 3-month bootcamps, it focuses on machine learning, data science, and big data engineering.

Educational Requirements

Applicants should hold a basic knowledge of machine learning and have spent at least 1000 hours programming, even in a language that is not 'data science friendly'.DSR accepts ~10 people out of the 200 who apply for a cohort.

Job Placement

According to DSR, 100% of participants got multiple interviews and 86% got the job they wanted.

Curriculum

Your coursework will include the fundamentals: Python, advanced machine learning, R, Spark, etc. But you'll also learn about areas such as Deep Learning for image classification, Numpy, Scipy, Pandas, Scikit-learnm and more. Classes are taught by senior data scientists and engineers and mentored by Chief Data Scientists and CTOs. As part of your career prep&comma you must also complete an independent portfolio project.

District Data Labs

 

Location

Online & Arlington, VA

Mode

Part-time & full-time

Length

Online courses: 2 hours. Hands-on workshops: 8 hours. Incubator: 3 months.

Cost

Varies
 

Summary

District Data Labs offers quick online courses, workshops, and a structured 3-month project development program to folks who want hands-on learning experiences in specific data science topics.

Educational Requirements

No education requirements are listed, but most courses and workshops are targeted at mid-tier professionals who want to dig deeper into topics.

Job Placement

No promises on job placement.

Curriculum

Examples of workshop titles include "Fast Data Apps with Spark & Python," "Relational Databases for Analytics," and Natural Language Processing with Python & NLTK." Online courses are typically focused on Python and Django. District Data Labs promises that more courses are coming.

General Assembly: Data Science Immersive

 

Location

Boston, MA

Mode

Full-time

Length

12 weeks

Cost

Price not quoted.
 

Summary

A crash course aimed at data professionals interested in a data science career. Can't handle full-time? Check out the part-time Data Science Course focusing on Python. This is aimed at those with a basic understanding of statistics.

Educational Requirements

Data professionals/analysts, advanced degree holders, researchers, software engineers, and aspiring data scientists.

Job Placement

Job placement is not guaranteed, but General Assembly organizes a variety of events with local partners/companies.

Curriculum

The curriculum targets fundamentals, including Git, SQL, UNIX, Python, machine learning, visualization methods, and data modeling techniques. Speaker lectures, meetups, and community-wide hackathons are included. To connect you to a career, General Assembly provides training for technical interviews & salary negotiations and organizes Meet & Hire events. You'll also be talking with an in-house career coach.

Metis: Data Science Bootcamp

 

Location

New York City, NY/San Francisco, CA/Chicago, IL/Seattle, WA

Mode

Full-time

Length

12 weeks

Cost

$16,000. Since Metis is not a Title IV organization, you cannot use 529 funds to pay for tuition. However, Metis offers a $3,000 scholarship for women, underrepresented minority groups, the LGBTQ community, and veterans or members of the U.S. military.
 

Summary

This intense and well-known bootcamp is targeted at the mid-tier data professionals who want to qualify for entry-level data scientist/analyst jobs.

Educational Requirements

No PhD is required, but applicants should have previous experience in programming (writing code) and/or statistics. Cohorts often have students with a mix of bachelors', masters', PhD, and professional degrees.

Job Placement

Although Metis won't guarantee you a job, the placement rate is quite high. Metis also has the benefit of being backed by Kaplan, a large education company. That means it has strong relationships with a lot of hiring partners (e.g. Enova, BuzzFeed, Etsy, Constant Contact, etc.).

Curriculum

Classes are 100% in-person and taught by senior data scientists. The coursework covers all the fundamentals (Python, SQL and NoSQL databases, big data, machine learning, statistical analysis, Hadoop stack, etc.). Metis integrates career coaching and job placement support into the curriculum. You'll be taking part in mock interviews, speakers & events, career workshops, a career day, company site visits, and consulting projects. You'll also receive support after graduation.

NYC Data Science Academy: Data Science Bootcamp

 

Location

New York City, NY

Mode

Full-time

Length

12 weeks

Cost

$16,000. In general, scholarships are not offered. Limited scholarships have been granted to financial need candidates, but this is a rare event.
 

Summary

Like Metis and Zipfian/Galvanize, this bootcamp is aimed at aspiring data scientists who want training in lots of languages and tools, including R.

Educational Requirements

Applicants are expected to have a Master's or PhD degree in science, technology, engineering or math or equivalent experience in quantitative science or programming. Some bachelor's degree holders are considered.

Job Placement

Jobs aren't guaranteed, but NYC Data Science has relationships with many hiring firms. Graduates have found employment in companies such as American Express, Aetna, Unified, IBM Watson, Goldman Sachs, Bosch, etc.

Curriculum

The curriculum focuses on the major building blocks of data science, including equal coverage of R, Python, Hadoop, and big data technologies. You'll be working on real-world data science challenges and spending two weeks on a final capstone project. Guest speakers and meet-up events are included as par for the course. Towards the end of the bootcamp, you'll participate in a resume review, interview preparation, and introductions to local hiring partners.

Zipfian Academy/ Galvanize gSchool: Data Science Immersive

 

Location

Denver, CO; Seattle, WA; San Francisco, CA; Austin, TX

Mode

Full-time

Length

12 weeks

Cost

$16,000. Galvanize also offers partial scholarships based on merit, demonstrated financial need, and increasing participation in technology among underrepresented groups.
 

Summary

Formerly known as Zipfian Academy, Galvanize's bootcamp is geared towards data professionals who want to become data scientists or analysts, machine learning engineers, software engineers, and the like.

Educational Requirements

No PhD is required, but applicants should be comfy with data analysis and have a solid foundation in quantitative disciplines like statistics, probability, linear algebra, and/or math. Get ready to be tested

Job Placement

Galvanize claims a 94% placement rate within 6 months of completion and an average starting salary of $114,000. Graduates have found work with big players such as Facebook, Twitter, Uber, Square, Airbnb, and more.

Curriculum

You'll be taught by a variety of data scientists with real-world experience. The curriculum is grounded in Python, and - like Metis - covers major tools and concepts (e.g. machine learning, SQL and NoSQL, statistical inference, Hadoop, big data, etc.). To help you land a job, Galvanize offers interview coaching, a resume review, and introductions to partner companies. You'll also be able to present your capstone project at a Hiring Day.

K2: Data Science Bootcamp

 

Location

100% Online

Mode

Part-time

Length

24 weeks

Cost

$6,000
 

Summary

Through an advanced curriculum and project based structure, students learn today's cutting edge analytic technologies. The program is designed for students who prefer not to leave their day jobs and are ready to take on an extra educational challenge during their evenings and weekends.

Educational Requirements

No Masters or PhD requirement. Students will need prior experience with a programming language and familiarity with statistics and probability.

Job Placement

Jobs are not guaranteed, but students will be competitive candidates for entry-level data analyst/scientist/engineer jobs. There is a dedicated career advisor that will coach students and help match them to desired industries and locations.

Curriculum

Classes are taught by currently employed senior data scientists. The coursework covers Python, databases, statistics, probability, machine learning, big data and natural language processing.

Statistics.com - The Institute for Statistics Education

 

Location

Online

Mode

Part-time

Length

40 weeks

Cost

$5,000
 

Summary

Statistics.com provides two certificate programs of 10 4-week online asynchronous courses.

  1. Programming in Data Science - Use R of data mining, Python for text mining, extract data from databases with SQL, learn Hadoop basics, and put it all together in a capstone project.
  2. Analytics in Data Science - Use Excel-based tools for data mining, time series forecasting, clustering, constrained optimization, simulation and social network analysis. Electives cover spatial analysis, natural language processing and more.

Educational Requirements

No requirements are listed, although an undergraduate degree is expected. Intermediate & advanced courses require a certain level of skill. Introductory Statistics is offered as a foundation at no charge. Most users are working professionals.

Job Placement

No promises on job placement.

Curriculum

You'll have your pick of elective courses, and if you can show that you're proficient in a topic area, a recommended replacement can be substituted. Many of the courses are taught by the authors of leading texts and you'll be able to ask questions and exchange comments via a private discussion board throughout the course period.

ASI Data Science Fellowship

 

Location

London, UK

Mode

Full-time

Length

8 weeks

Cost

Free (fellowship-based)
 

Summary

The ASI Data Science Fellowship offers intensive training in data science to PhDs and post-doctorates from around the world. View profiles of previous fellows.

Educational Requirements

PhD candidates and post-doctoral researchers in candidate sciences. Other cases are reviewed individually.

Job Placement

Job placement is not guaranteed, but ASI promises that many companies offer interviews towards the end of the fellowship.

Curriculum

You'll spend 8 weeks on a mentor-led portfolio, building a collection of real-world projects that demonstrate your skills. Because this fellowship is modular-based, you and your industry partner can customize modules according to your needs. Project partners include the BBC, Tesco, Siemens, NHS, Lloyds Bank, and other well-known British companies. At the end of the fellowship, fellows present their work in a Demo Day.

Big Dive

 

Location

Torino/Turin, Italy

Mode

Full-time

Length

5 weeks

Cost

Non-Profit Employee: AC;1,100. Start-Up Employee: AC;1,000. Regular: AC;1,350. Student: AC;725.
 

Summary

Big Dive is a broad-based training program covering development, visualization, and data science.

Educational Requirements

Applicants come from a wide variety of backgrounds, and include hackers, skilled coders, data-driven employees, post-doctoral researchers, and PhD students. Contact Big Dive for more info.

Job Placement

No promises on job placement.You can view the entire curriculum on the Big Dive website.

Curriculum

Coursework is split evenly between development, visualization, and data science. You'll also be working on team-based projects. Instructors are a mix of academics and entrepreneurs.

Data Science Europe

 

Location

Dublin, Ireland & Online

Mode

Full-time on-site. Full-time or part-time online.

Length

6 weeks (12 weeks part-time, online)

Cost

On-site: AC;7,500. Online: AC;5,000.
 

Summary

DSE is aimed at smart grads and academics who want to get into data science. The cohort is small (fewer than 10 people).

Educational Requirements

Applicants must have

Job Placement

DSE had a 100% first batch placement rate. If you don't receive any offer for a quantitative job within 6 months, DSE will refund the course fee.

Curriculum

Over 6 weeks, you'll tackle many major data science technologies, including machine learning, and programming in R and Hive/SQL. Silicon Valley data scientists teach the courses; mentors from European companies provide advice. At the end of the bootcamp, job interviews will be scheduled with your preferred companies.

Insight Data Science: Fellows Program

 

Location

New York City, NY, Silicon Valley, CA, and remote

Mode

Full-time

Length

7 weeks

Cost

Free (fellowship-based). Needs-based living expense scholarships are also available.
 

Summary

Insight Data Science is a comprehensive post-doctoral training fellowship that helps folks bridge the gap between academia and data science. View profiles of previous fellows.

Educational Requirements

PhD candidates or postdoctoral researchers.

Job Placement

According to Insight Data Science: "100% of Fellows who have stuck with the post-Insight job search process have found work in a data-related role within 3-4 months of completing the program." Most Fellows receive offers from mentor companies within 4-6 weeks. Alumni work for big hitters such as Facebook, Airbnb, Uber, LinkedIn, Twitter, Bloomberg, Dow Jones, etc.

Curriculum

Unlike other fellowships, Insight Data Science does not make you go to classes. Instead, all learning is self-directed and project-based. So if you're not an independent worker, you may want to consider more traditional formats. During the program, you'll also participate in roundtable discussions and Q&A sessions, demonstrate your portfolio to mentoring companies, and take part in practice interviews with alumni. At the end of the fellowship, you'll begin the real interview process with major companies.

Insight Health Data Science: Fellows Program

 

Location

Boston, MA

Mode

Full-time

Length

7 weeks

Cost

Free (fellowship-based)
 

Summary

Like its big brother (Insight Data Science), this fellowship is targeted at academics hoping to make the move to data science. Specifically, it prepares scientists to work in big data at leading healthcare organizations. PhD candidates, post-doctoral researchers, or MD graduates.

Educational Requirements

Prior fellows have come from the fields of computational biology, bioinformatics, genomics, medicine, physics, computer science, statistics, and engineering.

Job Placement

Insight claims a job 100% job placement rate, with all fellows finding work in a data-related role within 3-4 months. Alumni work at companies such as Counsyl, Palantir, Invitae, Jawbone, Memorial Sloan Kettering Cancer Center, Biogen, The Broad Institute, and start-ups.

Curriculum

This fellowship follows the same format as Insight Data Science (see above), but it focuses solely on healthcare data. If you don't enjoy self-directed learning, you may want to consider other options.

S2DS London

 

Location

London, UK

Mode

Full-time

Length

5 weeks

Cost

A4;800 registration fee. Free housing in London is included. Scholarships are available for exceptional candidates.
 

Summary

S2DS is geared towards analytical PHDs and scientists who want the commercial tools and techniques needed for a career in data science.

Educational Requirements

Applicants should have a PhD (or be in their last year of a PhD) in an analytical science, intermediate programming skills (R, Python, Java, or similar), and experience in math & statistics.

Job Placement

S2DS states that 70% of its 2014 fellows had a job or job offers 4 months after graduation. Ongoing job opportunities are supplied through Pivigo Recruitment.

Curriculum

Most of S2DS's curriculum involves working in small teams (3-4 fellows) on a real-world project. For each project, teams are paired with a mentor from a partner company. Fellows must also attend video lectures that cover key aspects of data science (e.g. Hadoop, machine learning, data visualization, etc.). Company presentations and panel debates run in parallel to project work. A careers fair is held at the end of the program, visited by some of the sponsoring companies.

S2DS Virtual

 

Location

Online

Mode

Full-time

Length

6 weeks

Cost

A4;800 registration fee. Scholarships are available for exceptional candidates.
 

Summary

S2DS has the same goals as its on-site sibling, SD2S London. However, it also accepts master's candidates.

Educational Requirements

Applicants should have an MSc or higher in an analytical science, intermediate programming skills (R, Python, Java, or similar), and experience in math & statistics. A reliable and fast broadband connection (minimum 10 Mbps) is also required.

Job Placement

Job placement statistics are only quoted for S2DS London. Please contact S2DS for more information on the virtual program.

Curriculum

S2DS Virtual follows the same format as S2DS London with a few differences. Video lectures, which you can watch in your own time, are all completed in Week 1. Weeks 2-6 are focused on the partner project. Fellows also have the opportunity to participate in online networking events and virtual meet-ups with partner companies.

Startup.ML Fellowship

 

Location

San Francisco, CA

Mode

Full-time

Length

4 months

Cost

Free (fellowship-based)
 

Summary

Startup.ML is a top-flight fellowship focused on machine learning. Fellows get the chance to build ML systems/models for products that are already in production.

Educational Requirements

Applicants should have an advanced quantitative degree and a solid background in software engineering and quantitative analysis.

Job Placement

Alumni have found jobs with companies such as Uber, Enlitic, Sentient Technologies, Orange, and more.

Curriculum

Over 4 months, you'll work on an industry-sponsored project

The Data Incubator

 

Location

New York City, NYC, San Francisco, CA, Washington, DC & online

Mode

Full-time

Length

8 weeks

Cost

Free (fellowship-based)
 

Summary

Funded by Cornell, The Data Incubator is a training fellowship aimed at academics that want to start work as data scientists within 12 months of completing the fellowship. Fellows can participate in-person or online.

Educational Requirements

Applicants must be within 1 year of receiving their master's or PhD degree from any math, science, engineering, or social science field. Postdocs and faculty, and people who already have a master's or PhD, are also welcome to apply.

Job Placement

The Data Incubator is serious about landing you a job. It asks that you interview with its hiring partners (or another company you prefer) immediately after graduating. Those partners include eBay, Microsoft, JPMorgan Chase, Pfizer, Yelp, and more.

Curriculum

Coursework covers key skills in data science, including programming tools (Python, Numpy, Scipy, Scikit-learn, and Matplotlib); natural language processing; advanced statistics; data visualization; and SQL, Hadoop, MapReduce, Hive, and Spark. During this time, you'll also be participating in seminars with mentors and building a portfolio with a series of mini-projects. Interview sessions with various hiring companies take place upon graduation.

SPONSORED DATA SCIENCE PROGRAMS

UC Berkeley - Master of Information and Data Science
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SPONSORED ANALYTICS PROGRAMS

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Syracuse University - Master of Science in Business Analytics
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