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How to Choose The Right Data Science Bootcamp in 2023 (With Examples)

Learn everything about data science bootcamps, including a list of top programs to kickstart your career.
Aug 2023  · 10 min read

In this blog, we will answer the most frequently asked questions about data science bootcamps in detail. Additionally, we'll review some top data science bootcamp-style learning opportunities offered on DataCamp.

If you're considering enrolling in a bootcamp, this blog will provide a comprehensive overview of everything you need to know about these intensive training programs. By the end, you'll understand what data science bootcamps entail, their benefits and drawbacks, and why they may not be the best option for breaking into data science.

What is a Data Science Bootcamp?

A Data Science Bootcamp is an intensive, short-term educational program designed to equip individuals with the skills they need to enter or advance in the field of data science. Unlike traditional degree programs that may take years to complete, bootcamps are typically designed to be completed in a matter of weeks or months. The goal is to provide a fast-paced, focused curriculum that covers essential data science topics and tools, enabling participants to quickly gain practical skills.

Why Choose a Data Science Bootcamp?

There are many reasons why people choose a data science bootcamp over a traditional degree program or online course, but the most common reason is to obtain professional certification in a short amount of time.

There are various other reasons why someone would choose to join a data science bootcamp, such as:

  • Career support. Bootcamps offer career services such as resume assistance, mock interviews, networking events, and partnerships with employers to aid in your job search.
  • Flexible schedules. Bootcamps offer flexible options to fit different schedules, including part-time, online, or in-person. This allows learners to work while still gaining new skills.
  • Cost-effective. Bootcamp tuition is typically much lower than the cost of a four-year degree in computer science or data science, making it a more financially accessible option for individuals.

Who is a data science bootcamp suitable for?

Several types of people will benefit from a bootcamp camp-style way of learning. This include:

  • Career changers. Those looking to transition into data science from another field.
  • Professional. Individuals already in the tech industry who want to upskill or specialize in data science.
  • Students. Those who want to supplement their traditional education with practical, job-ready skills.
  • Entrepreneurs: Business owners who want to understand how to leverage data in decision-making.

The Pros and Cons of Data Science Bootcamps

It is crucial to thoroughly evaluate the advantages and disadvantages of data science bootcamps prior to making a significant and time-intensive investment.

Advantages

  1. Focused and intensive learning. Data science bootcamps offer an intensive and structured data science curriculum, allowing participants to acquire knowledge quickly.
  2. Practical experience. Bootcamps often emphasize practical skills rather than theoretical concepts. Projects and team exercises simulate real-world data challenges.
  3. Industry-relevant curriculum. Since they're short-term programs, bootcamps are constantly updated to teach the latest data science tools and technologies that employers are looking for.
  4. Networking opportunities. Bootcamps attract a diverse group of participants, including aspiring data scientists, professionals transitioning into data science roles, and individuals from other data fields.

Disadvantages

  1. Fast-paced and intense. Bootcamps can be difficult for those new to data science or with limited programming experience due to their intense and fast-paced nature.
  2. Focused on practical skills. While bootcamps excel at providing hands-on experience and practical skills, they may offer less emphasis on theoretical foundations. This approach allows for quick, real-world application but may require additional learning for those interested in a deeper theoretical understanding.
  3. Varied quality. The quality and credibility of data science bootcamps can vary significantly. Some may lack experienced instructors, a comprehensive curriculum, or limited resources.
  4. Real cost. Data science bootcamps can be costly and require a full-time commitment, potentially resulting in a loss of income. It is crucial to evaluate the financial impact before enrolling.

Data Science Bootcamp vs Data Science Degree vs Data Science Online Course

In this section, we will compare data science bootcamps, degrees, and online courses to help you decide which program is best for you.

 

Bootcamp

Degree

Online Course

Cost

$7,500 - $27,500

$62,650 average in US for master’s

Varies, can be very low-cost or free

Duration

2-8 months typically

4 years for bachelor's, 1-2 years for master's

Self-paced

Time Commitment

Full-time or part-time

Full-time or part-time

Self-paced

Skills Learned

Practical and applied skills.

Balance of theoretical and practical skills.

Balance of theoretical and practical skills, ranging from beginner to advanced.

Structure

Online, in-person, or hybrid

Traditionally in-person, hybrid, or online

Online

Certification

Certificate of completion or professional certification

Bachelor's or master's degree

Course certificate, options for further certification options with access to community features and networking

Top Data Science Bootcamps

Here is a list of data science bootcamps and bootcamp-like options that offer high-quality tutorials, coding exercises, projects, and assessment tests. Each program contains multiple courses that cover fundamental statistics to machine learning applications.

1. Flatiron School Data Science Bootcamp

The Flatiron School bootcamp offers full-time and part-time options and is offered to both newcomers and those with existing skills. You’ll cover topics such as Python, SQL, and machine learning. This is a good option for those where needing a large budget isn’t an issue.

Who it's for: Those who can commit to a full-time schedule, as well as those looking for more flexibility with up to 60 weeks to complete the program.

What you'll learn: The curriculum covers a comprehensive range of data science topics, including data manipulation, statistical analysis, and machine learning techniques.

Cost and length: $16,900. The on-campus and live online programs are 15 weeks long and require a full-time commitment. The Flex program allows up to 60 weeks for completion.

2. NYC Data Science Academy

This bootcamp from NYC Data Science offers both in-person and online instruction, making it a good choice for those who want one-on-one mentorship. Again, there is a premium for this, coming in at over $17,000 for the course. There are several options for how much time you’ll need to commit, ranging from 20-30 hours per week to 40+ hours per week.

Who it's for: Individuals who are looking to commit at least 20-40 hours per week and who have the budget for in-person instruction.

What you'll learn: You'll cover R and Python for data analytics and visualization. Students will also delve into machine learning with Python and work on real-world projects. The program covers packages such as NumPy, SciPy, Pandas, Scikit-learn, Keras, TensorFlow, and SpaCy.

Cost and length: The full-time option lasts from August to December and costs $17,600. The part-time option extends from August to February and also costs $17,600.

3. BrainStation Data Science Bootcamp

BrainStation offers online training as well as in-person options in London and various cities in North America. There are both full-time and part-time bootcamps starting at various times throughout the year.

Who it's for: Individuals from various backgrounds, including those who are completely new to tech. They offer prep courses for beginners.

What you'll learn: The program includes coding and data science projects alongside real-world projects.

Cost and length: The bootcamp costs $16,500 and offers part-time and full-time formats. Shorter courses range from $3,250 to $3,950.

4. Codeup Data Science Bootcamp

This in-person bootcamp from Codeup is based in San Antonio, Texas. The full-time program covers full-stack web development and data science topics.

Who it's for: Good for individuals from various backgrounds, including those who are completely new to tech. They offer prep courses for beginners.

What you'll learn: You’ll cover a range of topics, including Python, SQL, Machine Learning, data wrangling, data exploration, and data storytelling.

Cost and length: The bootcamp costs $27,500 for their full-time, 22-week course.

5. TripleTen Data Science Bootcamp

This bootcamp from TripleTen covers a broad range of topics in a relatively short space of time (on a part-time basis).

Who it's for: Suitable for those who want a comprehensive data science education but are constrained by time, as it's a part-time program lasting just 9 months.

What you'll learn: The curriculum covers basic statistics, Python, data analysis, AI technologies, and machine learning.

Cost and length: The bootcamp costs $9,700 and offers a part-time format lasting 9 months.

Should I Take a Data Science Bootcamp?

A data science bootcamp can be a big investment of time and money, so it's important to determine if it's the right fit for your goals. Taking free online courses or introductory paid data science courses can help you get exposure to the field and gauge your interest before committing to a bootcamp.

Do you have a technical background?

If you lack experience in Python, SQL, statistics, etc., you should first focus on building fundamental programming skills.

Are you a quick learner?

Can you work independently and keep up with a fast-paced bootcamp? These types of learning environments often require self-motivated and hungry individuals who want to achieve milestones quickly.

Can you afford to pay upward of $10,000 in fees?

This is the most important consideration for many people. Can you afford to pay for bootcamp out of pocket, or will you need to finance it through a loan or scholarship? If not, lower-cost online courses and tracks may be a good option.

What are your goals?

Are you considering a career change or looking to enhance your skills? Define your goals to determine if a bootcamp, degree, or online learning is the best option for you. There is no one-size-fits-all approach to data science, so it's important to assess your specific situation before deciding on an intensive bootcamp.

Alternatives to Data Science Bootcamps

While data science bootcamps offer an intensive route to skill acquisition, they're not the only (or always the best) educational pathway available. Here are some alternatives:

  • Online platforms like DataCamp. Ideal for those seeking flexibility and lower costs. DataCamp offers specialized tracks in areas like data analysis and machine learning, complete with interactive exercises, real-world projects, and a thriving community. This allows for self-paced, comprehensive learning at a fraction of the cost of a bootcamp.
  • University degrees. Suitable for those looking for a deeper, more theoretical understanding of data science. University programs offer a broad curriculum and the opportunity for specialized research.
  • Self-study. Best for highly motivated individuals who are comfortable learning from books, online tutorials, and free courses. This is generally the most budget-friendly option but requires a high level of self-discipline.

Top DataCamp Data Science Career Tracks - The Perfect Data Science Bootcamp Alternative

Some of the best alternatives to data science bootcamps are DataCamp’s career tracks. These educational programs can provide you with the skills, knowledge, and hands-on experience to kick-start your career in data science.

1. Data Analyst with Python by DataCamp

Start your Data Analyst with Python career path with interactive exercises and learn to work with popular Python libraries such as pandas, NumPy, and Seaborn.

Learn to use real-world datasets to enhance your data manipulation and exploratory data analysis skills. Progress through the courses while mastering data manipulation, joining data, and key statistical skills like hypothesis testing.

Who it's for: This track is ideal for beginners and those who have little to no prior coding experience. It's also suitable for aspiring data professionals or researchers who want to focus on data analysis.

What you'll learn: The program covers Python programming basics and dives into data manipulation, data cleaning, data visualization, and basic statistics like hypothesis testing.

Cost and length: $25/month, billed annually. ~2 Months to complete, self-paced learning.

Skills acquired: Python, pandas, NumPy, Seaborn, data manipulation, data visualization, and basic statistics.

2. Data Scientist with Python by DataCamp

Learn data cleaning, manipulation, exploration, and visualization, machine learning, and other career-building skills you need to succeed as a data scientist.

The Data Scientist with Python career track covers essential topics for data science, with interactive exercises and hands-on experience with popular Python libraries. After completing the track, you will have the option to get certified as a Data Professional by taking certification exams that come with career support.

Who it's for: This track is designed for those who want to build a comprehensive skill set in data science, including both data manipulation and machine learning.

What you'll learn: The curriculum includes Python essentials for data science, statistical techniques, machine learning, and predictive modeling.

Cost and length: $25/month, billed annually. ~4 Months to complete, self-paced learning.

Skills acquired: Python, pandas, Seaborn, Matplotlib, scikit-learn, statistical analysis, and machine learning.

3. Machine Learning Scientist with Python by DataCamp

It is the next step in your data science career, focusing purely on the high-demand skills of AI and machine learning.

With a Machine Learning Scientist with Python career track, you will be able to master essential skills and tools needed for a career as a machine learning engineer/scientist.

You will learn to process and extract features from data, train models, assess performance, and tune hyperparameters with popular Python packages like Scikit-learn, Spark, and Keras. By the end, you will gain practical experience applying machine learning to real datasets across domains, including natural language and image processing.

Who it's for: This track is for individuals who already have some Python programming experience and want to specialize in machine learning.

What you'll learn: The program provides a comprehensive introduction to machine learning in Python, including supervised, unsupervised, and deep learning techniques.

Cost and length: $25/month, billed annually. ~5 Months to complete, self-paced learning.

Skills acquired: Python, scikit-learn, Spark, Keras, feature processing, model training, performance assessment, and parameter tuning.

Making the Most of Your Data Science Bootcamp Experience

Before starting the program, check out the following tips on making the most of your data science bootcamp.

  1. Ask questions. Don't feel shy or anxious about asking simple questions. These questions help clear up any confusion in your mind. You can ask for help from your instructor, teacher's assistant, or peers.
  2. Absorb the feedback. Improve your skills rapidly by applying feedback from assignments and projects. Embrace critiques as opportunities for growth. You can work on some data analysis projects to keep sharpening your skills.
  3. Go above and beyond. Instead of limiting yourself to a simple project. Experiment with additional analyses and visualization tools and techniques.
  4. Learn additional tools beyond the core curriculum. Use online resources to learn more about Python packages, programming languages, and frameworks. Don't limit yourself to the core curriculum.
  5. Networking is important. Get to know everyone in your cohort. Reach out to alums working in the field. Attend local or online data science meetups to make connections. It will help you land a job fast.
  6. Plan your job search early. Use career services provided by the bootcamp to develop your data resume, portfolio site, LinkedIn profile, and interview skills.
  7. Keep learning. Even after graduation, continue learning through mentorship programs, self-study, blog posts, tutorials, and keeping up with the latest data science trends.
  8. Treat it as a full-time job. Commit the required hours even if you are working part-time. Focus 100% during sessions, and don't multitask.

Conclusion

In conclusion, choosing the right data science bootcamp can help when starting a career in this fast-growing field, but it’s not the right choice for everyone.

In this blog, we have discussed why data science bootcamps can be useful, their pros and cons, comparisons between traditional degrees and online courses, and how to make the most out of the program.

We’ve also looked at some top bootcamps, as well as some more viable alternatives to help you jumpstart your data science career.

If you want to learn more about your options, check out the Data Professional Certification page. It will provide you with all of the necessary information on courses, resources, assessment tests, and projects for you to get certified as a professional data scientist.


Photo of Abid Ali Awan
Author
Abid Ali Awan

As a certified data scientist, I am passionate about leveraging cutting-edge technology to create innovative machine learning applications. With a strong background in speech recognition, data analysis and reporting, MLOps, conversational AI, and NLP, I have honed my skills in developing intelligent systems that can make a real impact. In addition to my technical expertise, I am also a skilled communicator with a talent for distilling complex concepts into clear and concise language. As a result, I have become a sought-after blogger on data science, sharing my insights and experiences with a growing community of fellow data professionals. Currently, I am focusing on content creation and editing, working with large language models to develop powerful and engaging content that can help businesses and individuals alike make the most of their data.

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