Complete guide to the top 16 data science bootcamps

For years companies have been revving up their big data efforts. Then, one day, people realized that someone had to analyze that mass of information. Thus was born the scramble for experts in data science.

For at least five years, analyst firms and other experts have warned of a coming shortage of data science practitioners: McKinsey Global Institute said in 2011 there would be a shortage of between 140,000 and 190,000 people with "deep analytic skills" and another 1.5 million managers and analysts. A year later, a Harvard Business Review article called data science positions the "sexiest job of the 21st Century."

Alternatively, there are some in the industry who say there's a lot of hype at work, and perhaps there is, but available salary figures suggest that, hype or not, the market is hot.

  • According to O'Reilly Media's 2015 Data Science Salary Survey, the median annual base salary of respondents was $91,000. In the US, the number was $104,000.
  • The middle 50 percent of respondents to the survey made between $77,000 and $135,000 a year.
  • Full-fledged data scientists—a title that often means a Ph.D. in such areas as statistics, particle physicals, biostatistics, or computer science—see starting salaries of $200,000.

When the salaries hit that level, you don't have to worry about whether someone has granted your occupation an official rating of "cool." The top people in the field have high-level degrees and they are experts at teasing out relationships and pairing causes with effects. But data science has a range of interpretations and levels of responsibility.

That means it's possible to enter the field without a Ph.D., which is good.

Embarking on a multi-year course of study before you know whether a line of work is of interest would be a bit extreme. Furthermore, the Accenture Institute for High Performance says that the overall structure of data scientist jobs will change just because they are relatively hard to find. Companies will split the one job into pieces, with developers, data analysts, managers, and others taking parts.

Gaining practical data science knowledge doesn't require a complicated degree path because, as has happened with programming, there has been a spike in the growth of boot camps for data science.

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 variety of prices. They vary in time commitments, work expected, and topics covered. All cover topics in statistics as well as some areas of programming.

We've picked out 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.

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Before you commit to a bootcamp

One consideration you may have is the collection of tools you will learn. According to the O'Reilly survey, the most popular tools are SQL, Excel, R, and Python. In addition, the use of Spark and Scala have greatly increased and correlate with higher earnings.

Cloud computing is important because, in so many cases, that's where the data sits. Cloud-based platforms also often have resident data analysis tools. Some other popular tools are MySQL; such Python libraries as numpy, scipy, scikit-learn, and matplotlib; ggplot; Tableau; Java; PostgreSQL; Oracle; D3; and Hive.

What you won't get at a data science bootcamp

The data science expert Nicole Forsgren, Director of Organizational Performance and Analytics at Chef,  told TechBeacon that there are four important areas that a boot camp might not cover because of time. One is research design, including such topics as experimental design and A/B testing. Another is the ethics of data access and testing, in which there are rarely clear answers. Only one of the programs addressed ethics in depth, and ethical issues of privacy can also become regulatory requirements.

No data analysis makes sense if you don't understand the basics of the business, including key value drivers, strategic goals, and the connection between them and data analysis. And then there is learning how to communicate the results to managers and executives, balancing between so much data that you lose people and so little that they don't understand the most subtle issues.

All of this is in addition to the basics of data science, including statistics, appropriateness of method and interpretation, algorithms, and coding of course. You need all the additional material and might well have to find other ways of learning it.

The data we collected

We contacted dozens of data science boot camps, of which 12 responded with answers to the following questions:

  • What courses are offered?
  • Is the program in person only, online only, or a combination?
  • Where are your U.S. locations?
  • What is the median 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 percentage of those instructors alone have worked for at least one year as a data scientist?
  • 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? 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 boot camps/courses?
  • How much does the program cost, and when must the fees be paid? Does that include living expenses?
  • Which of the following areas are covered in depth: research design, ethics, communicating results to non-experts, and alignment between data analysis and key business drivers?

The answers collected from these emails have been combined to make an excellent resource for anyone exploring data science bootcamps. However, 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:

Data Application Lab

  • Courses: Part-time programs for data related jobs, including data engineer, data scientist, and business analyst. Other courses covering topics like data science in marketing and SQL data analysis are also available.
  • In person, online, or a combination? Either in-person or via an online meeting.
  • US locations: Los Angeles, California.
  • Median starting salary: $112,000.
  • Teacher-to-student ratio: 1:5.
  • Time for individual instruction/help: No direct answer: "We have small classes for data scientist training, between 10 to 20 students."
  • Percentage of teachers with full-time data science experience: 100%.
  • Percentage of instructors hired directly out of the program: 0%; 20% of TAs and mentors are course graduates.
  • Languages, systems, and tools learned: Python, Java, SQL, Hadoop HDFS, Storm, Kafka, Spark, Pig, Hive, Lambda framework (data engineer course), Python matplotlib, Python pandas, Python scikit-learn, Spark MLLib, and Spark GraphX
  • Course syllabus: Data Science syllabus; Data Engineer syllabus; and Business Analyst syllabus
  • Required student background: For Data Engineer, intermediate Java programming skill. For Data Science, Python and basic machine learning.
  • Percentage of project time spent individually? In groups?: All work is individual.
  • Hours per week: 15
  • Length of program: Data Engineer and Data Scientist programs are 20 weeks; Business Analyst program is 8 weeks
  • How long offering bootcamps/courses?: Since 2015
  • Costs: Data Engineer and Data Scientist are $5,000; Business Analyst is $1,500
  • 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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DataCamp

  • Courses: Data science.
  • In person, online, or a combination? Online only.
  • US locations: Online.
  • Median starting salary: Not provided.
  • Teacher-to-student ratio: Self-paced from online materials and video.
  • Time for individual instruction/help: Help available on online forums.
  • Percentage of teachers with full-time data science experience: 100%.
  • Percentage of instructors hired directly out of the program: 0%.
  • Languages, systems, and tools learned: R, Python, dplyr, numpy, matplotlib, ggplot2, bokeh, will launch SQL courses by end of 2016.
  • Course syllabus: No specific pattern of courses.
  • Required student background: None.
  • Percentage of project time spent individually? In groups?: All work done individually.
  • Hours per week: Each course takes 4 to 6 hours.
  • Length of program: No specific length of time as it's a platform for courses.
  • How long offering bootcamps/courses?: 2.5 years.
  • Costs: Start any course for free; full access is $25 per month or $250 per year.
  • Specialty areas covered in depth: Research design, ethics, communicating results to non-experts, and alignment between data analysis and key business drivers all covered within courses on the platform.

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Data Science Dojo

  • Courses: Data Science and Data Engineering.
  • In person, online, or a combination? In person.
  • US locations: Austin, Texas; Chicago, Illinois; New York City, New York; Seattle, Washington; Silicon Valley, California; Washington, D.C.
  • Median starting salary: Not provided.
  • Teacher-to-student ratio: 1:10.
  • Time for individual instruction/help: "Instructors are available before, during, and after the bootcamp."
  • Percentage of teachers with full-time data science experience: Two main instructors only.
  • Percentage of instructors hired directly out of the program: 0%.
  • Languages, systems, and tools learned: R, Azure Machine Learning, data mining framework, hadoop, hive.
  • Course syllabus: Syllabus can be found online.
  • Required student background: Knowledge of at least one programming or scripting.
  • Percentage of project time spent individually? In groups?: 50% on individual projects, 20% on Kaggle predictive modeling platform, 10% on group IoT project.
  • Hours per week: 10+ hours pre-bootcamp work, 50+ hours during bootcamp, 10+ hours post-bootcamp work.
  • Length of program: 5 days.
  • How long offering bootcamps/courses?: Since 2014.
  • Costs: $3,000 including $900 downpayment and 6 to 12 monthly payments, interest free.
  • Specialty areas covered in depth: Not provided.

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Data Society

  • Courses: Data Science for leaders, and data science for analysts.
  • In person, online, or a combination? Online only.
  • US locations: Online; can provide in-person training for corporate clients.
  • Median starting salary: No provided.
  • Teacher-to-student ratio: N/A for online, 1:20 for in person corporate training.
  • Time for individual instruction/help: Access to course forums for help from instructors or other students.
  • Percentage of teachers with full-time data science experience: 100%.
  • Percentage of instructors hired directly out of the program: 0%.
  • Languages, systems, and tools learned: R, Python, hadoop, Tableau.
  • Course syllabus: Syllabus can be found online.
  • Required student background: None for either course.
  • Percentage of project time spent individually? In groups?: All individual.
  • Hours per week: 15 to 20 hours per week to finish in 3 months.
  • Length of program: 3 months.
  • How long offering bootcamps/courses?: Since 2014.
  • Costs: Lifetime subscription to either curriculum is $349. Both are bundled together for $599.
  • Specialty areas covered in depth: Across both of our curricula cover research design, ethics, communicating results to non-experts, and alignment between data analysis and key business drivers.

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Galvanize

  • Courses: Data Science Immersive Program.
  • In person, online, or a combination? In-person.
  • US locations: Denver, Ft. Collins, and Boulder, Colorado; Austin, Texas; Seattle, Washington; San Francisco, California, currently. Phoenix, Arizona being added in late 2016 and New York City expected in early 2017.
  • Median starting salary: $111,000.
  • Teacher-to-student ratio: 6:1.
  • Time for individual instruction/help: Doesn't offer explicit 1:1 coaching. A data science resident available to answer individual questions.
  • Percentage of teachers with full-time data science experience: 55%.
  • Percentage of instructors hired directly out of the program: 35%; no answer on percentage with at least 1 year professional experience.
  • Languages, systems, and tools learned: Python, SQL, NoSQL, machine learning.
  • Course syllabus: Available on website.
  • Required student background: "Looks for backgrounds in a quantitative discipline, including foundational statistics, probability, linear algebra, and mathematics. Galvanize expects a strong working knowledge of at least one programming language (we use Python)."
  • Percentage of project time spent individually? In groups?: 100%.
  • Hours per week: 55 hours per week.
  • Length of program: 3 months.
  • How long offering bootcamps/courses?: 2 years.
  • Costs: $16,000.
  • Specialty areas covered in depth: Research design, communicating results to non-experts, and alignment between data analytics and key business drivers.

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General Assembly

  • Courses: Data Analysis Circuit, Data Science Immersive.
  • In person, online, or a combination?: In person or online only.
  • US locations: Atlanta, Georgia; Austin, Texas; Boston, Massachusetts; Brooklyn, New York; Denver, Colorado; Los Angeles, California; Irvine/Orange County, California; New York City, New York; San Francisco, California; San Jose, California; Seattle, Washington; Washington, D.C.; Crystal City, Virginia.
  • Median starting salary: Not provided for graduating students.
  • Teacher-to-student ratio: 1:5 to 1:7.
  • Time for individual instruction/help: "Individual instruction available during office hours."
  • Percentage of teachers with full-time data science experience: 100%.
  • Percentage of instructors hired directly out of the program: 0%.
  • Languages, systems, and tools learned: Unix commands, Git, SQL, Python, NumPy, Pandas, Tableau, Matplotlib, Jupyter,  Hadoop, MapReduce, Apache Spark.
  • Course syllabus: Available on website.
  • Required student background: "For our full-time Data Science Immersive there is an on-boarding task which can be equated to pre-work for the course. Technically no background is needed but ideal candidates will have had 2-5 years of experience as an analyst. For our part-time and online data offerings, no background required."
  • Percentage of project time spent individually? In groups?: Group projects; time spent on projects "vary."
  • Hours per week: Full-time immersive, 42 per week; part-time or online, 5 hours per week.
  • Length of program: Full-time immersive, 12 weeks; part-time, 10 weeks.
  • How long offering bootcamps/courses?: Since 2011. Offered data courses "for several years."
  • Costs: Full-time immersive, $14,000; part-time, $3,500; online, $1,250.
  • Specialty areas covered in depth: All are "key areas of focus."

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K2 Data Science Bootcamp

  • Courses: Data science.
  • In person, online, or a combination? Online only.
  • US locations: Online.
  • Median starting salary: No data available yet (enrolling first cohort at time of reply.)
  • Teacher-to-student ratio: 1:5, but that can mean a teacher or TA.
  • Time for individual instruction/help: "Students have access to teachers/TA for help from 6pm to 11pm on weeknights and then 12pm to 5pm on weekends eastern time."
  • Percentage of teachers with full-time data science experience: 100%.
  • Percentage of instructors hired directly out of the program: 0%.
  • Languages, systems, and tools learned: Python, Numpy, Pandas, matplotlib, Seaborn, web scraping, APIs, SQL, NoSQL, Javascript, D3.js, Hadoop, Spark.
  • Course syllabus: Syllabus available online.
  • Required student background: 1 to 3 years as a data/business/financial analyst, engineer (software or other), full-stack developer, or other highly technical profession as well as STEM M.S. or Ph.D. graduates.
  • Percentage of project time spent individually? In groups?: Can be done individually or working in groups on projects.
  • Hours per week: 20 hours a week.
  • Length of program: 28 weeks.
  • How long offering bootcamps/courses?: Less than 1 year (enrolling first cohort at time of responses.)
  • Costs: $12,000, including $2,000 deposit.
  • 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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Machine Learning Fellowship

  • Courses: Data Science course which includes engineering, machine learning, and business presentation.
  • In person, online, or a combination? In-person only.
  • US locations: Oakland, California, with plans for South Bay and San Francisco, California locations by the end of 2016.
  • Median starting salary: $115,000.
  • Teacher-to-student ratio: 1:1 or 1:1.5.
  • Time for individual instruction/help: 40 hours per week.
  • Percentage of teachers with full-time data science experience: 100%.
  • Percentage of instructors hired directly out of the program: 15%, but all have professional experience by the time they become mentors.
  • Languages, systems, and tools learned: Python and R using agile methodology.
  • Course syllabus: Varies, as each cohort focuses on an industry-sponsored product.
  • Required student background: "Potential fellows have to finish a small coding test, and interview with at least 2 fellows. The coding challenge demonstrates the ability of the fellows to code up unseen problems and also their ability to write (and document) clear code. The interviews provide a deeper insight into the quantitative abilities of a fellow. At least 50% of the fellows have a graduate or higher in their field of study."
  • Percentage of project time spent individually? In groups?: Products are created by the group, not by individuals alone.
  • Hours per week: 40 hours, though "fellows usually work much longer and even at times work on weekends."
  • Length of program: 4 months.
  • How long offering bootcamps/courses?: 18 months.
  • Costs: Free.
  • 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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Metis

  • Courses: Full-time immersive data science bootcamps; part-time professional development data science courses, including Data Visualization with D3, Introduction to Data Science, and Big Data Processing with Apache Spark; online data science lessons; and corporate training.
  • In person, online, or a combination? Bootcamp is in-person with 25 hours of online pre-work. Professional development courses are in-person with up to 10 hours online pre-work. Data science lessons are online. Corporate training primarily in-person, although can be delivered online.
  • US locations: New York City, New York; Chicago, Illinois; and San Francisco, California.
  • Median starting salary: Does not report median starting salaries "because of the tremendous variance in backgrounds and interests of our 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: "A typical day [at the bootcamp] includes 4-5 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 3 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 (2 to a bootcamp).
  • Percentage of instructors hired directly out of the program: 0%; Metis has hired some graduates as 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 bootcamp syllabus, Data Visualization with D3.js syllabus, Introduction to Data Science syllabus, Big Data Processing with Apache Spark syllabus, and Explore Data Science syllabus.
  • Required student background: Data Visualization with D3:  JavaScript, HTML, and CSS. Big Data: Java, C++, and Python. Explore Data Science: Python, basic linear algebra, calculus, statistics, and probability.
  • Percentage of project time spent individually? In groups?: Group exploratory data analysis project, classification and interactive dashboards,  and individual-directed projects. For the Metis data science professional development courses and for Explore Data Science, the project work is all individual.
  • Hours per week: 40-60 hours a week for bootcamp; 6-10 hours a week for professional development courses.
  • Length of program: 12 weeks for bootcamp; part-time development courses are 6 weeks.
  • How long offering bootcamps/courses?: Started in February 2014
  • Costs: Bootcamp is $15,000, including $1,500 deposit, remainder in three parts. Professional development courses are $2,500, 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, intermediary data analytics, marketing analytics, and foundations in cloud computing.
  • In person, online, or a combination? Full-time in-person, part-time evenings in-person, and a hybrid option in which students learn through a combination of online and in-person instruction.
  • US locations: Boston, Massachusetts; Charlotte, North Carolina; San Jose, California; and Seattle, Washington.
  • Median starting salary: Not provided.
  • Teacher-to-student ratio: Average of 1:10. Remote and onsite TAs also available.
  • Time for individual instruction/help: No specific answer. "We offer a number of dedicated instructional resources to help students reach their fullest potential throughout the course of the program" using competency-based curriculums.
  • Percentage of teachers with full-time data science experience: 100%.
  • Percentage of instructors hired directly out of the program: Some graduates become TAs.
  • Languages, systems, and tools learned: In data analytics courses, Excel, R, SQL and Tableau.
  • Course syllabus: Available at website on request. "Students begin by learning the theoretical foundations of the tools they are learning through a mixture of online and in-class lectures, then they learn to apply the concepts through scaffolded lab exercises. Finally, they are paired with an employer partner and bring their new skills to bear in a real project with a real fact pattern and consequences. Training in soft skills, such as communicating analytical insights, contextualizing data, and general professionalism, is woven throughout the course."
  • Required student background: No background for introductory data analytics or marketing analytics. For intermediate data analytics, general familiarity with statistics and basic Excel concepts.
  • Percentage of project time spent individually? In groups?: "The course components that students are graded on—their capstone projects and their final summative evaluations—are 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 full-time program, students spend 40 hours a week in the classroom and have additional work after class. Part-time programs range from one to two nights per week with additional course material online, with the total taking 10 to 15 hours a week.
  • Length of program: Full-time program is 8 weeks. Part-time is 15 weeks to 20 weeks.
  • How long offering bootcamps/courses?: Since fall 2015. The school, a full university, has been in existence since 1896.
  • Costs: $7,995 for Level Core Data Analytics, $5,250 for Level Set Data Analytics and Marketing Analytics, and $3,000 for Level AWS Foundations in Cloud Computing.
  • Specialty areas covered in depth: Covers communicating results to non-experts and alignment between business analytics and key business drivers.

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NYC Data Science Academy

  • Courses: Data Science Bootcamp.
  • In person, online, or a combination? Both in-person and hybrid.
  • US locations: New York City, New York.
  • Median starting salary: "Probably around $90K to $150K."
  • Teacher-to-student ratio: Student to instructor or TA ratio of 1:6.
  • Time for individual instruction/help: TAs available when "at all times" class not in session.
  • Percentage of teachers with full-time data science experience: 100%.
  • Percentage of instructors hired directly out of the program: "Many of our instructors are graduates of the program."
  • Languages, systems, and tools learned: R, Python, Linux, Github, SQL, Hadoop, Spark.
  • Course syllabus: Available online.
  • Required student background: None, but "we design individual pre-work programs to bring student skills up to speed in advance of the program."
  • Percentage of project time spent individually? In groups?:"Projects are mixed between individual and group projects.  Students probably spend between 10 hours (for the simpler projects) to 40 hours (for the capstone projects)."
  • Hours per week: 50-60 hours a week.
  • Length of program: 12 weeks.
  • How long offering bootcamps/courses?: 3 years.
  • Costs: Bootcamp is $16,000, including $5,000 deposit, remainder due on first day of class.
  • Specialty areas covered in depth:Research design, data analysis, and key business drivers

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Science to Data Science

  • Courses: 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? In-person in the U.K. or online anywhere.
  • US locations: None.
  • Median starting salary: $47,000 to $73,000.
  • Teacher-to-student ratio: N/A.
  • Time for individual instruction/help:"A lot of time is available for support and help."
  • Percentage of teachers with full-time data science experience: 66% of lecturers are "from industry or professional data scientists" while "all the technical mentors are experienced freelancing data scientists."
  • Percentage of instructors hired directly out of the program: 10%
  • Languages, systems, and tools learned: "The exact experience will depend on which project team the participant joins, as the focus of the program is on projects and each project is unique. Projects are typically related to predictive analytics and data science problems, about 70% of teams work mainly in Python, with about 30% working mainly in R. Many projects also involve database solutions such as MySQL, MongoDB or Neo4J or cloud computing or Hadoop technologies. The vast majority of projects involve some level of machine-learning work."
  • Course syllabus: "The program varies between virtual and London events. They all include various presentations, but the greatest emphasis is on project work. The teams typically spend the first week setting up their computing environments, understanding the problem at hand and creating plans for the following four weeks. They then spend a week or two conducting research, building a product or prototype and testing. For the final week or two they focus on improving on their solution, and presentation and hand over to the company sponsor. The deliverable varies from being just code and a presentation to complete prototypes and algorithms."
  • Required student background: Minimum of a Masters of Science in an analytic topic for the virtual course.
  • Percentage of project time spent individually? In groups?: 30% individual and 70% group.
  • Hours per week: "Full-time and very intense."
  • Length of program: 5 weeks.
  • How long offering bootcamps/courses?: 3 years.
  • Costs: £800, or about $1065.
  • Specialty areas covered in depth: Communicating results to non-experts and alignment between data analysis and key business drivers.

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Springboard

  • Courses: Foundations of Data Science covers basics in statistics and R programming language. Data Science Intensive covers teaches machine learning and more advanced data science techniques with Python. Data Analytics for Business teaches Tableau, Excel and SQL and applies data skills to real-world business case studies.
  • In person, online, or a combination? Online, with weekly one-to-one video calls with a data science mentor.
  • US locations: Online.
  • Median starting salary: Not provided. "On average, our alums see a salary increase of $18,000 after completing our programs."
  • Teacher-to-student ratio: 1:1.
  • Time for individual instruction/help: 30 minutes a week for most courses, with 45 minutes a week for Data Analytics for Business, and also weekly office hours.
  • Percentage of teachers with full-time data science experience: 100%.
  • Percentage of instructors hired directly out of the program: 0%, though over time expect some alumni to become mentors.
  • Languages, systems, and tools learned: Foundations of Data Science: R, Github, and SQL. Data Science Intensive: Python, SQL, and Github. Data Analytics for Business: Excel, SQL, and Tableau.
  • Course syllabus:Available online: Foundations of Data Science syllabus, Data Science Intensive syllabus, and Data Analytics for Business syllabus.
  • Required student background: No prerequisites for Data Analytics for Business. Basic programming knowledge for Foundations of Data Science. Significant programming experience and knowledge of statistics for Data Science Intensive.
  • Percentage of project time spent individually? In groups?: Under self-paced courses, students may opt to work individually or within study groups.
  • Hours per week: 10-15 hours a week.
  • Length of program: 3 months.
  • How long offering bootcamps/courses?: 2 years.
  • Costs: Foundations of Data Science and Data Science Intensive are self-paced at $499 a month. Data Analytics for Business is a fixed duration course and costs $1,499 upfront.
  • Specialty areas covered in depth: Research design and data storytelling in-depth in Data Science Intensive, and alignment between data analysis and key business drivers in-depth in Data Analytics for Business.

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The Data Incubator

  • Courses: 8-week data science fellowship (free), online data analyst course.
  • In person, online, or a combination? Online and in-person.
  • US locations: Washington D.C., San Francisco Bay Area, New York City.
  • Median starting salary: $100K to $125K median, up to $150K base.
  • Teacher-to-student ratio: 1:15.
  • Time for individual instruction/help: "Available for the majority of the 9-5 workday."
  • Percentage of teachers with full-time data science experience: All instructors have industry experience, with most having a PhD.
  • Percentage of instructors hired directly out of the program: Alumni are only brought back as TAs and guest lecturers.
  • Languages, systems, and tools learned: Python, including numpy, pandas, matplotlib, sklearn, and flask; Hadoop, with MapReduce and MRJob; Spark; Scala; JavaScript and D3.
  • Course syllabus: The Fellowship covers the fundamentals of data science including SQL, basic and advanced Machine Learning, MapReduce, Spark, Data Visualization, and specific data science case studies. The course concludes with a capstone project.
  • Required student background: Fellows have either a Master’s or PhD, mostly in STEM fields.
  • Percentage of project time spent individually? In groups?: Students work in small groups throughout the course.
  • Hours per week: 40 hrs/week average for Fellows. 4 hrs/week average for online foundations course.
  • Length of program: 8 weeks for fellowship, 4 weeks for online program.
  • How long offering bootcamps/courses?: 2 years.
  • Costs: Free for fellows but no living expenses covered.
  • Specialty areas covered in depth: "Thinking Outside the Data" is a course in the program which covers topics like privacy, ethics, experimental design, A/B testing, communication, and alignment with business metrics.

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Thinkful

  • Courses: Beginner data science.
  • In person, online, or a combination? Online only.
  • US locations: Online.
  • Median starting salary: Not provided for data science students.
  • Teacher-to-student ratio: 1:1.
  • Time for individual instruction/help: More than 40 hours a week.
  • Percentage of teachers with full-time data science experience: 100%.
  • Percentage of instructors hired directly out of the program: 0%.
  • Languages, systems, and tools learned: Pandas, Git, SQLite, relational databases, Python.
  • Course syllabus: Syllabus available online.
  • Required student background: None.
  • Percentage of project time spent individually? In groups?: Choose to work individually or across community of current and former students.
  • Hours per week: Self-paced, but usually 7 to 12 hours a week for 3 months.
  • Length of program: Same as above.
  • How long offering bootcamps/courses?: 4 years.
  • Costs: $500 a month.
  • Specialty areas covered in depth: Research design and communicating results to non-experts.

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XTOL (University of Texas at Austin and Rutgers Unversity)

  • Courses: Project-based Data Analytics and Big Data.
  • In person, online, or a combination? Online only in US.
  • US locations: Online.
  • Median starting salary: Not provided.
  • Teacher-to-student ratio: Between 1:5 and 1:8.
  • Time for individual instruction/help: "Learning teams [of 5 to 8 students] have one to two meetings with their mentor weekly, and mentors are available days, evenings, and weekends to provide individual help and advice as needed."
  • Percentage of teachers with full-time data science experience: 100%.
  • Percentage of instructors hired directly out of the program: 100% of associate mentors are graduates of the program; all have more than a year of "data science or closely related experience."
  • Languages, systems, and tools learned: Weka, R, R Studio, R Statistics, caret machine learning package, AWS, Hadoop, Python.
  • Course syllabus: "Students complete seven projects end to end – from a business or engineering problem through machine learning-based analytics to a report back to stakeholders that delivers actionable insights." Course 1: "Use statistical machine learning techniques to understand the relationship between customer demographics and purchasing behavior and then to develop a model for recommending products to specific customers." Course 2: "Learn to use statistical machine learning techniques to analyze potential new products to recommend the ones a merchant should offer, and also to build a model to predict brand preferences based on customer characteristics. At your option, you can continue to use the Weka machine learning package or switch to the R statistical programming language and the R Studio analytics environment." Course 3: "Learn how to mine and analyze extremely large data sets to provide insight to real-world business problems. You will conduct sentiment analysis utilizing cloud-based computing, machine learning tools, and the Common Crawl of the World Wide Web, and interpret the results to make and communicate predictions of vital interest to business stakeholders." Course 4: "Learn how to use the R statistical programming language and a variety of add-on 'packages' to visualize data relationships and to implement classification and regression models for emerging engineering applications, such as understanding behavior in the 'Internet of Things.'"
  • Required student background: None.
  • Percentage of project time spent individually? In groups?: Balance of time depends on preference of individual students, but group work is usually a minimum of 2 hours per week.
  • Hours per week: 30 hours per week on the 22 week track or 15 hours per week on the 44-week track.
  • Length of program: 22 weeks or 44 weeks.
  • How long offering bootcamps/courses?: Since 2002.
  • Costs: Approximately $6,000, depending on the school.
  • Specialty areas covered in depth: "In addition to technical skills our program puts heavy emphasis on communicating results to non-experts, and alignment between data analysis and key business drivers. We also address a range of soft skills including analytical thinking, principled decision making, teamwork, effective communication, negotiation, and self-directed learning."

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Have you tried any of these bootcamps before? What was your experience like?  Would you like to add a bootcamp to this article? Simply add your suggestions to the comments section.

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