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Our Guide to the Best Data Science Bootcamps

When the Harvard Business Review named data scientist the “sexiest job of the 21st century” in October 2012, Facebook had yet to hit a billion users. Today Facebook has tripled its user base, even as younger generations eschew it and flock to sites like Tiktok and Snapchat. And this massive inflow of raw data ripe for analysis is only the tip of the iceberg when you consider the data harvested through the increasing automation and connectedness driving industries like manufacturing, ecommerce, finance, and healthcare.

So it’s not a surprise that when Thomas Davenport revisited his prediction early in 2022, he found there would surely be increasing differentiation in responsibilities as the field of data science continued to mature. It was safe to say that data scientists would continue to be in high demand. Data from the US Bureau of Labor Statistics bears this out, with the US data science job market projected to grow by 21% from 2021 to 2031, over fourfold the growth rate the BLS projects for all occupations.

Much of this growth will happen at the beginning of the career pipeline, where aspiring data scientists will compete to land entry-level positions that pay $80,432 to $101,098 per year, well above the $58,260 that the average American earned in 2021.

Traditionally in data analytics and other quant-heavy fields, these entry-level positions have gone to those holding bachelor’s degrees. In data science, however, another educational option is increasingly offering a viable path for entering the field: the data science bootcamp. Data science bootcamps are also allowing those holding bachelor’s degrees and working in another field to upskill and change career paths.

What exactly does a data science bootcamp entail, and who is it right for? What do you learn, and in what format? Finally, what factors do you have to take into consideration as you decide if the investment in a data science bootcamp is worth it for you? In this article, we’ll dive into these questions before presenting our picks for the best data science bootcamps out there.

What is data science?

Before we get started, let’s clear up an important question: what actually is data science? It seems like a simple question, but with so much overlap with business analytics and machine learning engineering, it’s important to clarify. According to IBM, data science is an interdisciplinary field concerned with analyzing large data sets to derive actionable insights and presenting these insights to internal and external stakeholders. It’s also concerned with establishing new tools and techniques for this analysis.

A data scientist works across all stages of the data pipeline, identifying research questions, preparing data (turning “raw data” into usable data), performing data analysis using machine learning algorithms and models, and persuasively communicating findings using data visualization tools.

What’s a data science bootcamp?

Now that we’ve clarified what exactly data science is, let’s dive into what you can expect to get out of a data science bootcamp and how it differs from other options out there.

A data science bootcamp is generally a months-long, intensive course of study designed solely to prepare participants for a career as a data scientist. Accordingly, the focus is on practical, job-ready skills, with less emphasis placed on theoretical background or the broader education a bachelor’s degree would provide.

Oftentimes, data science coursework is supplemented with a capstone project and a careers-guidance component. The capstone project gives students the opportunity to apply what they have learned independently, with the final project serving as the first entry of the data science portfolio that many employers will want to see during the recruitment and hiring process.

Careers guidance can take many forms. Some bootcamps offer weekly one-on-one mentoring throughout the course of the program. Others have career departments that can provide advice on everything from resumes and cover letters to interviewing skills. Some programs also curate jobs boards accessible only to bootcamp attendees.

Data science bootcamp or data science certificate program?

The way we see it, all bootcamps are certificate programs — students who successfully complete one are awarded a certificate — but not all certificate programs are bootcamps. In general, you can expect a data science bootcamp to distinguish itself from a regular certificate program by being:

Longer: While some non-bootcamp data science certificate programs can be completed in as little as 3 hours a week over the course of several weeks, data science bootcamps generally require a significant time commitment, generally three or more months of 20+ hours of instruction per week. Of course, a bootcamp won’t have nearly the time commitment of a degree program.

More intensive: In line with the bigger time commitment, data science bootcamps will also offer more intensive instruction than their non-bootcamp counterparts. While some non-bootcamp certificate programs offer at best a cursory overview of the different aspects of data science, you can expect a bootcamp to dive deeper into more topics.

More expensive: As would be expected with a longer run-time and higher intensity, a data science bootcamp will generally cost more than a non-bootcamp certificate program, with tuition often running in the tens of thousands of dollars, not the hundreds and thousands you would expect with a non-bootcamp certificate. At the same time, bootcamps offer a more affordable option than most degree programs.

Geared to different audiences: As we detail in our guide to data science certificate programs, certificate programs have a variety of use cases, including serving as a low-cost, low-time way to see if a data science career is appealing, helping professionals add new skill sets, or providing background in the field for managers and executives. 

Data science bootcamps, by contrast, only really have one purpose: to help aspiring data scientists gain the skills necessary to land an entry-level job as a data scientist. To this end, data science bootcamps also offer extensive career services to help students land job offers quickly.

Who are data science bootcamps for?

So who are these aspiring data scientists for whom a data science bootcamp can serve as a jumping off point to a new career? The good news is that it can really vary.

Some bootcamp attendees might have existing backgrounds in STEM fields such as information technology (IT), data analytics, computer science, computer engineering or applied statistics, but many will come from the social sciences or humanities. While some might already have a bachelor’s degree, for many attendees a data science bootcamp is the first experience of post-secondary (post-high school) education.

Some programming experience or experience with statistics will typically help a student get more out of a data science bootcamp, but this is not essential. In fact, some programs even offer self-paced pre-course modules in these areas to help those with less experience hit the ground running.

Regardless of background, attendees of data science bootcamps are united in their goal: to make a quick but lasting change in their career without as much time and money as it would take to pursue a data science bachelor’s or master’s degree.

What are the different ways you can attend a data science bootcamp?

Aspiring data scientists considering a data science bootcamp will have options when it comes to the camp’s modality and schedule.

Self-Paced Online

Self-paced online bootcamps offer pre-recorded learning content that can be accessed asynchronously, so attendees have the flexibility to study when it suits them and progress on their own schedule.

While some self-paced online bootcamps will be open-ended, meaning that students can take as long as they want to complete the curriculum, others will have concrete start- and end-dates, potentially aligned with exams. Many will offer one-on-one mentoring opportunities in addition to the course content, which can happen live through video chat.

Because they offer asynchronous learning, self-paced online bootcamps are a great option if someone wants to study part-time, either because they wish to continue working or have familial obligations.

Live Online

Instead of pre-recorded videos, live online data science bootcamps feature real-time instruction from faculty through a video conferencing service. To encourage student-teacher and student-student interaction, these bootcamps are often cohort-based, meaning classes are capped at a relatively low number of students, usually around 35.

Because instruction is synchronous, there are sometimes opportunities for group projects in addition to the traditional independent capstone project. Live online bootcamps can be offered both part-time and full-time, but because students learn in real-time, they will have to pick an option at the outset and stick with it.

Hybrid Online & In-Person

Hybrid online & in-person data science bootcamps pair synchronous and/or asynchronous online instruction with in-person learning and events. These are great options for aspiring data scientists who want opportunities for in-person interaction and networking but also want to have the flexibility to complete most of the program from their couch. As with live online bootcamps, hybrid bootcamps allow for greater collaboration between students on projects.

While part-time study is possible with hybrid bootcamps, the in-person component will usually be inflexible and full-time, if only for a brief period. This means that attendees will have to make the appropriate accommodations, either by asking for time off from work or finding coverage for other obligations.

If online is your preferred learning modality, check out our guide dedicated to online data science bootcamps for a deeper dive. 


The option most resembling traditional learning, an in-person bootcamp is just that: instruction that takes place in a classroom with a live instructor. As would be expected, students often have greater opportunities to interact than they would in self-paced, and even live online bootcamps.

Bootcamp providers often offer part-time and full-time options for in-person bootcamps to allow both those who need flexibility and those who want to maximize the efficiency of their study to attend.

What core topics do data science bootcamps cover?

While there is certainly variation, the core curriculum of a data science bootcamp will be consistent across the board. Students can expect to cover the following:

  • Fundamental computer science skills

  • Statistical modeling

  • Machine learning fundamentals

  • Data management

  • Data visualization

We’ll go into each in detail.

Fundamental computer science skills

Either in self-paced prerequisite work or during the course itself, students will learn how to write code using SQL, Python, and/or R programming languages. SQL (usually pronounced “sequel”) is a programming language used to manage data in databases.The R programming language was instead created to enable statistical computing and graphics. Python is a general-purpose programming language that is a frequent favorite in the data science community because of its great libraries for data science and machine learning, including Pandas, TensorFlow, and Keras.

For more, head to our data science programming language primer.

Statistical modeling

Statisticsthe collection, organization, and analysis of numerical data — and probability, the calculation of the likelihood that a particular event will occur — are crucial for data science because they allow data scientists to learn from existing data sets and use relevant insights to predict future outcomes. 

In a data science bootcamp, students learn methods of statistical modeling and predictive analytics that will allow them to develop and test hypotheses from data and use linear regression to make predictions.

Machine learning fundamentals

The discipline of machine learning focuses on developing algorithms that can teach themselves to perform complicated analysis and other tasks much more quickly and efficiently than a human could. Especially when big data sets are involved, machine learning is essential for data science. 

Bootcamp students can generally expect to cover elementary machine learning algorithms; machine learning methods like unsupervised learning, supervised learning, reinforcement learning, and deep learning with neural networks; and machine learning libraries like PyTorch and Pandas. 

Oftentimes, students also learn the basics of linear algebra, a discipline of applied mathematics focusing on the use of linear equations and matrices to model natural and artificial phenomena and compute with these models.

Data management

Data science bootcamp students will also learn the fundamentals of data management. Data management is crucial to ensuring a viable, efficient, and effective data pipeline — essential to any data science project.

Core data management skills taught in a bootcamp generally include web scraping, data cleaning, data mining, cloud computing with services like Amazon Web Services (AWS), and basic data engineering.

Data visualization

While the title would suggest that data scientists spend most of their time in deep analysis of data, a large part of the job involves communicating findings of this analysis to interested stakeholders, many of whom aren’t as technically or data-savvy. Crucial to this communication is data visualization, which puts data and the findings of analysis in a more easily digestible form.

In a data science bootcamp, students generally practice their data visualization skills using Tablea, an industry-standard software that allows for the creation of elegant and informative dashboards, among other visualizations. Other data visualization softwares sometimes taught include Matplotlib and Seaborn.

As mentioned above, students are often given the opportunity to apply all of these skills through a capstone project at the end of the bootcamp. 

How much does a data science bootcamp cost?

The cost of a bootcamp varies depending on the institutions and the type of bootcamp. Generally, bootcamps that are full-time, live, and/or in-person will be more expensive, while part-time and self-paced online bootcamps will be more affordable.

The average bootcamp cost $11,727 in 2020, far less than a traditional degree program in the US, though some data science bootcamps can cost as much as $18,000. It’s worth noting, however, that there are a contingency of self-paced options like Coursera, DataCamp, Udacity, and Udemy that, while not truly “bootcamps,” offer extensive data science training for much less. You can take courses for credit on Coursera, for example, for as little as $39/month.

Some bootcamps offer financing, installment plans, or income-share plans where you pay back a fraction of your income once you get a job. Others have guarantees that you will get a job offer or you get your money back. While these sound appealing — and they can be — it’s also important to always read the fine print: with financing, installment plans, and income-share plans you can often end up spending much more than you would on tuition up-front, while job guarantees often require strict adherence to a set of job search procedures that, if not followed, void the guarantee.

What kind of positions do data science bootcamps prepare attendees for?

$18,000 is certainly not cheap — to be a good investment, there needs to be a significant return in terms of salary. Luckily, the kinds of roles that data science bootcamps prepare attendees for, mostly entry-level data science and data analyst positions, suggest that for a driven and successful attendee, the tuition for a data science bootcamp will pay for itself over time.

Data Analyst vs Data Scientist

Typically, data analysts will have a narrower scope of work, performing more rudimentary analyses to identify trends from existing databases. According to, the salary range for data analysts is $73,002 to $91,552. 

Data scientists instead focus more on developing predictive models and other data tools, as well as performing more advanced analytics. According to, the salary range for junior data scientists is $80,432 to $101,098.

The differences in the responsibilities of data analysts and data scientists become clearer when looking at the following real-world positions. You can also check out our deeper dive on the difference between a data analyst and a data scientist.

Data Analyst, Service Operations - New York Life Insurance Co.

New York, NY


  • Act as a reporting and data analysis advisor.

  • Serve as a SME (Subject Matter Expert) within the service operations team, regularly advise on proper dispositioning of reviews and how to leverage the tools for maximum efficiency and insights.

  • Guide team to analyze and opine on the effectiveness and trending and reporting as well as identify gaps and opportunities for improvement.

  • Manage sites, lists, and documents in SharePoint that meet individual line of business needs while also enabling data aggregation and reporting.

  • Collaborate with key stakeholders and business partners to ensure quality systems provide line of sight to the highest priority quality issues and needs.

  • Ensure alignment between the quality teams on the information collected.

  • Drive awareness and understanding of how to interpret potentially uncomfortable quality results at the individual, process, and organizational levels.

  • Influence front line business partners toward action to resolve quality issues.

  • Keep up with current best practices regarding risk management/internal auditing.


  • Bachelor’s degree preferred

  • 2+ years of experience in data collection & storage, SharePoint administration, or Power Platform applications 

  • Strong reporting and data analytics skills

  • Strong verbal and written communication skills 

  • Comfort and confidence in interaction with all levels of management

  • Ability to solve problems, multi-task and manage changing priorities

  • Proficiency in Microsoft Suite with strong Excel skills

  • Familiarity with Power Apps and Power Automate

  • Relevant insurance or securities knowledge is a plus

  • Microsoft Certified: Power Platform Fundamentals certification is a plus

  • Proficiency in Tableau Visualization creation is a plus

Salary range: $55,000–$85,000, depending on qualifications

Junior Data Scientist - Nissan

Smyrna, TN


  • Utilize expertise in statistical and machine learning methods using tools such as Python & R programming languages and proficiency in big data (ex. Hadoop) & cloud infrastructures to perform tasks related to all aspects of model development.

  • Identify what data is available and relevant while leveraging new data collection and analysis processes.

  • Collaborate with internal and external SME’s to select relevant sources of information

  • Interface with IS/IT for data pipeline needs, collaborative data curation, certification, and appropriate data product sourcing.

  • Develop experimental design approaches to validate findings or test hypotheses.

  • Provide on-going tracking and monitoring of performance of decision systems and statistical models. 

  • Perform rapid prototyping & POCs (proofs of concept).

  • Evaluate and improve system operations by conducting systems analysis and ongoing continuous improvement of machine learning models & output.

  • Recommend changes in policies and procedures. 


  • BS in Data Science or related STEM field

  • At least 3 years of relevant experience in data analysis, data management, quantitative and qualitative research and analytics experience.

  • Proven experience in building, deploying, and maintaining analytics models. 

  • Experience in business needs assessments, prioritization techniques, and business case development desired. Prior automotive experience a plus.

  • Ability to find solutions to loosely defined business problems by leveraging structured, semi-structured & large unstructured datasets.

  • Ability to use appropriate SQL tools and techniques to explore given datasets or databases including data preparation, cleansing, and data product assembly.

  • Advanced knowledge of statistical and machine learning methods required. High proficiency in statistical analysis, quantitative analytics, forecasting/predictive analytics, multivariate testing, and optimization algorithms.

  • Can write functional code using common programming languages (R and/or Python).

  • Builds models which can successfully determine whether relationships exist between various data & reach insights.

  • Ability to synthesize data, uncover inherent trends in the data, make recommendations about associated opportunities and implications to business performance, & communicate findings & recommendations to a variety of audiences.

Salary Range: $72,000–$92,100, depending on qualifications (Indeed estimate)

What should you consider when deciding if a data science bootcamp is right for you? (And if so, which?)

Return on Investment

Compared to traditional degree programs, data science bootcamps are relatively inexpensive and flexible — but that doesn’t mean you should consider them a casual investment. As you decide whether you should pursue a bootcamp, and if and when you decide to do so, decide which to pursue, you want to take a high-level view of the financial sense a data science bootcamp makes for your particular situation. Individual factors to keep in mind include:

  • Current Earning Potential: If you are already earning upwards of $100k a year, taking time off work to reskill for data science full-time doesn’t make much sense if there aren’t pressing risks to your current profession. However, if your current profession has a low earning ceiling, a data science bootcamp could offer a path to much higher lifetime earnings. It’s also worth keeping in mind that pursuing a data science educational program part-time could potentially alleviate any loss of earnings.

  • Disruption of Current Earning: A bootcamp costs more than just tuition. If you are currently working and would have to take time off to complete a bootcamp, add that unrealized income to the total bill of your education. You might very well be able to recoup the lost income and then some, but losing that income in the short term might also cause immediate financial difficulties.

  • Modality: The kind of bootcamp you choose (part-time or full-time, online, in-person, or hybrid) matters a lot for its financial viability. If you chose a part-time, online option, then it’s feasible you could continue working while studying on nights and weekends. In this case, you wouldn’t need to worry about any lost income. If you choose a full-time, in-person option, you might need to worry about lost income plus travel and even relocation costs.

  • Financing: Another factor that goes into calculating whether a data science bootcamp makes financial sense is whether there are realistic financing options that won’t saddle you with unmanageable amounts of debt. Is there a zero-interest installment plan? Or financing plans through a lending partner? Many bootcamps partner with private loan providers such as Ascent, Meritize, Upstart, or Climb. According to CourseReport, initial APRs for these loans can range from 5% to 7.5% for 2-3 year terms. Alternatively, is there an income-sharing agreement that lets you pay off the bootcamp with a portion of your salary once you land a job? A job guarantee? Scholarship or grant options available to you? 

  • Probability of Success: We’ve already covered the substantial salary expectations for an entry-level data scientist — but it’s important to be open-eyed about whether you will realistically be able to land a job. Do you lack natural aptitude for math? Do you lack the kind of work ethic or discipline needed to the largely independent study of a bootcamp? Asking (tough) questions like these can help you be sure that you remain grounded as you make a significant financial decision. Of course, it’s not just about your innate potential for success. It’s also important to ensure that a particular data science bootcamp leads students to successful outcomes. Whenever possible, allow outcome data to guide your choice.

  • Other Options: Below, we’ll give an overview of the other kinds of educational paths that can help you land a data science job. While a data science bootcamp is certainly relatively inexpensive and flexible compared to these, certain paths — especially a data science master’s degree — can offer more financial upside in the long run, even if they cost more up front. Many mid-level and senior data scientist positions, which generally offer compensation in the high $100,000s and beyond, require applicants to hold a master’s degree.

What are some alternatives to data science bootcamps?

While a bootcamp is a great option to get started down a data science bootcamp, it’s not the only option. As you are deciding whether to pursue a bootcamp, consider the following as well:


Now more than ever, resources are available for free online that can help you teach yourself data science. Especially with the weight given to data science project portfolios by recruiters and interviewers, those who are extreme self-starters might consider hitting up Reddit or other websites to find the latest ways to self-teach programming, statistics, linear algebra, machine learning, and other data science fundamentals.

But while self-study is a viable option, we must stress that it does require a high level of discipline and doesn’t provide the networking and careers-guidance opportunities of formal educational paths.

Data Science Certificate Program

We’ve already covered the differences between a data science bootcamp and a non-bootcamp certificate program. While these certificate programs sometimes lack the rigor needed to realistically upskill sufficiently to land an entry-level data science job, they are still options for those who already have some experience and the resume-boost of a credential or those who are self-starters but need more structure than mere self-study would provide. Sometimes, a data science course or graduate certificate is all that is needed to break through.

If you’re interested in data science certificate programs, head to our guide for more.


Data science apprenticeships are open to individuals from a variety of backgrounds looking to transition into data science, from those holding GEDs and associate’s degrees with little formal experience to those already working at a company in a different position. Apprenticeships pair rigorous instruction in data science basics with on-the-job training in which apprentices make real contributions. While the other educational opportunities listed typically require the learner to pay for their education, apprenticeships are unique in offering training for free, or even for a modest salary. After completing their program, an apprentice can go on to earn a full data science wage, either at the company where they apprenticed or elsewhere.

Massive open online courses

Data science massive open online courses are free or low-cost open-access courses that can support thousands of participants at any one time. DS MOOCs can have a variety of different foci, from discrete skills to comprehensive data science training, to applied data science practices, through asynchronous materials such as pre-recorded videos, readings, and problem sets. Sometimes, students have the opportunity to interact through digital touchpoints such as a message board, chat, or discord.

Cohort-based course

Data science cohort-based courses, an alternative to MOOCs quickly growing in popularity, promise more learner engagement by offering smaller class sizes, more interaction with faculty and peers, and more emphasis on skill practice and feedback. As with MOOCs, data science CBCs can serve many different purposes for learners at all stages of their careers. Because of the higher level of interaction with instructors and peers, smaller class sizes, and synchronous study, these are generally more expensive than their massive, asynchronous alternatives.

Bachelor’s degree

While a bachelor’s degree would seem to offer a far more substantial education and a more impressive credential than a bootcamp, it’s unclear whether they offer better employment prospects.

They are also longer (typically four years) and more expensive. Tuition for bachelor’s degree programs can vary widely, especially between in-state tuition at public universities and tuition at elite private universities. In general, undergraduates can expect to spend between $10,000 and $60,000 on tuition per year before living expenses, generally for 4 years.

Historically, the best majors for those wishing to eventually enter data science roles have been those that allow students to gain training in advanced mathematics, programming, business, or data analytics, such as computer science, mathematics, finance, or information technology, sometimes with relevant minors. Recently, colleges and universities have been offering specific majors in data science.

If you’re interested in learning more about data science bachelor’s degrees, you can check out our guide to the best data science undergraduate colleges and universities.

Master’s degree

If you already have a bachelor’s degree, you should consider a data science master’s degree as an alternative to a bootcamp, especially if you are able to part with the money and time required to earn one.

While those with only a bootcamp certification or a bachelor’s degree may be able to gain promotion from an entry-level position to a mid-level data scientist role, frequently those interested in accelerating their progress down a data science career path will level-up their skills and gain exposure to new, more advanced areas of data science through graduate study. Accordingly, for those who are convinced data science is the field for them and already hold a bachelor’s degree, going straight into graduate school for a master’s data science program may help save money and time down the road.

Universities frequently offer full-time in-person master’s programs, but there are more and more part-time and online options for students looking for the flexibility to continue working or care for a loved one while they study.

As with bachelor’s programs, tuition for data science master’s programs can vary widely, not only because of the difference between public university in-state tuition and private university tuition, but also because of the availability of online and part-time programs. That said, graduate students can still expect to spend between $10,000 and $60,000 on tuition per year before living expenses.

If you’re interested in learning more about data science master’s degrees, check out our guide to learn more.

Our picks for best data science bootcamps

It’s important to emphasize that there is no single “best” data science bootcamp for all the aspiring data scientists out there: students come to bootcamps from a variety of different backgrounds and with a variety of different experiences, learning styles, and needs. Accordingly, for our picks, we’ve foregone ranking and focused instead on pointing interested individuals to a variety of programs that we think will deliver them lasting value. In making our decisions, we’ve looked for programs of a variety of different modalities (part-time, full-time, online, in-person, hybrid), all with the following qualities:

  • A rigorous curriculum: It’s not called a bootcamp because it’s supposed to be easy. The goal is to get you ready for an entry-level job, and so we’ve picked programs that jam-pack their syllabus with useful concepts, skills, and training.

  • Practical application: While in a traditional degree program you might have more time to dive into theory, in the abbreviated period of a bootcamp you need to get right down to putting what you learn into practice. For this reason, we’ve picked programs that emphasize job-readiness through curricula that focus on real-world situations and create opportunities for meaningful independent projects.

  • Robust careers-guidance: A rigorous curriculum: It’s not called a bootcamp because it’s supposed to be easy. The goal is to get you ready for an entry-level job, and so we’ve picked programs that jam-pack their syllabus with useful concepts, skills, and training.

  • Practical application: While in a traditional degree program you might have more time to dive into theory, in the abbreviated period of a bootcamp you need to get right down to putting what you learn into practice. For this reason, we’ve picked programs that emphasize job-readiness through curricula that focus on real-world situations and create opportunities for meaningful independent projects.

  • Robust careers-guidance: Attending a bootcamp is not just about learning how to be a data scientist, but about learning how to land a job as a data scientist. The programs we’ve picked are committed to helping students prepare for the job market, offering resume and cover letter help, jobs boards, 1:1 mentoring, and more.

  • Successful alumni: If you’re paying the tuition for a bootcamp, you want to know you’ll get your money’s worth. The programs we’ve picked are either able to demonstrate that their students successfully find data science jobs or are willing to offer a money-back guarantee.

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General Assembly Data Science Immersive

General Assembly’s Data Science Immersive features a rigorous 12-week curriculum taught by expert instructors, as well as one-on-one mentorship opportunities and a massive hiring network with partners including Disney, Samsung, J.P. Morgan, Deloitte, and many more.

In addition to covering data science fundamentals and exploratory data analysis for real-world problems, students learn classical statistical modeling, different machine learning models, and about recommender systems, neural networks, and computer vision models.

Throughout, the focus is on applying the knowledge gained: each unit has an associated project students complete en route to a final capstone project.

  • Length: 12 weeks full-time

  • Modality: Live, in-person, in New York City

  • Prerequisites: Mathematical foundation and familiarity with Python

  • Career Support: One-on-one expert mentorship, networking opportunities, hiring partners

  • Cost: $15,950

  • Financing Options: Income share agreement, loans, installment plans, tuition discounts, GI Bill

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Flatiron School: Data Science

Like General Assembly’s program, Flatiron School’s Data Science program teaches students basic skills in data science and analysis with Python, statistical modeling methods, as well as machine learning fundamentals and advanced methods involving deep learning and big data. But Flatiron differs from GA in that Flatiron’s program can be taken either full-time or part-time, in-person or online. 

Flatiron students get one-on-one interaction with instructors to get feedback and advice on their projects, as well as one-on-one support from a career coach for 6 months following the course.

  • Length: 15 weeks full-time or 40 weeks part-time

  • Modality: Live, in-person, in New York City, or online

  • Prerequisites: Students complete preparatory work focusing on programming with Python

  • Career Support: One-on-one support from career coach for 6 months following the course

  • Student Outcomes: According to Flatiron’s own data, 84% of those 2,214 graduates of their programs that completed a job search cycle accepted a full- or part-time job offer related to their field of study within a year of completing the program.

  • Cost: $16,900

  • Financing Options: Loans, installment plan

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Practicum’s Data Science Bootcamp

Practicum’s Data Science Bootcamp offers a sprint-based, self-paced data science curriculum where students cover Python, statistical data analysis and software engineering, and fundamental and advanced machine learning (including deep learning), all on a user-friendly proprietary coding platform with live code-review. 

Practicum’s bootcamp is notable in that students complete so-called “Apiary projects,” externships with real companies that leave students with portfolio fodder and endorsements. Students also take a career prep course and have access to one-on-one career coaching. All this for substantially less than GA and Flatiron.

  • Length: 9 months, part-time (~20 hours per week)

  • Modality: Self-paced online

  • Prerequisites: None

  • Career Support: Externship, career prep course, one-on-one support from mentor

  • Student Outcomes: According to Practicum’s data, their employment rate exceeds 80%. Practicum offers a money-back guarantee.

  • Cost: $10,900

  • Financing Options: Monthly payments, income share agreement, loans 

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Georgia Tech: Data Science and Analytics Boot Camp

Delivered by edX, Georgia Tech’s Data Science and Analytics Boot Camp offers a flexible, university-backed data science curriculum through 23 weeks of part-time, live online classes. Geared towards students with less experience with technology and analytics, this bootcamp features a crash course in Excel before moving to data analytics using Python, database management with SQL, machine learning and data visualization with Tableau, and a final culminating project.

Students have access to learning support through text or video chat, as well as a one-on-one tutor. Georgia Tech maintains a network of 250+ potential employers, including UPS, Walmart, and BCG.

  • Length: 24 weeks, part-time (9 hours per week)

  • Modality: Live online

  • Prerequisites: None

  • Career Support: Access to career services including portfolio, resume, and interview guidances, as well as an employer network

  • Cost: $10,000

  • Financing Options: Interest-free payment plan

What’s next?

We’ve just given you all you need to know to get started researching data science bootcamps and choosing the right one for you — but what if you’re still on the fence? If you’re interested in learning more about a data science career before diving into a bootcamp, check out our article on the typical data science career path and our step-by-step guide to landing a data science job. Perhaps you’re less interested in developing new methods of analytics and more interested in performing analysis with existing methods? If so, then maybe an educational program in data analytics is more your speed. To learn more, check out our guides on: