In 2012, the Harvard Business Review dubbed the role of a data scientist as the “sexiest job of the 21st Century”. In the years that followed, the clamor for data scientists has grown at a tremendous scale. So loud is the demand, in fact, that working as a data scientist remains to be one of the most sought-after roles, topping LinkedIn’s Emerging Jobs reports for three years running. As LinkedIn co-founder Allen Blue so poignantly put it: “Data scientists are almost all already employed. There are very few data scientists out there passing out their resumes because they are so in demand.”
This begs the question: What’s behind the boom?
In a space of a minute, tons of data is generated. By some estimate, the amount of data expected to be “created, captured, copied, and consumed” in 2020 alone stands at 59 zettabytes. For those struggling to make sense of this–by which I mean everyone–data science and analytics news portal Datanami provides a simple visualization: “A large hard drive for a personal computer might have a terabyte of storage capacity. Now, imagine filling one of those with data every day for around 161 million years” and you have an idea of this year’s data stockpile.
This sheer quantity has propelled data to be at the forefront of the digital economy. Indeed, data scientists have become the lifeline of almost every industry today, with companies looking to transform their data in a way that gives them a competitive edge.
A Strong Field
Effective data scientists are those who extract actionable insights from lakes of continuously streaming data. They are, in a nutshell, data wranglers and problem solvers.
The demanding nature of this role is why it is well remunerated. In keeping with previous years, data scientists continue to exceed others in analytics in terms of compensation. According to Glassdoor, data scientists’ salaries in the United States boast between $83,000 on the low end and $154,000 on the high end, recording an average base pay of $113,309 across all levels.
The influence that data science wields in business operations has allowed it to remain immune to recent massive disruption across the US workforce. Case in point: while employment in other industries has fallen due to the onslaught of the Covid-19 pandemic, data science and analytics organizations are holding steady as 50% of which have either increased hiring (7.6%) or suffered no impacts (42.1%).
In terms of job satisfaction, negative sentiments appear to be the exception rather than the rule. Data scientists early this year came in with an overall job satisfaction rating of 4.0 on a scale of 1 to 5, one of the highest across the board.
There’s just one downside: the “datafication” of everything has led to a huge disparity in demand and supply, where the number of jobs that need to be filled is far outstripping the number of people who have the skills to fill them.
Galvanize: Providing Byte-Sized Learning
Galvanize needs no introduction. Backed by strong faculty, curriculum, and pedagogy, its coding programs have been hailed by independent review companies as one of the leading platforms for learning data science and software engineering. This recognition has catapulted Galvanize to becoming a reputable name in the industry.
Galvanize was born out of the desire to deliver needs that other platforms meet poorly. Over the years, it has managed to bring together the finer points of universities and MOOCs with students having the option to attend programs at their preferred method and schedule, all while being presented with an array of financing options.
Let’s break that down.
Galvanize currently has four full-time and two part-time courses in immersive software engineering and data science in its roster, all of which are accessible onsite or remotely and last between 12 and 36 weeks.
The quality and flexibility of the Galvanize curricula are enhanced by its state-of-the-art conferencing software that allows students to have regular consultations with the industry’s leading instructors. Its pair programming initiative also fuels collaboration with highly motivated individuals who come from different backgrounds and experiences.
Earlier this year, Galvanize moved to convert its upcoming in-person courses to live online instruction and delivery due to the staggering impact of the coronavirus pandemic. The decision was premised on the goal to help students move forward with their personal and professional goals irrespective of the circumstances.
Galvanize’s continued show of accessibility and adaptability has translated to a high conversion rate. Since its inception in 2012, Galvanize has established itself with a growing alumni base of over 8,000, all of whom have gone on to deliver a competitive edge to more than 2,250 companies while earning a median base salary of $90,000.
In keeping with its streak of receptivity to students’ needs, Galvanize is set to roll out a new part-time and immersive data science program this October.
Unlocking a New Data Science Cohort
Perhaps the best way to encapsulate the motivation behind this new program comes from Galvanize CEO Harsh Patel himself who said: “Time, location, and cost shouldn’t force anyone to sideline their career goals. By offering this program remotely and on a part-time basis, our hope is that anyone who wants to keep working while learning data science will see the possibilities our program can unlock.”
Program Lead Morgan Abbitt, in an interview with Career Karma, said the 30-week course was created after Galvanize noticed “a huge interest for a part-time course by individuals who want to make a career switch in data science but don’t really have the luxury of quitting their jobs or giving up other commitments”. Among them are veterans, full-time employees–especially working parents and employer-funded analysts looking to upskill–and other underrepresented groups in the tech industry.
While this opens the door for people of various backgrounds, Morgan said applicants are expected to have a working knowledge of probability and statistics and a comfortable intermediate-level understanding of Python to get through to the admissions process.
Upon submission of the application, two things will take place asynchronously: a coding challenge and an interview, both of which are geared to measure the applicant’s knowledge on the technical scale. Applicants are then given feedback and, if they failed to make it through, will be given another chance to apply.
Despite being a part-time program, the course doesn’t hold out on comprehensiveness and depth. It will still bear the same content as the full-time immersive course but with a more flexible schedule.
The curriculum is divided into four parts, covering Python, best practices in probabilistic and statistical analysis (including frequentists and Bayesian methods), current and relevant machine-learning algorithms, and the fundamentals of data science (such as recommender systems, neural networks, and time series) seen both in theory and in practice. The course will culminate with several group case studies and three capstone projects, each uniquely designed to showcase the students’ interests and aspirations.
This was perfectly demonstrated by Morgan who, interestingly enough, was also a former student of Galvanize’s full-time immersive data science program. During the final leg of the course, Morgan fused her interest in sports and data science by building a model, NFL 4th Down Analytics, which advances point optimization by taking in NFL play-by-play data and predicting whether a team will punt, kick, or kick a field goal on the fourth down.
These projects can also serve as a portfolio during the job application process that will follow upon completion of the course. While this is by no means a requirement, having a record of accomplishments can help make the case for the applicant’s suitability for the position.
Financial support for students of any Galvanize program comes in many forms, among which are Income-Share Agreements, loans, sponsorships, scholarships, and veteran benefits. Although the last option has not been made available for the new part-time immersive data science program, additional financing methods are now offered on the table. Last year, the human resource professional association SHRM found that 56% of employers offer tuition assistance for employees pursuing further education while student loan repayment assistance doubled since 2018 from 4% to 8%.
Meanwhile, Galvanize during the second half of this year announced a new structure for its ISA, in a bid to create one of the most student-friendly financing models during these challenging times. The changes include a higher minimum income threshold of $60,000 and a lower repayment cap at $25,000. This means that students, after paying a $2,000 deposit upfront, can defer their tuition until after they start earning $60,000 annually and will pay no more than $25,000.
The Upskilling Imperative
Over the years, we have witnessed a big wave of data change the way we live and upend industries on a global scale. What was once cordoned off to the IT domain has since reached even the hands of the common user. With a pandemic and corresponding recession fast-tracking the adoption of data science and machine learning, the labor market has become ripe with opportunities for individuals looking to upskill or jumpstart their careers in tech.
Eight years ago, the Harvard Business Review named a data scientist career as the sexiest job of the decade, advancing it as a crucial player in a rapidly-changing economic landscape. “Think of big data as an epic wave gathering now, starting to crest. If you want to catch it, you need people who can surf,” it said at the time. In the same year, Galvanize was created. Its timely entry into the playing field has allowed professionals to thrive in the digital economy. Now, it’s forging yet another path for those who have the same goal but are in need of more flexibility. If you’re one of them, visit Galvanize and get on board.
About us: Career Karma is a platform designed to help job seekers find, research, and connect with job training programs to advance their careers. Read more