Approaching a Career Change with a Growth Mindset
Approaching a Career Change with a Growth Mindset
Data science is everywhere. It delivers your favorite thai food from across town within 30 minutes. It detects credit card fraud when you lose your wallet. It helps your doctor make a difficult diagnosis. And it unlocks your phone with just a glance.
What emerged as a new field just a few years ago is now fundamental to our daily lives. So, it’s no wonder why data science has become one of the most sought-after roles in today’s employment landscape.
As a professional domain, data science integrates mathematics, applied statistics, computer science, and complex systems to help guide operational strategies and fuel growth. Data scientists break down big data to create software algorithms, support machine learning, and inform decision-making. And if that wasn’t enough to pique your interest, Harvard Business Review boldly declared data science the “sexiest job of the 21st century.”
There’s no doubt data science is a hot career – but is it the right path for you?
According to the U.S. Bureau of Labor Statistics, data science is one of the fastest-growing careers in the country, projected to grow by more than 30% over the next decade. Data science has also topped LinkedIn’s Emerging Jobs Report for three years running, with the job title “Machine Intelligence Specialist” growing an incredible 74% over the past four years. Guy Berger, Principal Economist at LinkedIn, even noted in the 2020 report that “artificial intelligence and data science roles continue to proliferate across nearly every industry.”
Today, data scientists are employed at early-stage startups, Fortune 500 companies, government and nonprofit organizations, and virtually anywhere in between. Numerous activities fall within the umbrella of ‘data science,’ from business analytics to database maintenance to machine learning. These roles are amplifying (and, in some cases, replacing) traditional jobs across industries like insurance and government, taking on big initiatives from cyber security to statistical projections.
Wondering why data science has such staying power? It helps us make smarter decisions, build better products, and provide more value for customers, users, and partners. Data science has deservedly carved out a (large!) niche as a core business function, and it’s sticking around for good.
If you’re considering a career in the expansive fields of analytics or engineering, data science might be an enticing path. But before you dive into an intensive learning program, it’s important to understand the full scope of the job and ensure the role fits with your goals and interests.
Here are just a few of the pros and cons of data science to keep in mind when considering your future career:
Pros of Data Science:
With thousands of new jobs projected within the next few years (according to the U.S. Bureau of Labor Statistics), the data science field is rife with opportunity for newcomers. Despite an increase in college degree offerings, online intensive programs, and turnkey bootcamps, the supply of data scientists can’t keep up with the growing demand. As a job-seeker, you’ll have a leg up.
As reported by National Occupational Employment and Wage estimates, the mean annual salary for a data scientist in 2019 was $100,560 – with wages in states like New York and California reaching averages of $125,000 per year. If it fits with your skill set, interests, and aspirations, data science can be a very lucrative career path.
These positions are often indispensable for companies. For instance, a recent study by staffing firm Burtch Works found that data science jobs were immune to the impacts of Covid-19. Despite layoffs across many industries and job functions, data scientists remained the exception — with nearly 8% of study respondents reporting that their team actually increased hiring due to the pandemic.
With artificial intelligence, robotics, and big data fueling every industry from auto manufacturing to medical devices, data scientists’ work is helping to build our future in a meaningful way. Data science is tackling childhood obesity, building self-driving cars, and empowering entrepreneurs to take big risks — and you can be part of it.
Calling all WFH fans! Previously filed under ‘unique job perks,’ today an increasing number of companies are opening their roles to remote candidates, particularly for in-demand, computer-based jobs like data scientists.
Cons of Data Science:
Data science roles have grown more than 650% since 2012, but not all of those roles look the same. Despite the field’s exponential growth in recent years, there’s still ambiguity about what exactly your role will look like if you become a data scientist. If vagueness scares you, this could be a concern.
Just as the possibilities in data science are endless, so are the complexities. If you choose to become a data scientist, you’ll have a wealth of knowledge to absorb, including a variety of coding languages, mathematics theory, and data modeling. It’s a difficult journey, so you’ll have to be all-in.
Data laws and regulations are constantly evolving, which can pose unique challenges for data scientists. With huge growth in the collection of personal data, it’s imperative that data scientists have a strong understanding of governance and best practices to handle data responsibly and securely.
Excited about those ‘pros’ and not too worried about the ‘cons’? Great! A career in data science might be right up your alley. Now, let’s talk skills.
Data science relies heavily on statistics, computer science, and critical analysis, requiring a varied and sharpened skill set to succeed. At a bare minimum, you’ll need to be comfortable in the digital world and have an inquisitive mindset.
To become a data scientist, we highly recommend honing the following skills:
You’ll need a robust toolkit of hard skills, including an expert understanding of the core languages used in data science. According to Stack Overflow, Python is the fastest growing programming language worldwide, and one that’s essential in this role. Other important languages for data scientists include SQL, Apache, and R.
Many of your colleagues won’t speak your language — literally — so you’ll need keen interpersonal skills to communicate complex concepts to your teammates or external stakeholders. Being able to craft a narrative from data and tell that story in a persuasive, compelling way is an essential skill for data scientists.
Perhaps one of the most important skills for data scientists is being able to thoughtfully analyze large sets of data and pull out key insights. You’ll need the ability to identify common threads, make connections, and extract value from distinct data sets. Statistical analysis, data mining, and data visualization skills will all be critical to your success.
Of course, this list isn’t exhaustive of the tools and know-how you’ll need to become a data scientist, but it’s a great place to start. Aside from hard and soft skills, you should have a healthy dose of curiosity and the ability to adapt quickly — data science is an intricate, ever-changing field. It’s not for everyone, but, if this all sounds interesting, it might be for you.
You’ve now considered the pros and cons of data science, uncovered some of the advanced skills you’ll need, and answered the quasi-existential question of “why data science?” If you’re ready to take the next step to become a data scientist, you have a challenging but exciting path ahead of you.
Lambda School offers live online programs for students ready to get serious about a career in tech. We’re not a bootcamp, and we’re not a university program. Our hands-on curriculum builds deep knowledge at a fraction of the cost of a four-year degree, helping committed students gain the skills they need to get hired.
Learn more about our intensive data science course to determine if Lambda School is the right fit for you.
