Should I Do A Data Science Bootcamp?

Enrolling in a data science bootcamp is your first step toward a successful career in data science. Companies everywhere rely on data to help understand trends within their business and how to adjust for future success. A data science bootcamp is an excellent way to quickly get the training you need so you can start looking for your first role in data science. 

Let’s start with the basics: What exactly is a science bootcamp? And how do they differ from a standard four-year college degree program?

What Is a data science bootcamp?

A data science bootcamp is an intensive, immersive training program that teaches the basics of data science and guides you through creating a job-ready portfolio. Unlike a traditional four-year college degree program, these programs are shorter and offer more opportunities for hands-on learning. 

A data science bootcamp covers everything from an introduction to data to Python and SQL, statistics, A/B Testing, machine learning, and more. These types of programs are designed to help students learn a variety of frameworks and languages and prepare them for a professional career in data science. 

In a good data science bootcamp, you’ll learn how to discover patterns in data to make predictions to help businesses make decisions. Data scientists spend a lot of their time programming and collecting, cleaning, modeling, and examining data. Data scientists use many techniques to come to conclusions that include predictive analytics and machine learning. 

What are the benefits of data science bootcamps?

Data science bootcamps provide a variety of benefits, from networking opportunities to a program that adapts more readily to the latest trends in tech. Compared to college programs, data science bootcamps can adjust their curriculum more frequently to ensure students are learning the latest in data science industry trends — and learn the skills employers need.

These programs teach students a variety of data science programming languages and programs essential to entering the data science field. Python, Pandas, Java, Scala, Hadoop, R, SQL, Julia, MATLAB, and Spark are some of the languages and frameworks students can expect to learn in this type of bootcamp.

What are the top 4 reasons to attend a data science bootcamp?

1. Gain skills that are in high demand

The skills learned in a data science bootcamp are at the forefront of the technology industry. Bootcamps have the flexibility to adapt to the latest trends and in turn teach students the most relevant platforms and coding languages. 

In good bootcamps, you’ll learn all the data science programming, data visualization, data analysis, and math skills necessary to start a data science career.

And data science skills are in high demand for employers in tech. According to LinkedIn, data scientist was named the #1 most promising career of 2019 and also the one of best jobs of 2020 in America, according to Glassdoor. 

2. It gets you ready for a new career – fast

The beauty of a bootcamp is it can be completed in as little as 15 weeks. From there you have the tools and training necessary for data science jobs.

Plus, some bootcamps include career coaching beyond what you would find in a traditional university. 

3. Build your professional network

Whether you attend an in-person or virtual bootcamp, you can build your professional network. Opportunities vary from connecting with speakers and experts to forming connections with your classmates and study groups. 

As you build your professional network, you will understand more about the different areas of the data science field and which parts interest you most. Plus, you can often rely on your professional network to help you find a job. Maybe someone you met at bootcamp has a job opening at their company, or maybe they can introduce you to a colleague who has interesting roles at their company. 

Either way, a professional network is important to become part of the data science industry and to learn from what other data scientists are learning and doing in their career. 

4. Learn from experienced instructors

Unlike at a university where professors study the field they are teaching about, bootcamp instructors are typically hired because of their successful experience working in the industry. Some bootcamp instructors are even hiring managers or team leaders, which means they know exactly what you need to learn to get a job in the field. 

Plus, in a data science bootcamp, you get 1:1 access to instructors and lecturers so you have support when you need that little bit of extra help. At a bootcamp, you typically have the same instructor throughout your learning experience, versus at a university where you only get your favorite instructors for a short time in one class. 

Finally, these instructors will give you assignments to help build your job-ready portfolio. You will build projects that are directly related to typical assignments you might receive in your future industry career.  

What will I learn in a data science bootcamp? 

You can expect to learn a lot during your time in a data science bootcamp. Some of the key topics covered include statistics, machine learning, and programming languages like Python.

Here are some popular topics you can expect to learn in a data science bootcamp. 

Hard skills

Python

Python is a popular programming language that is used in website development, but it can also be used by data scientists to integrate software programs, for machine learning, deep learning, and to sort through large amounts of data. Data Scientists use Python almost daily to efficiently organize and analyze massive amounts of data.  

In addition to getting a grasp of Python programming, you’ll learn about Python libraries that are used in data science, and how to use Jupyter notebooks. Other items covered include variables, booleans and conditionals, looping and functions, and more. 

Machine learning 

Machine learning is the process of setting up computers to be able to perform tasks without explicit programming. To do that, computers are given structured data, and that’s where the data scientist comes in. 

To work on machine learning, you’ll learn regression analysis and logistical regressions. 

Data Science Fundamentals

Data science fundamentals include probability theory like combinations and permutations. You’ll also learn about statistical distributions and how to run A/B tests. 

“Big data” is also a popular topic in the data science industry. In a data science bootcamp you will learn threading and multiprocessing to work with big data. You will also learn the PySpark and AWS tools.

Other things you’ll learn in a data science bootcamp:

• Data science math skills

• Natural language processing

• Data visualization

• SQL

• Probability theory

• Data cleaning

• Data structures

• Broad data analytics

• How to navigate and manipulate databases

• And often much more

Further reading: The curriculum and coursework at Flatiron School’s flagship data science bootcamp.

Soft Skills

Problem Solving

At the core of data science is understanding ‘next steps’ based on what the data is telling you. You will learn problem solving techniques such as how to tackle a specific project, all the way to the end result of determining a solution to the business problem based on your data analysis.

Communication

As a data scientist, you will need to communicate your ideas and assessments to others on your team. In a data science bootcamp, your instructor will often act like a manager at a company and you will have to explain your analysis to the instructor, just like you would in a real job. Communication skills are critical to data science, and you’ll learn to hone that skill in a data science bootcamp. 

Networking

Networking in data science is about networking with other people, not about computer networking. It’s important to learn networking skills because, like with communication skills, you will need to be comfortable working with other people. In a data science bootcamp, you can learn and practice networking skills as you build your professional network. 

How much do data science bootcamps cost?

Costs of a data science program can vary from school to school, and can be as low as $10K or as high as almost $30K. There are several options to finance a program, scholarship opportunities, and some employers may even cover the cost of tuition. 

There are also scholarships available. Further, there are financing options available that can help cover the cost of deposit or offer more flexible monthly payment plans. 

How much will I make after graduating from a data science bootcamp?

Completing a data science bootcamp can help you land a job as a data scientist making just over $85K annually according to national averages. As you continue to advance your skills and experience you can expect to see that number grow significantly. The average Data Scientist in the United States brings home $119K with Senior Data Scientists earning an average salary north of $140K. 

Learn more about the average starting salaries for Data Science course grads from Flatiron School.

How are data science bootcamps perceived by employers?

In a study from Indeed, 72% of employers think that data science bootcamp grads are just as prepared for careers as candidates with computer science degrees. And 12% of employers think bootcamp grads are more prepared than candidates with a four-year computer science degree. 

In sum, data science bootcamps are viewed very favorably by employers and often produce job-ready alumni. Alumni often find that employers are eager to engage when a data science bootcamp is the sole thing on your resume.

Data science bootcamps vs college

When looking to become a data scientist there are two routes to explore — bootcamp or college. There are pros and cons to both and it depends on what is best to serve your needs as a student. 

Pros of bootcamps

• Bootcamps offer the flexibility to gain the skills and experience needed to get a job in a short amount of time.

• These bootcamps offer comprehensive but flexible education.

• These bootcamps come at a lower tuition price point and help lead to high-paying jobs upon graduation.

Cons of bootcamps

• The drawback to bootcamps is that you may have to pay the full tuition up front.

• But, there are loans and scholarships available so don’t let the price tag deter you.  

• Some employers still require a degree but many don’t. 

Pros of college

• Going to college to get a career as a data scientist will teach you the hard skills you need while giving you a more comprehensive, well-rounded education.

• A computer science degree is a standard that many employers look for in potential employees.

• You’ll learn the underlying tech principles that are useful as you start your career. 

Cons of college

• College tuition fees are expensive.

• You can expect to spend upwards of $99,000 on a four-year degree, versus less than $20,000 for a data science bootcamp.

• Plus, you can finish the data science bootcamp much quicker than a bachelor’s degree.  

What job can I get after a data science bootcamp?

Aside from becoming a data scientist, there are actually several other career paths you can go down thanks to your training through a data science bootcamp. 

Some career options include*: 

• Machine learning engineer

• • Avg. salary: $151k

• Machine learning scientist

• • Avg. salary: $134k (Glassdoor)

• Applications architect

• • Avg. salary: $135k

• Data architect

• • Avg. salary: $128k

• Data engineer

• • Avg. salary: $130k

• Data analyst

• • Avg. salary: $71k

• Statistician

• • Avg. salary: $98k

*stats accurate as of June 2021. 

Having the background and understanding of data and necessary processes can be leveraged into a variety of career options. 

How to choose the right bootcamp for you

Once you’ve decided to enroll in a bootcamp, there are more choices to be made and options to be had. Some of the top things to consider are full-time or part-time schedules as well as in-person versus online programs.  

Think about your learning style

Your learning style is key in figuring out the environment best suited to your needs. Some things to consider: Do you order to learn full-time or art at your own pace? Do you like to work in groups or solo? When you work do you prefer to have a partner to stay diligent or do you find yourself having good self-discipline? 

Full-time bootcamps

Full-time bootcamps are immersive programs where you spend anywhere from 40 hours in class per week, plus time outside of class for studying and projects. 

In this model, you can often complete your program within 15 weeks, allowing you to enter the workforce perhaps even quicker than expected. The benefit of this model is that it is a quick way to achieve your goals. The downside can be it is hard to balance this type of immersive program. 

Flexible pace bootcamps

Some bootcamps offer a flexible pace program. This option takes more time to complete but is often a better option for those who need to work during their bootcamp or don’t have as flexible a lifestyle. 

Can you afford the cost of a data science bootcamp?

Compared to college, bootcamps are relatively affordable and are less expensive than a four-year college degree. Short-term it can be expensive with the upfront payment nature. 

There are many flexible financing options that can make monthly payments much more feasible. In addition, there is always the option of attending part-time to not interfere with any current job and source of income. Make sure you do your homework and find the financing option that is best for your lifestyle and be sure to look into any potential scholarships.

Summary

The decision to do a data science bootcamp is ultimately up to you and your career goals. A data science bootcamp is a great way to get the training you need to become a data scientist quickly. 

From hands-on learning to career services, bootcamps are a clear, easy option for those ready to start their career journey. The real choice comes down to the type of program you want to attend. 

Still, have questions on whether you should do a data science bootcamp? Book a 10-minute chat with admissions to learn more. 

Disclaimer: The information in this blog is current as of June 16, 2021. Current policies, offerings, procedures, and programs may differ.