Why You Should Learn Big Data Through a Bootcamp
Big data is a buzzword for a reason. Over the last decade, data analytics has become an infrastructure necessity for businesses in every industry.
Naturally, the growing emphasis on big data has also created a significant need for data professionals. But taking advantage of this demand isn’t as simple as filling out an application and hoping for the best. If you want to build a career in data analytics, you’ll need a robust set of foundational analytical skills. There are several ways to learn these skills — but for many, the best way is via a data analytics bootcamp.
In this article, we’ll make a case for why you should learn big data through a bootcamp and how doing so can springboard your career.
Learning Big Data Analytics Can Be Hard
Generally speaking, big data courses aren’t designed for novices. Given the subject matter’s complexity, instructors generally assume that their students know their way around statistics, higher math and basic analytical principles. If you don’t happen to have the necessary context to guide you through the class, you could find yourself hopelessly confused by the time the first lecture ends.
But never fear; even as a data beginner, you have educational options. Data analytics bootcamps are designed to gently onboard relative beginners into the field and impart the practical, foundational skill set they’ll need to qualify for an entry-level professional role in just 24 weeks.
Learning big data analytics requires hard work — but the process doesn’t have to feel frustrating or impossible.
Big Data Professionals Are in Great Demand
To say that big data professionals are in demand is a little like saying the ocean is wet — it’s technically accurate, but it doesn’t quite capture the scope of the matter.
According to analysis from QuantHub, job listings experienced 37 percent year-over-year growth in 2019. Researchers also noted that there were three times as many job listings for data professionals as there were job searches. As of 2020, the data science field faced a talent shortfall of over 250,000 roles.
QuantHub researchers attribute the ever-increasing need for data professionals to several factors, including the growing popularity of data analytics among small businesses, the increasing potential of Internet of Things (IoT)-generated data and a general increase in digitized business models.
None of these factors seem likely to diminish in the near future. So, if you have a knack for math and an interest in analytics, there’s no better time to start building a career in big data! By attending a data-focused bootcamp, you’ll have the opportunity to upskill quickly and learn enough fundamental analytics principles to get your feet wet in an entry-level job and start growing in your career.
Data Bootcamp Graduates Are Qualified for These Jobs
Big data bootcamps are often designed to maximize a learner’s immediate career potential. These programs focus on imparting practical skills that will allow the student, upon graduation, to qualify for a variety of entry-level data jobs.
Given how in-demand data professionals are, it should be no surprise that analytical thinkers have an abundance of career options and potential roles available to them. Below, we’ve highlighted just a few jobs that bootcamp learners could qualify for once they finish their studies.
It’s intuitive; after attending a data analytics bootcamp, you can pursue a job as a data analyst.
These savvy professionals use complex mathematical and analytical methods to solve high-level problems and help their organizations make better, more informed strategic decisions.
According to the Bureau of Labor Statistics (BLS), data analysts are also in high demand; job openings for the profession are on track to grow 25 percent by 2029, which is much faster than average. For context — the typical rate of growth for all other professions is a mere four percent. It’s not hyperbolizing to say that data analysts have fantastic career potential.
These professionals are generally well-compensated for their work. However, the BLS suggests that those working in specific industries — namely, aerospace, finance and machinery manufacturing — tend to earn even more than their peers.
Like data analysts, business analysts are tasked with identifying and recommending ways for businesses to become more efficient. Specifically, they advise managers on how to increase revenue, lower costs and enhance profitability.
These professionals consider many data-based focal points such as employment structure, inventory control and internal company protocol. Business analysts may also conduct onsite observations of staff and equipment to form more robust insights.
Business analysts aren’t quite as in-demand as data analysts; BLS research indicates that job openings in the profession are only slated to rise 11 percent by 2029. However, this growth rate is still substantially higher than the average and indicates notable industry performance and potential. The highest-earning sectors for these professionals include oil and gas extraction, residential building construction and technical consulting.
Software engineers design, test and implement software based on user needs and expectations. They also ensure the long-term functionality of software through regular maintenance and analysis. While their role doesn’t hinge on data analysis like the positions mentioned previously, having analytical skills can be invaluable for those working in software engineering.
Software engineers are also in high demand, as evident through the role’s projected 22 percent growth for 2019–2029. Much of this demand comes from a growing need for new mobile applications, stronger corporate computer systems and generally intuitive digital interfaces — all of which have been underscored by an increasingly remote workforce.
Software engineering offers some of the highest wages mentioned thus far. That said, those working in industries such as software publishing and computer equipment manufacturing tend to out-earn others in their profession.
Entry-Level Data Scientists
The ongoing data scientist shortage has significantly changed the field’s growth trajectory, which now projects a staggering rate of 31 percent by 2029. Data scientists create and implement analytical techniques used to convert raw data into meaningful insights. They achieve this goal via the ongoing use of data-based programming languages and visualization software.
Other key parts of the role involve data mining, data modeling, machine learning and natural language processing. The profession’s blend of above-average pay and high job demand makes it one of the discipline’s most significant professional pathways.
That said, it is worth noting that those who want to pursue this role may need to obtain additional education or experience to supplement a data science bootcamp. Those who want to get into the field right away can complete a bootcamp, work to land a data analytics role, gain experience and/or further education and then gradually work their way up into a data scientist role.
Bootcamps Don’t Require Big Data Experience
The roles above involve high-level responsibilities rooted in complex data analysis, system construction and ongoing technological vigilance. Though these factors may seem intimidating at first, they are more than learnable through the right educational platform.
Enter the big data bootcamp — a leading way for beginners to establish and hone fundamental data skills. These programs tailor their instruction to suit a variety of experience levels, including those who have no prior data knowledge whatsoever. Leading bootcamps offer a data analytics curriculum rooted in practical, skills-based training that blends data fundamentals with their real-world applications.
Create a Comprehensive Portfolio of Complex Projects That Prove Your Data Skills
As a learner, you’ll have countless opportunities to construct projects for your professional portfolio. The best bootcamps are designed with this in mind; after all, giving learners the tools and materials they need to land their first job in big data is the core purpose of a bootcamp. Portfolios are critical to that process.
Here’s a quick rundown on what a portfolio is and why you should care. A portfolio is a comprehensive (but curated) collection of your best academic and personal projects. The pieces you include will confirm the skills you list on your resume and demonstrate to employers that you know your way around a data set.
As you attend a big data bootcamp, your instructor will set you up with an assignment that lends itself to portfolio creation. Such projects might include conducting data analysis to answer a research question, developing web visualization or compiling aggregations of data via dashboards or similar landing pages.
Broadly speaking, you should approach the portfolio as an extension of your professional resume, building it to validate your credentials while proving your ambition to help further the field as a whole.
The best portfolios are organized to showcase a variety of noteworthy skills in a clear, straightforward manner. The outlined projects should depict your genuine interest in big data — not just a list of checked boxes or “proof-of-concept” homework assignments. Settling for the latter is a quick way to devalue yourself in a pool of applicants, as most employers will prefer dependable, passionate innovators over candidates who just complete tasks.
Like a resume, your portfolio should be adaptable as you gain new interview experience and tailor your application for different roles. Use the projects you create during your big data classes as proof of your essential skills — as you learn and experience more, you can build on the foundation your portfolio provides.
You’ll Learn More Than Just the Basics
Though big data bootcamps are designed to benefit both beginners and fledgling coders alike, they are much more than basic introductory lessons.
Industry fundamentals are core to bootcamp instruction; they provide the framework for a layered, skills-oriented educational experience that favors hands-on learning and practical involvement over lectures. This hands-on process allows learners to gain required data analytics capabilities, flesh out their portfolio for stronger job applications and gain the confidence to enter one of the world’s most up-and-coming professional sectors.
Here are a few of the skills you can expect to learn from your data analytics bootcamp curriculum:
- Advanced Excel functions
- Fundamental statistics (i.e., modeling, forecasting)
- API interactions
- Database languages (i.e., SQL, NoSQL)
- Machine learning basics
You’ll be learning a lot — and by the time you finish your online data analytics course modules, you will be well-prepared for an entry-level career in data science.
The truth is, big data bootcamps don’t have all that many downsides — and the downsides that they might appear to present are usually readily fixable.
Consider cost as an example. According to Career Karma’s 2020 State of the Coding Bootcamp Market, the average tuition cost posed by a bootcamp is $13,293.
Now, at first glance, this might seem like a hefty sum. However, when you consider the value of what you’re getting — the chance to break into data analytics in just six months — it’s actually remarkably affordable. For context, a single year of college at the average in-state public college is $9,687. Private college tuition costs even more, at roughly $35,087 per year.
All told, finishing a four-year data science undergraduate degree would require you to pay between $38,748 and $140,348. Compared to those sums, a bootcamp is downright cheap.
Of course, you’re not getting the same experience or breadth of education; after all, there’s only so much a bootcamp can teach in a matter of months. However, for sheer accessibility and practicality, bootcamps are hard to beat. Students have the opportunity to learn at a low cost and pivot into the workforce in a few short months.
It’s worth noting, too, that a lot of bootcamp providers offer financing options and/or scholarships to make paying tuition more immediately manageable for students.
Another potential downside is the time commitment a bootcamp demands. Bootcamp students need to set aside several hours every week, for 24 weeks, for lecture and homework periods.
There’s no getting around this point. Reasonably speaking, every upskill requires you to sacrifice your time. Sure, you might rationalize that you could teach yourself through data analytics books and online tutorials — but if you aren’t internally motivated or have trouble holding yourself accountable, this process may not be nearly as efficient or effective as a bootcamp.
Moreover, most bootcamps are designed to be as flexible and accommodating as possible. Often, they come in part-time or virtual schedules; these big data analytics online courses allow professionals to balance their education with their professional or personal obligations.
So, are data science bootcamps worth it? Logic tells us that if you want to upskill quickly or on a budget, the answer is a resounding yes.
Bootcamps are accessible, affordable and effective. Anyone who wants to pivot into big data quickly should at least consider them as an academic path.
A big data bootcamp provides an excellent means to gain skills and earn credibility in a challenging but rewarding line of work. Graduates can confidently seek out jobs that they may have previously considered out of reach; approaching each application with a robust skill set and, in most cases, a polished portfolio.
Moreover, bootcamps are structured to meet industry demand by offering affordable, accelerated data instruction to aspiring professionals. This practical training empowers students to gain real-world experience in a collaborative classroom environment and prepare for professional roles.
If you want to take your first steps into this high-potential field, check out Berkeley Data Analytics Boot Camp! Our curriculum provides an immersive educational experience that will help you accelerate your career journey into data analysis.