Recently, I had multiple well-educated young mothers contact me if they should pursue a data science bootcamp. Let's talk about it.

Bootcamps can be a great way to learn about data science and data science and machine learning-related topics. However, they aren't for everyone. Here's what you should consider if you are considering a bootcamp in data science.

Data science has been a hot topic in recent times. There are many options for learning data science but most of them cost a lot of money. Some data science bootcamps even cost more than a college degree. This article discusses the costs of these bootcamps, some of the training methods they use, their pros and cons and more.

Data Science Bootcamp – What Is It?

Data science bootcamps are one of the most effective ways to learn how to do data science in a matter of months. They focus on a few topics, such as data analytics, data visualization, and machine learning. The instructors at data science bootcamps are generally experienced professionals and data scientists.

Data science bootcamps are programs that are designed to give you a brand new skill set that you can use on the job. They can be very challenging and difficult, but they can also be very rewarding.

There are many different types of programs that you can take, and some of them are designed for people who want to learn about data science without degrees in the field. Others are designed for people who already have degrees.

Data science bootcamps are a great way to round out your education and give you the skills that you need to get a job in the field. They are also a good way to get you started in the field if you don’t have the right credentials yet.

Who Should Go For Data Science Bootcamp?

When you think of data science, you probably think of people with PhDs in statistics, analysts who spend their days poring over reams of data and making sense of it all. But is that what a data scientist really does?

What if you don’t have a background in statistics but you want to learn data science anyway?

That’s where a data science bootcamp comes in. These bootcamps can help you quickly learn how to use the skills and tools to get a job as a data scientist.

One of the most interesting questions I've been asked lately is whether or not I think well-educated moms should go to a data science boot camp. This question has come up in a number of different contexts, but it seems to be indicative of a larger trend.

What Does It Take To Make It In A Bootcamp?

As a data science bootcamp graduate, youI have to learn how to become a data scientist quickly. As if that weren’t hard enough, you'll also have to build a data science portfolio. All that in minimal time.

First of all, it takes money to enrol. Most data science bootcamps are expensive. Especially in countries like Germany where education is free, paying five figures for bootcamps is a serious investment. Additionally, conventional degrees often have financial aid options, whereas bootcamps do not fall under these regulations. Money can be a huge motivator, as well as, a source of anxiety, or simply be prohibitive for someone that isn't already well off or can't risk the investment.

It takes time. While data science bootcamps promise to be a fast track to a data science job, it still takes immense amounts of learning. They condense the information to the most applicable and important information. Nevertheless, it will be up to a year of intense study. This is important to consider in addition to other obligations.

Different bootcamps will have different technical pre-requisites. You'll need a computer at least and internet access. In my opinion having a prior degree that has taught scientific thinking is very valuable in data science. Programming experience will be a huge benefit but most bootcamps will assume no programming experience.

Advantages of getting a data science bootcamp

The biggest advantage of bootcamps is their focus on application, jobs, and the structure they provide for students.

You could teach yourself how to do data science on your own and some people do learn how to do data science on their own, but it is much better to have a mentor guiding you through your learning process because they can spot mistakes and point you in the right direction faster than you would be able to on your own.

The accelerated learning in bootcamps can give people motivation to stay on track. Especially with frequent distractions, be it ADHD or a newborn at home, having set deadlines and curriculae can provide the necessary structure and accountability to finish.

I'm fairly skeptical of this, but some bootcamps come with job guarantees. Be sure to read reviews and do your due diligence. Having certain placement guarantees out of a bootcamp can be a nice insurance, as well as, a carrot to work towards.

Challenges you will likely face

As a student in a data science bootcamp, you'll be required to solve a variety of complex problems.

Some of these problems will be in the field of statistics, but a good portion of them will be in machine learning. You should be aware of some of the challenges that you'll likely face in the future as you learn more about data science and machine learning.

The first challenge is that you will be required to learn and understand a few underlying statistical concepts.

The other challenge is that you'll be required to learn how to program a computer to perform these calculations. Most bootcamps focus on the application part though, so make sure to choose the right one for your liking.

These are quite doable, as most of the concepts in data science are intuitive if taught appropriately. Especially when we're focusing on the application side of data science and machine learning.

The main challenge I see is motivation. Committing to a year of highly engaging education can be exhausting, especially with other commitments. The best way to approach this is to have a good support system and build awareness to possibly counteract course burnout.

How to choose the right boot camp?

This may vary, but in my opinion the main factors in deciding on a bootcamp are length, pre-requisites and curriculum, and price.

Additional benefits like online-only and job guarantees can be influence your, however, the main focus should be on

  1. What you learn.
  2. How long it will take.
  3. What it will cost you

It's also good to have a few favourites as some bootcamps can be competitive to get accepted. Having a few choices is better than missing out on the cohort once you have decided to make the jump into data science.

Generally, you'll want a data science bootcamp to cover the software fundamentals & programming, experiment design, machine learning, data visualization, and project work. This is the design I followed in my Skillshare course.

How to get into a bootcamp

The application process varies from school to school, but in general there are three steps:

  1. a pre-screening application,
  2. a coding challenge,
  3. an interview

Each step requires more and more time so it's important to know what your expectations are and if you are willing to put in the work. In this post, we will cover the top three challenges and how to prepare for them.

Conclusion: Data Science Bootcamps can be a great way to make a career pivot into the world of Data Science

Data bootcamp as a career path has been growing in popularity for a number of years. There are a number of reasons why you might be interested in learning data science. The demand for data scientists is very high, so it could be a great career move.

If you're interested in pursuing a career in data science, a bootcamp can be a great way to get you started. Learning how to code can be a challenge, but with a little guidance, the process can be a lot easier. We hope this post has helped shed some light on the data science bootcamp process.

Thank you for reading, and good luck in your data science journey!

Frequently Asked Questions

  1. What's your biggest motivation for becoming a data scientist?

    Data science is not just fun, but also challenging. A role as a data scientist is to always think about what is the best way to use the data we have collected to solve customer problem and guide decision making. I have not seen a better way to use a varied skill set to help a business gain an edge over their competitors. The advantages of a data driven approach, the ROI is worth it. Additionally, every day you'll work on something new and interesting.

  2. What are some of the most popular data science libraries/packages?

    R is one of the most popular data science libraries and is used by statisticians and data miners to perform statistical analysis and create graphs. In R the tidyverse is very popular. Python leans more on the data science and machine learning side. In Python libraries like Pandas for data wrangling, seaborn for visualization, numpy for numeric computation, and scikit-learn for machine learning are popular. Moreover, Pytorch and Tensorflow are used for deep learning.

  3. What kind of degree is needed to become a data scientist?

    The degree required to become a data scientist is an advanced degree in Mathematics/Statistics or Computer Science, along with several years of industry experience. Typically, data scientists have a master's degree in Mathematics/Statistics or Computer Science, and several years of experience in a related field.

    Nevertheless, in light of the new and upcoming demand for these kinds of jobs, many universities are now offering master's degrees in statistics or data science. Earning a master's degree can allow you to land better paying jobs.

    Moreover, data science bootcamps and online MOOCs and professional certificates can be a way to break into the field of data science.

  4. If you were a mother who wanted to be a data scientist, would you still invest in a data science bootcamp?

    If I had the necessary money and thought I could stem the additional load of an education, I would invest in a bootcamp. Bootcamps provide a great place for you to learn all the necessary skills you need to be successful in the data science field. However, most bootcamps do not teach you the soft skills that you will need to succeed in the data science field. Some examples of these soft skills include: how to communicate effectively, how to manage a team, how to negotiate a deal. It can be an incredible way especially for highly educated young mothers to stay engaged mentally during their share of the parental time.

  5. What is an alternative to bootcamps?

    I would suggest you to start with data science basics like R or Python and after you build a foundation, you can take a course for a real-world application. If you want to have some fun learning data science, you can take one of the online course on Coursera or Edx. You can pursue different aspects like data science for marketing, data science for social good, or data science for business.

  6. Do you recommend the deferred payment / Income Share Agreement (ISA) bootcamps?

    No. In theory, they sound nice because you don't have a lot of cash on hand when starting out or changing a career. However, there have been cases where the school's curriculum wasn't great and the students couldn't leave because essentially they entered a loan agreement. Be very careful with these.

  7. How do you gauge the quality of a boot camp?

    This is one of the toughest questions. Essentially you will have to do your own due diligence. Testimony is often faked these days. Survivorship bias skews the results of finisher and job statistics. Essentially, I look for transparency. The more transparent an organisation is, the better you can evaluate if the knowledge you will gain from the course is worth the cost and time investment.