Your Complete Guide for Data Science Bootcamp I

Your Complete Guide for Data Science Bootcamp I

from both a student and a program manager's perspective

*Disclaimer: Everything I said is substantiated by my opinion. Everying you see is a perspective.*

I will use a series of posts to share you my experience and take on regarding data science bootcamps and the topics I planned to cover will help to answer questions such as

what is a data science bootcamp?

who should consider taking the bootcamp?

How can you get the most from the bootcamp?

If you are a potential bootcamp student, this blog will give you a comprehensive view of the data science bootcamp and subsequently save you some time from doing research and trying to find out if the decision is right for you.

If you are a recruiter or my supportive friends and family, this helps you understand what I went through and learned in my past professional experience. Things are moving fast in the real world, and especially in the work environment, you always react on what has just happened or is happening. Reflection helps me to grow, and it is like rewatching a movie and the second time you watch it, you focus on the details and discover hidden plot lines.

Background

My first full-time job was with WeCloudData, a company known for offering the best part-time, full-time data science, and AI courses in Canada. I held different roles during my time at this company and this part of my career had given me a huge impact till this day. I will talk more about my experience with the company in my future posts.

I had a memorable experience when I was in charge of the sales department and part of my job includes providing program and career consultations to prospective and registered full-time students. Later, I decided to take the course myself which allowed me to gain a new perspective.

This is the first post in the series and I will give you an introduction to the data science bootcamp and walk you through the important elements of a data science bootcamp.

Overview

We call the full-time data science program: "Data Science Immersive Bootcamp" or just "bootcamp" for short. Just like every other data science or coding bootcamps out there in the market, the design philosophy was to provide an accelerated learning environment for individuals who want to learn the essential knowledge and skills to become a hirable candidate in the job market. From here, you can probably tell that the main target customers are those who are aiming to get relevant jobs in the field such as recent graduates and career-switchers.

The curriculum was very intense by design and as a student, you were expected to learn various topics within a short period. To give you an example, the bootcamp I attended last for about thirteen weeks. Students are taught *Python* programming and database query language *SQL* in the first two weeks and spend the next one to two weeks on intermediate to advanced *data manipulations* using Python. Typically, the following five weeks are about *machine learning*, and the last two to three weeks are about *big data* and *cloud*.

Why are students expected to learn this much and within a short period? First of all, those materials covered in the bootcamp, although, may not be entirely demanded by the hiring companies depending on the roles you applied to but are suppose to make you competitive in the job market. Secondly, there are no univerisal agreed requirements on hiring data scientists, and it usually depends on the expectation of hiring team/company and how technologically advanced they are.

Timing-wise, the bootcamp lasts about 13 weeks and it is just enough to cover all the contents and get your foot in the door. Students are expected to spend even longer time after the completion of the bootcamp to get their skills polished and to get advanced in certain areas.

Contents

If you are considering a bootcamp, the first thing you want to check is to make sure that the curriculum used by the bootcamp is both well suited for your need as well as industry demands. A bad scenario is when you have never done any computer programming in your life, but the curriculum assumes you have some prior experience in computer programming, and everything is built on top of this.

In the case of WeCloudData, the detailed content covered in the bootcamp is designed by field experts and is constantly updating to incorporate new business use-cases and changes in market demand. The curriculum works best for people with some prior knowledge in database language SQL, and some prior coding experience in a programming language like Python. If neither requirement is satisfied when you coming in, you are advised to take some prerequisite courses before taking the bootcamp to build up your foundations.

During my time as a program manager, I constantly hear students have discussions about the learning materials. There was always a mixture of opinions, and depending on your background (I will introduce later), you might found them to be easy or difficult. But everyone knows that the content covered is extremely useful and by sticking to the course content, it saves a ton of time on finding and switching between the right study materials.

Learning Curve

Before I joined the bootcamp as a student, I have seen the journeys of many bootcamp students from the first day they signed up till the day they got an offer. Although individual experience varies the majority of students went through ups and downs during the process of learning or/and getting the desired offer.

I remembered our CEO demonstrated the learning curve of an average bootcamp student to the prospective bootcamp candidates. The shape of the curve is close to a high degree polynomial function, which there are minimums and maximums which represents lows and highs of a student during the bootcamp period. Not everyone follows the same pattern, but everyone is expected to face different challenges earlier or later, no matter how good you are and what background you come from.

I hold a strong belief that everyone from any background can benefit from taking the bootcamp if your goal is to use the most effective approach to land a career into this field and as long as you do you research and understand what you are truly signing up for, you will be fine.

Students

I will use the next three short paragraphs to introduce three types of student you will likely to share class with if you decide to enrol. Thinking about yourself, which group do you belong?

Why should you know about this? Well, part of the reason people choose to enrol a bootcamp is that it offers face-to-face classes and you get to learn and work together with like-minded individuals. Therefore, whenever you run into questions, you can ask for help from your instructors as well as your peers.

Besides, for your next job, there is a high chance of you working in a team with members of various backgrounds. It is crucial to strengthen your communication skills and teamwork in a diverse environment.

1. Graduate Students

We always have knowledgable graduate students who studied the relevant subjects in the past and was here to fill in gaps and to get hands-on project experience. Those students are probably sailing the entire time of learning but having difficulties relating complex data problems to actual business issues and explain it in simple language to the others. Some of them are struggling to find jobs that meet their pre-set expectations.

2. Professionals

On the other hand, we have seasoned business veterans who were the subject matter expert in their domains. In this case, they are here to expand their skill-sets to level up in their field. Depending on what subject they studied back in the school, some of them would have some problems going through either Math/Stats or the coding part of the bootcamp. However, they often have a crystal clear goal of what they want to achieve. They knew to focus on the most important parts at different phases of the bootcamp and made sure they can tell a story about the work they did.

3. New Graduates

Lastly, we have our recent graduates from universities with bachelors or masters degree and with zero to a handful of work experiences. Most of them are here to learn the hands-on skills to kickstart their career in this field. A lot of these students tried to apply for relevant data jobs themselves and then realized that the gaps between the academia and industry are keeping them away from landing offers.

This type of student has a unique advantage over others. They have not graduated for a long time, thus they are still used to and capable of learning new things in full-time. The bootcamp is nothing but an additional full-time semester to them. While others, sometimes, having a long period of adjustment to this full-time learning schedule.

Highlight

Till now, you probably have a high-level understanding of the students in the bootcamp. How you can get the most from the bootcamp not only comes from the bootcamp itself and also from those that are around you. Learn from each other, and the learning will be more effective.

Job Assistance

While most bootcamps in the market now claim to offer some kinds of job assistance upon student graduation from the bootcamp. Some of those career services include resume critique, mock interviews, access to alumni network and industry network...etc.

The career service is offered in different forms both online and in-person by either the staffs or the instructors from the organization. But in my opinion, how much can a student truly benefit from those services is largely dependent on the following factor:

How long do those services last after graduation?

Job hunting takes time. In most cases, a candidate has to go through at least 2 rounds of interviews to receive the final hiring decision. In this field, we have seen candidates go through 4 or even 5 rounds of interviews and because of that, it is quite common that a hiring process takes up one month to complete.

Therefore, as a student, it is vital to not only consider the variety of the services offered but also the length of the services available, especially when you project yourself to spend extra time to get ready for jobs.

Instructors

The ability of the instructor to keep students engaged throughout learning in the bootcamp and to make complex ideas easy to understand for everyone is the key to drive student success.

To me, it is always about how well the instructor can cover the materials and connect with students rather than how experienced and knowledgable he is in this field. Remember, you are not going to be at their level overnight, and at this stage, the teaching should help to cultivate your interests in the field but not killing it.

It is probably a good idea to find out who is going to teach the bootcamp before you make the decision. Essentially, you are paying for the instructor to teach you the knowledge and skills in a way that you can accept. Otherwise, the knowledge itself should be free. Moreover, you cannot compare learning from a university or a college to learning from a bootcamp, wherein the former case, students sometimes choose to avoid classes and study by themselves to avoid unfavorable instructors. In comparison, you must try your best to attend every class and learn from every class.

Costs

The cost of attending a data science bootcamp is not simply the tuitions and fees that you pay to the service provider. You must include the opportunity cost into your thinking process. The time and money you could spend on something else rather than the bootcamp.

From my best knowledge, the tuition for attending a full-time data science bootcamp falls between $8000 - $16000. Usually, there are some kinds of financial aid offer to students who are in need. But still, it is a lot of money, considering you have already invested in formal educations and some of us probably still carry outstanding loans.

But keep in mind, the bootcamp allows you to fill in your knowledge and skills gaps efficiently and systematically. If you do it right, the return on investment is a promising career in an up-rising field.

Is bootcamp the right choice for me?

Finally, we are here. After learning different elements of a data science bootcamp, now you are probably wondering if it is the right choice for you to take the bootcamp.

The answer is always yes and no, what works for others may not work for you. At least, you should do some research and talk to someone who works at the company. You should tell the advisor about your situation and listen to what he or she suggests.

Moreover, you can talk to someone who went through this path and preferably someone with a similar background as you. Afterwards, combine all your search results to support your decision making.

Other Learning Options

There are different places where you can learn data science knowledge and skills, and based on the way these courses are delivered, we can divide them into two main categories.

  1. Unsupervised & Online: Learning from massive online materials such as from Youtube and other online learning platforms such as Coursera and edX.

  2. Supervised & Inclass: Lots of Universities and Colleges are expanding their curriculums to this field and we observe new data science certificate programs, and degree programs are springing up rapidly. In many cases, the company who offers the full-time bootcamp also has its part-time substitute. You will spend a long time to learn everything but the contents are almost the same as the bootcamp.

These are more options where you can learn data science, such as from your everyday works if you are already in the relevant fields.

Highlight

The most important thing for you to keep in mind when choosing the learning option is, you need to think about your goals (what do you plan to do with data science? Do you want to just learn it or get a relevant job?) and how long do you give yourself to achieve such goals.

What's next?

This marks the end of the post. For my next post regarding the data science bootcamp, I will share some tips on how to get through different parts of learning from the bootcamp curriculum by utilizing "external" resources.

Since I don't have a software nor a STEM background, I overcame a great deal of "challenges" to get to where I am now. Still, there is a long way ahead of me since data science is a broad field with many different specializations. I need to keep on learning and improving.

Link for part II