Data science is a new buzzword in the tech world, promising high pay and great job growth. But what exactly is data science? Is it a good career choice?
Here’s what we’ll cover:
Data scientists study where information comes from, how data fits together to tell meaningful stories, and what those patterns signify for business outcomes.
On a day-to-day basis, this means creating econometric and statistical models for projections, clustering, classification, simulations, and other purposes, predicting user behavior through rigorous data analysis and pattern/trend identification, and conveying insights through data summarization and visualization.
With 650% growth in jobs since 2012, the data science industry is on the rise, leading many people to wonder, “Is data science a good career for me?”
When considering the switch to a new career field, people often want to know if it’s worth it to put in the extra time and effort studying, honing new skills, and preparing for interviews. Fortunately, the answer for many is a resounding yes!
Glassdoor labeled “data science” as the third most desired career in America, with a median data scientist salary of $108,000. The Bureau of Labor Statistics (BLS) lists the median salary for all US workers at $49,800, meaning data science salaries are over double the national average.
In 2019, LinkedIn ranked data science as the top most promising job in the US and reported a 56% increase in job openings.
According to a recent survey, COVID-19 has not slowed data science opportunities either: 50% of analytics and data science organizations have suffered no impacts (42.1%) or have actually grown in size (7.6%) during the pandemic.
The demand for data science links back to more companies ramping up big data, Internet of Things (IoT), and cybersecurity efforts and small-to-medium-sized enterprises also expanding their data analytics capabilities.
Data science is definitely intellectually rigorous and can have a steep learning curve. It may be coined by Harvard Business Review as the sexiest job of the 21st century but it’s not all glamorous—there is a lot of time spent cleaning the data, importing large datasets, building databases, and maintaining dashboards. To thrive as a data scientist, you should enjoy quantitative fields and be passionate about helping companies make more data-driven decisions.
According to LinkedIn, SQL is the most commonly requested skill in data science jobs, with Hadoop and Spark also rising in popularity. You will probably need to learn a programming language like R, SAS, or Python; brush up on mathematics with a focus on statistics and probability, linear algebra, and multivariate calculus; and learn some data visualization tools like Tableau. It is recommended to learn coding from scratch, as even a parameter change can disrupt results and there’s little margin for error. As you progress as a data scientist, you may specialize in machine learning algorithms, deep learning, and natural language processing, among other related fields that deal with big data and unstructured data.
Successful data scientists should also be well-rounded with soft skills, like interpersonal skills, communication skills, teamwork, and storytelling. Often, these skills cannot be taught in a textbook and only be developed on the job by collaborating with stakeholders across business, product, and tech teams.
Each step of the data science process can be challenging. First, companies need to acquire the right data from different internal and external sources (e.g. credit card transactions, weather data, order history, competitor intelligence) and make sure it is structured in a legible format. Once the data is queryable, you then have to build complex models and algorithms to extract meaningful insights and convey them in a way that answers key business questions and influences stakeholders.
This article provides a solid overview of the top in-demand data science skills in 2021.
Although data science can require advanced domain knowledge and many job qualifications do mention post-graduate education, you don’t need a master’s degree to break in as an entry-level data scientist. There are many online courses, workshops, bootcamps, and books available, so it is definitely possible to acquire technical skills on your own (link to data science bootcamp article), especially if you come from a software engineering background or hold a bachelor’s degree in another STEM field.
Those interested in data manipulation and computation may also want to explore the work of data analysts, data architects, business intelligence analysts, machine learning engineers, and similar job titles when searching for opportunities.
Most full-time jobs require 40 hours or more of work, but many freelance positions allow you to set your own hours and schedule. If you go over team expectations beforehand, it is definitely possible to maintain work-life balance as a data scientist.
The supply of data science postings grew by 31% over the past few years, while data science job searches only rose by 14% over the same period. This high demand has led to a shortage of over 150,000 data science professionals.
Given the supply and demand imbalance and the skyrocketing growth of the field, the data science market is one of the hottest for jobseekers, with many companies continuing to hire under uncertain market conditions.
Data science expertise is highly sought-after because it leads to tangible and measurable business outcomes. As stated in Harvard Business Review, “Companies in the top third of their industry in the use of data-driven decision making were, on average, 5% more productive and 6% more profitable than their competitors.”
A data science career path is not for the faint of heart, but sure to satiate the curiosity of those who value continuous learning and a growth mindset.
Is data science the right career for you?
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Check out Springboard’s Data Science Career Track to see if you qualify.
Not quite ready to dive into a data science bootcamp?
Springboard now offers a Data Science Prep Course, where you can learn the foundational coding and statistics skills needed to start your career in data science.
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