Automation and analytics are transforming companies in every industry—and across the IT landscape—which is why the need for qualified data scientists is increasing. In fact, demand in 2016 was growing at about 12% per year, far outpacing the available supply, according to a report by the McKinsey Global Institute.

The situation hasn't changed much since. Data science skills shortages "are present in almost every large U.S. city," according to an August 2018 LinkedIn report. "Nationally, we have a shortage of 151,717 people with data science skills."

Indeed, by 2024 there could be a shortage of 250,000 data scientists in the United States, according to the McKinsey report.

And that's despite how well paid data science pros are. In 2018, the mean base salaries for data scientists ranged from $95,000 (with one to three years of experience) to $165,000 (with more than nine years of experience) for non-managers, and from $145,000 (one to three reports) to $250,000 (more than 10 reports) for managers.

Why the paucity of data scientists?

One reason for this shortage is that many companies—not just tech suppliers—are looking for people with data science skills, including those who know machine learning (ML), artificial intelligence (AI), and data visualization, as well as people skilled in NoSQL, Hadoop, and predictive analytics.

For example, companies currently embarking on digital transformation projects, such as those in the automotive, retail, advertising, and healthcare industries, are turning to data scientists to help them on their journeys.

Even firms in legacy sectors, such as manufacturing and government, are investigating digital transformation opportunities and at some point will need the assistance of data scientists.

Other reasons to learn data science

Even if you don't want to work as a full-time data scientist, it's still important to get up to speed on the subjects offered in data science bootcamps. IT Ops staffers and developers in particular need to be familiar with concepts such as predictive analytics, AI, and ML.

These capabilities are increasingly being integrated into the application development process, as well as into tools IT Ops and developers use for such things as systems monitoring and service automation.

Predictive analytics can benefit IT teams in several ways, including enabling them to monitor the health or status of an application so they can predict and respond to application outages, saving time and money.

Additionally, interest in ML and AI is increasing as enterprises look to process huge quantities of data to come up with predictive models and insights. Individuals who learn about these capabilities will put their organizations, and their careers, in a position to reap the benefits.

Getting there

But don't fear; you can catch up quickly enough. These days, with the increase in the number of data science bootcamps, gaining practical data science knowledge doesn't require a complicated degree path.

Data science bootcamps are aimed at students with bachelor's degrees who have an aptitude for statistics and math. Students typically don't need PhDs, but knowing a programming language such as Python or R is a plus.

However, some schools, including Science 2 Data Science, require students to have PhDs or master's of science degrees before taking their virtual courses.

Benefits of data science bootcamps

Data science bootcamps are intensive, three-to-six-month programs that prepare graduates for entry-level and junior data science jobs. These programs teach technical skills in data analysis, data visualization, statistical analysis, predictive analytics, and some areas of programming.

They also help students master a variety of languages and frameworks, including Python, Pandas, R, SQL, Hadoop, and Spark, that can help them land intermediate or advanced positions.

Some benefits of data science bootcamps include:

​​A number of bootcamps offer online courses as well as part-time and evening classes that accommodate working students' schedules.

Typically, bootcamps cost less and are shorter than traditional degree programs.

Many bootcamps offer career services, including preparation for job interviews, networking opportunities, and even career coaching after graduation.

Frequently, bootcamps, particularly online bootcamps, offer tutoring and support through one-on-one mentorships.

Bootcamp courses offer more opportunities for hands-on learning than do many higher education programs. Bootcamps also provide students with experience using tools and technology relevant to today's market.

Individuals who graduate from these bootcamps are prepared for jobs such as data scientist, data engineer, and data analyst and can find employment in almost any industry.

Students who want to get into the field can attend one of these, whether in person or online, for a number of weeks or months at a range of prices. They vary in time commitments, work expected, and topics covered.

The data we collected

We picked out 12 of the best-known and recommended data science bootcamps and asked them for specifics about how the programs work so you can make a more informed decision. We asked them to answer the following questions via email:

What courses are offered?

Do you have programs targeted at IT Ops? Security? Developers?

Is the program in person only, online only, or a combination?

Where are your North American locations?

What is the mean starting salary of graduates?

What is the teacher-to-student ratio for any given class?

How much time is available for individual instruction and help?

What percentage of the teachers previously worked commercially as data scientists, including modeling and data analysis? (Just writing code to support data analysis does not count.)

What percentage of instructors are graduates of the program?

What programming languages, tools, frameworks, and libraries do students learn and use in the program?

What is the course syllabus? What are the stages of exercises and/or actual application writing? What applications are students required to build?

What background knowledge is required for the program?

How much time do students work on projects individually and in groups?

How many hours a week does the student need to commit to coursework and class time on average?

How long does the program run?

How long has this organization publicly offered code bootcamps/courses?

How much does the program cost, and when must the fees be paid? Does that include living expenses?

The answers we collected have been combined to make an excellent resource for anyone exploring data science bootcamps. However, before making a decision, you should perform your own due diligence and contact the programs directly.

You may find that if two providers seem equivalent in terms of raw information, one may ultimately be a better fit once you have a chance to talk with a representative. A good bootcamp should set up a call with you if you apply to enroll.

Here is the data for each bootcamp, in alphabetical order.

BrainStation

Courses: Data science full-time program. Also offers part-time courses in data science and machine learning, and training courses in advanced Excel for analysts, and Python for data science.

Data science full-time program. Also offers part-time courses in data science and machine learning, and training courses in advanced Excel for analysts, and Python for data science. Programs: Also offers a web development full-time program and a training course in cybersecurity.

Also offers a web development full-time program and a training course in cybersecurity. In person, online, or a combination? The data science full-time program begins with two weeks of hands-on online learning, which provides students with the foundational data skills they'll need to excel in the program. Students then participate in 10 weeks of on-campus, project-based learning, which emphasizes collaboration and outcome-based skills development.

The data science full-time program begins with two weeks of hands-on online learning, which provides students with the foundational data skills they'll need to excel in the program. Students then participate in 10 weeks of on-campus, project-based learning, which emphasizes collaboration and outcome-based skills development. North American locations: Currently programs in the Canadian cities of Toronto, Ontario, and Vancouver, British Columbia; will soon be offering programs in New York City at the Soho campus.

Currently programs in the Canadian cities of Toronto, Ontario, and Vancouver, British Columbia; will soon be offering programs in New York City at the Soho campus. Mean starting salary: Doesn't collect this information.

Doesn't collect this information. Teacher-to-student ratio: 1:8

1:8 Time for individual instruction/help: Students have access to support from the educator team throughout the day and during dedicated work periods. They also have access to a wide variety of additional training resources and materials available at all hours of the day through Synapse. Also offers a range of career services, including portfolio development, mock interviews with current industry professionals, résumé and job search workshops, and office tours of leading tech companies.

Students have access to support from the educator team throughout the day and during dedicated work periods. They also have access to a wide variety of additional training resources and materials available at all hours of the day through Synapse. Also offers a range of career services, including portfolio development, mock interviews with current industry professionals, résumé and job search workshops, and office tours of leading tech companies. Percentage of teachers with full-time data science experience: All educators have professional experience modeling and analyzing large sets of data.

All educators have professional experience modeling and analyzing large sets of data. Percentage of instructors hired directly out of the program: None of the educators are graduates. Educators must have more than two years of professional experience. Teaching assistants who are recent graduates of the program assist every semester.

None of the educators are graduates. Educators must have more than two years of professional experience. Teaching assistants who are recent graduates of the program assist every semester. Languages, systems, and tools learned: Primarily SQL and Python, along with a number of packages and libraries created for statistical analysis and data science such as NumPy, Pandas, Matplotlib, and Scikit Learn. Focus is on modeling and ML techniques using Python, but students are also exposed to tools such as R and Tableau, along with big data tools such as TensorFlow, Hadoop, and Spark.

Primarily SQL and Python, along with a number of packages and libraries created for statistical analysis and data science such as NumPy, Pandas, Matplotlib, and Scikit Learn. Focus is on modeling and ML techniques using Python, but students are also exposed to tools such as R and Tableau, along with big data tools such as TensorFlow, Hadoop, and Spark. Course syllabus: The six-step iterative model of education prioritizes hands-on, project-based learning. The program can be found online.

The six-step iterative model of education prioritizes hands-on, project-based learning. The program can be found online. Required student background: A bachelor's degree in

one of the STEM disciplines (science, technology, engineering, or

math)

A bachelor's degree in one of the STEM disciplines (science, technology, engineering, or math) Percentage of project time spent individually and in groups: All projects are individual, but all programs are designed to be highly collaborative, to best replicate real-world data work in the field.

All projects are individual, but all programs are designed to be highly collaborative, to best replicate real-world data work in the field. Hours per week: 35-40 on average, but students can expect to spend time outside the class to complete projects.

35-40 on average, but students can expect to spend time outside the class to complete projects. Length of program: 12 weeks

12 weeks How long offering bootcamps/courses: Since 2012

Since 2012 Costs: Information can be found online, on the program package page.

Information can be found online, on the program package page. Specialty areas covered in depth: Research design, ethics, communicating results to non-experts, and alignment between data analysis and key business drivers. The ability to clearly communicate findings to varied audiences (especially nontechnical individuals) is a critical skill when it comes to driving business decisions, and as such, it is a focus of the program.

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Byte Academy

Courses: Full- and part-time data science bootcamps that are part of its Python Fullstack bootcamp program. Predictive analytics, AI, and ML come into play particularly with students' project work. Occasionally offers workshops or workshops series on these topics.

Full- and part-time data science bootcamps that are part of its Python Fullstack bootcamp program. Predictive analytics, AI, and ML come into play particularly with students' project work. Occasionally offers workshops or workshops series on these topics. Programs: Specialized programs, "mini-bootcamps," and workshops are geared toward smaller groups such as at IT Ops, security, and developers.

Specialized programs, "mini-bootcamps," and workshops are geared toward smaller groups such as at IT Ops, security, and developers. In person, online, or a combination? Onsite, remote, and hybrid formats

Onsite, remote, and hybrid formats North American locations: New York City; Houston, Texas

New York City; Houston, Texas Mean starting salary: Approximately $79,000 (all programs)

Approximately $79,000 (all programs) Teacher-to-student ratio: 1:5

1:5 Time for individual instruction/help: Usually at least one instructor is available for student questions at all times. Outside class hours, instructors also available to help on Byte Academy’s Discord channel.

Usually at least one instructor is available for student questions at all times. Outside class hours, instructors also available to help on Byte Academy’s Discord channel. Percentage of teachers with full-time data science experience: All instructors have industry or real-world data science experience.

All instructors have industry or real-world data science experience. Percentage of instructors hired directly out of the program: 25%

25% Languages, systems, and tools learned: Python is the core coding language. Covers frameworks such as Flask and React, and libraries such as Pandas, NumPy, and Matplotlib. Also goes over Jupyter notebooks, some web scraping, SQL, Git protocol, Bash, Linux, and system architecture. Front-end languages, including JavaScript, HTML, and CSS, are covered.

Python is the core coding language. Covers frameworks such as Flask and React, and libraries such as Pandas, NumPy, and Matplotlib. Also goes over Jupyter notebooks, some web scraping, SQL, Git protocol, Bash, Linux, and system architecture. Front-end languages, including JavaScript, HTML, and CSS, are covered. Course syllabus: Syllabus can be found online.

Syllabus can be found online. Required student background: No background knowledge is required. Students required to do about 20 hours of "pre-work" by the first day of class. Also offers a beginner Foundation Program option that students may take to skip the pre-work.

No background knowledge is required. Students required to do about 20 hours of "pre-work" by the first day of class. Also offers a beginner Foundation Program option that students may take to skip the pre-work. Percentage of project time spent individually and in groups: Typically 50/50

Typically 50/50 Hours per week: 40

40 Length of program: 14 weeks for the full-time bootcamp; 24 weeks for the part-time bootcamp

14 weeks for the full-time bootcamp; 24 weeks for the part-time bootcamp How long offering bootcamps/courses: Five years

Five years Costs: $14,950 in New York City, where salaries tend to be higher; $12,950 in Houston, Texas. This pricing is for the 14-week full-time or 24-week part-time program and does not include living expenses. Offers scholarships for women and veterans.

$14,950 in New York City, where salaries tend to be higher; $12,950 in Houston, Texas. This pricing is for the 14-week full-time or 24-week part-time program and does not include living expenses. Offers scholarships for women and veterans. Specialty areas covered in depth: Soft skills workshops on communication and business concepts. Many are elective, but most students tend to take them.

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Data Application Lab

Courses: Data scientist, AI engineer, business analyst

Data scientist, AI engineer, business analyst Programs: Software development bootcamps that include data structure and algorithms

Software development bootcamps that include data structure and algorithms In person, online, or a combination? Combination of online and in classroom

Combination of online and in classroom North American location: Los Angeles, California

Los Angeles, California Mean starting salary: $70,000

$70,000 Teacher-to-student ratio: 1:10

1:10 Time for individual instruction/help: Provides teacher's assistant (TA) office hours, four hours per week and online Q&A during the day.

Provides teacher's assistant (TA) office hours, four hours per week and online Q&A during the day. Percentage of teachers with full-time data science experience: 80%

80% Percentage of instructors hired directly out of the program: Very few; all the instructors are from industry—LinkedIn, Google, Facebook, Uber, etc.

Very few; all the instructors are from industry—LinkedIn, Google, Facebook, Uber, etc. Languages, systems, and tools learned: Python, R, SQL, Hadoop, Spark, TensorFlow, Tableau

Python, R, SQL, Hadoop, Spark, TensorFlow, Tableau Course syllabus: Data science syllabus

Data science syllabus Required student background: Basic probability and statistics

Basic probability and statistics Percentage of project time spent individually and in groups: All the projects are individual, but students can ask help from TAs.

All the projects are individual, but students can ask help from TAs. Hours per week: 20

20 Length of program: Data science bootcamp is 16 weeks.

How long offering bootcamps/courses: Over four years

Over four years Costs: Data science bootcamp is $6,000, not including living expenses.

Data science bootcamp is $6,000, not including living expenses. Specialty areas covered in depth: Ethics, communicating results to non-experts, modeling and testing

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DataCamp

Courses: Data science and analytics

Data science and analytics In person, online, or a combination? Online only

Online only North American location: Online

Online Mean starting salary: Not provided

Not provided Teacher-to-student ratio: Self-paced, online courses

Self-paced, online courses Time for individual instruction/help: Additional help is available on the DataCamp Community forums.

Additional help is available on the DataCamp Community forums. Percentage of teachers with full-time data science experience: 100%

100% Percentage of instructors hired directly out of the program: Not provided

Not provided Languages, systems, and tools learned: Python, R, SQL, Git, Shell, and spreadsheets

Python, R, SQL, Git, Shell, and spreadsheets Course syllabus: More than 200 available courses, plus skill tracks, career tracks, practice exercises, and more than 50 real-world projects

More than 200 available courses, plus skill tracks, career tracks, practice exercises, and more than 50 real-world projects Required student background: None

None Percentage of project time spent individually and in groups: All work is done individually.

All work is done individually. Hours per week: Courses take about four hours to complete.

Courses take about four hours to complete. Length of program: Continuous resource

Continuous resource How long offering bootcamps/courses: Five years

Five years Costs: Start any course for free; full access is $29 per month or $300 per year.

Start any course for free; full access is $29 per month or $300 per year. Specialty areas covered in depth: ML, probability and statistics, programming, and real-world applications

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Data Science Dojo

Courses: Data science and data engineering, data science for product managers, data science for managers and business leaders, and big data, artificial intelligence, and decision science in health

Data science and data engineering, data science for product managers, data science for managers and business leaders, and big data, artificial intelligence, and decision science in health In person, online, or a combination? Five-day, hands-on Data Science Bootcamp includes 50 hours of in-person training, with a combination of 10 hours of pre-bootcamp and 20 hours of post-bootcamp coursework to be completed online. Launching a completely online platform this year. Time in the bootcamp is split pretty evenly between theory and hands-on labs.

Five-day, hands-on Data Science Bootcamp includes 50 hours of in-person training, with a combination of 10 hours of pre-bootcamp and 20 hours of post-bootcamp coursework to be completed online. Launching a completely online platform this year. Time in the bootcamp is split pretty evenly between theory and hands-on labs. North American locations: Austin, Texas; Chicago, Illinois; Las Vegas, Nevada; New York City; Seattle, Washington; Silicon Valley, California; and Washington, DC

Austin, Texas; Chicago, Illinois; Las Vegas, Nevada; New York City; Seattle, Washington; Silicon Valley, California; and Washington, DC Mean starting salary: Not provided

Not provided Teacher-to-student ratio: 1:15

1:15 Time for individual instruction/help: Instructors are available before, during, and after the bootcamp. Post-bootcamp office hours are offered for students who register for the Sensei package.

Instructors are available before, during, and after the bootcamp. Post-bootcamp office hours are offered for students who register for the Sensei package. Percentage of teachers with full-time data science experience: 25%

25% Percentage of instructors hired directly out of the program: 25%

25% Languages, systems, and tools learned: They teach using R, but also offer the corresponding Python code. Tools used in the bootcamp include R, Python, Azure, AWS, Hadoop, Spark, and Kaggle.

They teach using R, but also offer the corresponding Python code. Tools used in the bootcamp include R, Python, Azure, AWS, Hadoop, Spark, and Kaggle. Course syllabus: Syllabus can be found online.

Syllabus can be found online. Required student background: No prior knowledge or background of data science or programming required.

No prior knowledge or background of data science or programming required. Percentage of project time spent individually and in groups:

During the bootcamp, all students work collaboratively, but the submissions are graded individually. Besides the in-class model-building and coding exercises, attendees also spend an average of 15 hours on a data science competition. Many students decide to collaborate and participate in an open data science competition after the bootcamp.

During the bootcamp, all students work collaboratively, but the submissions are graded individually. Besides the in-class model-building and coding exercises, attendees also spend an average of 15 hours on a data science competition. Many students decide to collaborate and participate in an open data science competition after the bootcamp. Hours per week: 10+ hours pre-bootcamp work, 50+ hours during bootcamp, 10+ hours (optional) post-bootcamp work

10+ hours pre-bootcamp work, 50+ hours during bootcamp, 10+ hours (optional) post-bootcamp work Length of program: Five days

Five days How long offering bootcamps/courses: Since 2014

Since 2014 Costs: $4,000; fees are either paid at time of registration or attendees of US bootcamps can pay $999 down and pay the remaining balance in 12 monthly installments.

$4,000; fees are either paid at time of registration or attendees of US bootcamps can pay $999 down and pay the remaining balance in 12 monthly installments. Specialty areas covered in depth: Ethics, online experimentation and A/B testing, communicating results to non-experts, data exploration, visualization, storytelling, and alignment between data analysis and key business drivers

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Data Society

Courses: General data science, including AI and ML; executive data science; Excel; SQL; R programming; Python programming; predictive analytics specialization

General data science, including AI and ML; executive data science; Excel; SQL; R programming; Python programming; predictive analytics specialization Programs: All trainings are customizable to a client's use cases. Data Society has developed trainings for financial institutions, healthcare organizations, and government agencies.

All trainings are customizable to a client's use cases. Data Society has developed trainings for financial institutions, healthcare organizations, and government agencies. In person, online, or a combination? Online; can provide in-person and hybrid training for corporate clients.

Online; can provide in-person and hybrid training for corporate clients. North American locations: Based in Washington, DC, but delivers across the country.

Based in Washington, DC, but delivers across the country. Mean starting salary: Not provided

Not provided Teacher-to-student ratio: 1:20

1:20 Time for individual instruction/help: Individualized support available during training and can arrange time for more support outside of training.

Individualized support available during training and can arrange time for more support outside of training. Percentage of teachers with full-time data science experience: 100%

100% Percentage of instructors hired directly out of the program: 0%

0% Languages, systems, and tools learned: Excel, SQL, Python, R programming, AWS, Spark

Excel, SQL, Python, R programming, AWS, Spark Course syllabus: Syllabus can be found online.

Syllabus can be found online. Required student background: No background knowledge is required for introductory-level courses.

No background knowledge is required for introductory-level courses. Percentage of project time spent individually and in groups: Time is usually focused on individual capstone projects and assignments, as

opposed to group work.

Time is usually focused on individual capstone projects and assignments, as opposed to group work. Hours per week: Depends on the programs.

Depends on the programs. Length of program: Short trainings from one to five days; bootcamps from one to three months

Short trainings from one to five days; bootcamps from one to three months How long offering bootcamps/courses: 4.5 years

4.5 years Costs: Usually paid by employers that requisition the data training program.

Usually paid by employers that requisition the data training program. Specialty areas covered in depth: Research, design, ethics, communicating results to non-experts, and alignment between data analysis and key business drivers

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K2 Data Science Bootcamp

Courses: Data science

Data science In person, online, or a combination? Online only

Online only North American location: Online

Online Mean starting salary: $90,000

$90,000 Teacher-to-student ratio: 1:5, but that can mean a teacher or TA.

1:5, but that can mean a teacher or TA. Time for individual instruction/help: Students have access to teachers/TAs for help from 6 to 11 PM on weeknights, and then 12 to 5 PM on weekends (Eastern time).

Students have access to teachers/TAs for help from 6 to 11 PM on weeknights, and then 12 to 5 PM on weekends (Eastern time). Percentage of teachers with full-time data science experience: 100%

100% Percentage of instructors hired directly out of the program: 0%

0% Languages, systems, and tools learned: Python, NumPy, Pandas, Matplotlib, Seaborn, web scraping, APIs, SQL, NoSQL, JavaScript, D3.js, Hadoop, Spark

Python, NumPy, Pandas, Matplotlib, Seaborn, web scraping, APIs, SQL, NoSQL, JavaScript, D3.js, Hadoop, Spark Course syllabus: Syllabus available online.

Syllabus available online. Required student background: One to three years as a data/business/financial analyst, engineer (software or other), full-stack developer, or other highly technical profession, as well as STEM master's or PhD graduates.

One to three years as a data/business/financial analyst, engineer (software or other), full-stack developer, or other highly technical profession, as well as STEM master's or PhD graduates. Percentage of project time spent individually and in groups: Can be done individually or working in groups.

Can be done individually or working in groups. Hours per week: 20 hours a week

20 hours a week Length of program: Six months

Six months How long offering bootcamps/courses: Since 2016

Since 2016 Cost: $6,000

$6,000 Specialty areas covered in depth: Research design, communicating results to non-experts, and alignment between data analysis and key business drivers

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Metis

Courses: Full-time immersive data science bootcamps; part-time professional development data science courses, including beginner Python and math for data science, introduction to data science, and Metis admissions prep (free); and corporate training.

In person, online, or a combination? Bootcamp is in-person with 25 hours of online pre-work. Professional development courses are online, spanning three to six weeks, depending on the course. Corporate training primarily in-person, although can be delivered online.

North American locations: New York City; Chicago, Illinois; Seattle, Washington; and San Francisco, California

Mean starting salary: Does not report mean starting salaries because of the tremendous variance in backgrounds and interests of the data science students. Salaries have ranged from $25/hour full-time apprenticeships that include on-the-job training to $150,000 senior data science roles.

Teacher-to-student ratio: Between 1:10 and 1:14 for bootcamps. Professional development courses range from 1:10 to 1:25.

Time for individual instruction/help: Bootcamp includes four to five hours of challenge work and project time, in which students can work directly and individually with the senior data scientist instructors and/or the data scientist TAs. For the data science professional development courses, which are three hours/class, typically about one-third to one-half of the class is dedicated to project work in which the instructor helps students individually. The instructor also holds weekly online office hours for students who have questions.

Percentage of teachers with full-time data science experience: 100% of the senior data scientists (two to a bootcamp)

Percentage of instructors hired directly out of the program: 6%; one Metis graduate has been an instructor for over a year, and some graduates are TAs.

Languages, systems, and tools learned: Python, Jupyter Notebook, Git and GitHub, HTML, CS, JavaScript, BeautifulSoup, Selenium, Flash, NumPy, SciPy, Pandas, Statsmodels, Sci-Kit Learn, Hadoop, Hive, Spark, AWS, PostgreSQL, MongoDB, D3.js, Matplotlib, Seaborn

Course syllabus: Available online are the bootcamp syllabus, Beginner Python & Math for Data Science syllabus, and Introduction to Data Science syllabus.

are the Required student background: Beginner Python & Math for Data Science has no required prerequisites. Introduction to Data Science requires s ome familiarity with basic statistical and linear algebraic concepts, such as mean, mean, mode, standard deviation, correlation, and the difference between a vector and a matrix. Additionally, Python is a requirement for the course. Data science bootcamp requires experience with statistics and programming.

Percentage of project time spent individually and in groups: Group exploratory data analysis project, classification, and interactive dashboards, and individual-directed projects. For the Metis data science professional development courses (Beginner Python & Math for Data Science, Introduction to Data Science) the project work is all individual.

Hours per week: 40-60 for bootcamp; 6-10 for professional development courses

Length of program: 12 weeks for bootcamp; part-time development courses range from three to six weeks.

How long offering bootcamps/courses: Started in February 2014

Costs: Bootcamp is $17,000, including $1,500 deposit, remainder in three parts. Professional development courses are $750, due at time of enrollment.

Specialty areas covered in depth: Research design, communicating results to non-experts, and alignment between data analysis and key business drivers

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Northeastern University’s Level Analytics

Courses: Introductory data analytics, intermediate data analytics, workshops

Introductory data analytics, intermediate data analytics, workshops In person, online, or a combination? Full-time, in-person in Boston and Charlotte (intermediate only), part-time evenings, in-person in Boston and San Francisco, and part-time online available to students anywhere.

Full-time, in-person in Boston and Charlotte (intermediate only), part-time evenings, in-person in Boston and San Francisco, and part-time online available to students anywhere. North American locations: Boston, Massachusetts; Charlotte, North Carolina; San Jose, California; San Francisco, California; and Seattle, Washington

Boston, Massachusetts; Charlotte, North Carolina; San Jose, California; San Francisco, California; and Seattle, Washington Mean starting salary: Not provided

Not provided Teacher-to-student ratio: Average of 1:10

Average of 1:10 Time for individual instruction/help: Remote and onsite TAs are available for office hours outside of class.

Remote and onsite TAs are available for office hours outside of class. Percentage of teachers with full-time data science experience: 100%

100% Percentage of instructors hired directly out of the program: Some graduates become TAs.

Some graduates become TAs. Languages, systems, and tools learned: Excel, R, SQL, Tableau, and ML

Excel, R, SQL, Tableau, and ML Course syllabus: Available upon request.

Available upon request. Required student background: There is no background required for Introductory Data Analytics. For Intermediate Data Analytics, general familiarity with statistics and basic Excel concepts.

There is no background required for Introductory Data Analytics. For Intermediate Data Analytics, general familiarity with statistics and basic Excel concepts. Percentage of project time spent individually and in groups: The course components that students are graded on—their capstone projects and their final summative evaluations—are typically completed independently so that employers have the best possible sense of a student's individual skills and strengths. However, students are encouraged to collaborate and support one another's learning within the classroom during lectures and labs.

The course components that students are graded on—their capstone projects and their final summative evaluations—are typically completed independently so that employers have the best possible sense of a student's individual skills and strengths. However, students are encouraged to collaborate and support one another's learning within the classroom during lectures and labs. Hours per week: In the full-time program, students spend 40 hours a week in the classroom. Students in the part-time programs typically dedicate 10-15 hours per week to their work. Part-time programs are held one evening per week for three hours, and additional course material is assigned online for homework.

In the full-time program, students spend 40 hours a week in the classroom. Students in the part-time programs typically dedicate 10-15 hours per week to their work. Part-time programs are held one evening per week for three hours, and additional course material is assigned online for homework. Length of program: Full-time program is eight weeks. Part-time is 13-20 weeks.

Full-time program is eight weeks. Part-time is 13-20 weeks. How long offering bootcamps/courses: Northeastern University has been offering the Level Analytics program since fall 2015.

Northeastern University has been offering the Level Analytics program since fall 2015. Costs: $7,995 for Intermediate Data Analytics; $4,495 for Introductory Data Analytics

$7,995 for Intermediate Data Analytics; $4,495 for Introductory Data Analytics Specialty areas covered in depth: Data visualization and communicating results to non-experts

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NYC Data Science Academy

Courses: Data science bootcamp

Data science bootcamp In person, online, or a combination? In person, live streaming, and recorded videos

In person, live streaming, and recorded videos North American location: New York City

New York City Mean starting salary: $90,000 to $150,000

$90,000 to $150,000 Teacher-to-student ratio: Student to instructor or TA ratio of 1:6

Student to instructor or TA ratio of 1:6 Time for individual instruction/help: TAs available 2-6 PM Eastern; instructors available 9:30 AM to 5:30 PM Eastern.

TAs available 2-6 PM Eastern; instructors available 9:30 AM to 5:30 PM Eastern. Percentage of teachers with full-time data science experience: 100%

100% Percentage of instructors hired directly out of the program: All instructors must have completed the bootcamp program or provide evidence of equivalent skill training.

All instructors must have completed the bootcamp program or provide evidence of equivalent skill training. Languages, systems, and tools learned: R, Python, Linux, Github, SQL, Hadoop, Spark

R, Python, Linux, Github, SQL, Hadoop, Spark Course syllabus: Available online.

Available online. Required student background: None, but individual pre-work programs are designed to bring student skills up to speed in advance of the program.

None, but individual pre-work programs are designed to bring student skills up to speed in advance of the program. Percentage of project time spent individually and in groups: 50% individual, 50% group

50% individual, 50% group Hours per week: 80-100

80-100 Length of program: 12 weeks

12 weeks How long offering bootcamps/courses: Part-time class since November 2013; bootcamp program since January 2015

Part-time class since November 2013; bootcamp program since January 2015 Costs: Bootcamp is $17,600, including $5,000 deposit; remainder due on the first day of class.

Bootcamp is $17,600, including $5,000 deposit; remainder due on the first day of class. Specialty areas covered in depth: Big data, deep learning, research design, data analysis, and key business drivers

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RMOTR

Courses: Data Analysis and Visualization using Python, and Introduction to Lachine learning (all part of the data science program)

Data Analysis and Visualization using Python, and Introduction to Lachine learning (all part of the data science program) Programs: Python automation and web development with Python

Python automation and web development with Python In person, online, or a combination? A combination of physical and online

A combination of physical and online North American locations: Texas, New York, Hawaii, and Canada

Texas, New York, Hawaii, and Canada Mean starting salary: $70,000

$70,000 Teacher-to-student ratio: 1:20

1:20 Time for individual instruction/help: There's live mentor support during the entire day.

There's live mentor support during the entire day. Percentage of teachers with full-time data science experience: 100% of teachers worked professionally in the industry, coming from different disciplines.

100% of teachers worked professionally in the industry, coming from different disciplines. Percentage of instructors hired directly out of the program: There's just one instructor who is a graduate. The rest are professional developers in their areas (worked for more than a year).

There's just one instructor who is a graduate. The rest are professional developers in their areas (worked for more than a year). Languages, systems, and tools learned: Python with all the data science stack: Pandas, NumPy, Satsmodels, Matplotlib, Seaborn, Pygal, Bokeh, etc. For automation and webdev: Flask, Requests, Django, etc.

Python with all the data science stack: Pandas, NumPy, Satsmodels, Matplotlib, Seaborn, Pygal, Bokeh, etc. For automation and webdev: Flask, Requests, Django, etc. Course syllabus: Syllabus available online.

Syllabus available online. Required student background: No background in programming. It is important to understand the fundamentals of "science" (a little bit of math, statistics, algebra, etc.).

No background in programming. It is important to understand the fundamentals of "science" (a little bit of math, statistics, algebra, etc.). Percentage of project time spent individually and in groups: Around 60% of the time in bootcamp, students are working on projects, which can be done individually or in groups. Working in groups is optional.

Around 60% of the time in bootcamp, students are working on projects, which can be done individually or in groups. Working in groups is optional. Hours per week: 20; students have to complete projects each week, from managing data structures to pulling data from APIs and scraping it.

20; students have to complete projects each week, from managing data structures to pulling data from APIs and scraping it. Length of program: Four months

Four months How long offering bootcamps/courses: 14 months

14 months Costs: $1,099 for the four months; $349 if paid monthly

$1,099 for the four months; $349 if paid monthly Specialty areas covered in depth: Data analysis for business applications, reporting and customer engagement mostly

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Science to Data Science (S2DS)

Courses: Data science workshop "trains analytical PhDs and scientists in the commercial tools and techniques needed to be hired into data science roles."

Data science workshop "trains analytical PhDs and scientists in the commercial tools and techniques needed to be hired into data science roles." In person, online, or a combination? A combination. Runs two virtual courses twice a year in March and October. Runs a residential bootcamp in London, England, at a university campus.

A combination. Runs two virtual courses twice a year in March and October. Runs a residential bootcamp in London, England, at a university campus. North American locations: None

None Mean starting salary: £35,000-£45,000 (about $39,670-$51,000)

£35,000-£45,000 (about $39,670-$51,000) Teacher-to-student ratio: Each student team is made up of three to four members. Each team has access to an internal mentor to foster communication and teamwork. There is a technical mentor to remove any technical challenges, and a business mentor to assist the team in understanding the business queries.

Each student team is made up of three to four members. Each team has access to an internal mentor to foster communication and teamwork. There is a technical mentor to remove any technical challenges, and a business mentor to assist the team in understanding the business queries. Time for individual instruction/help: Teams meet with their internal mentors once a week. They meet with technical mentors two to three times a week. Business mentors provide a minimum of half a day per week contact time.

Teams meet with their internal mentors once a week. They meet with technical mentors two to three times a week. Business mentors provide a minimum of half a day per week contact time. Percentage of teachers with full-time data science experience: Technical mentors are always practicing data scientists. Business mentors are company representatives, and internal mentors are data scientists or operations for Pivigo (the company that runs S2DS).

Technical mentors are always practicing data scientists. Business mentors are company representatives, and internal mentors are data scientists or operations for Pivigo (the company that runs S2DS). Percentage of instructors hired directly out of the program: This varies from project to project; currently there are six.

This varies from project to project; currently there are six. Languages, systems, and tools learned: Does not teach coding—requires applicants to have an intermediate level of coding ability in Python, R, or similar. This is the equivalent of 1000 hours to 10,000 hours of experience.

Does not teach coding—requires applicants to have an intermediate level of coding ability in Python, R, or similar. This is the equivalent of 1000 hours to 10,000 hours of experience. Course syllabus: Doesn't offer a curriculum as such. During the program, students work on commercial data science problems with companies, which include startups, SMEs, charities, and large multinational companies. There will be a huge range of different projects, and students' preferences will be taken into consideration.

Doesn't offer a curriculum as such. During the program, students work on commercial data science problems with companies, which include startups, SMEs, charities, and large multinational companies. There will be a huge range of different projects, and students' preferences will be taken into consideration. Required student background: An analytical PhD (London program) or MSc/PhD for the virtual course.

An analytical PhD (London program) or MSc/PhD for the virtual course. Percentage of project time spent individually and in groups: Students work exclusively in groups for five weeks, Monday to Friday, 9 AM to 5 PM.

Students work exclusively in groups for five weeks, Monday to Friday, 9 AM to 5 PM. Hours per week: 40

40 Length of program: Five weeks

Five weeks How long offering bootcamps/courses: Since 2014

Since 2014 Costs: £800 (about $907) for each of the virtual and London programs. The London program offers free accommodations on site.

£800 (about $907) for each of the virtual and London programs. The London program offers free accommodations on site. Specialty areas covered in depth: Ethics, communicating results to non-experts, and alignment between data analysis and key business drivers

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