First coined by the financial industry in 2001, data science has evolved from assisting in business decisions to predicting user patterns and analyzing trends. But data science jobs are no longer limited to finance and tech. Today, data science is highly applicable to pretty much any industry – from healthcare to retail to manufacturing and music platforms and these jobs continue to evolve. Lanisa Farnsworth, Manager of Employer Relations at Lighthouse Labs, explains how data science is used across 7 different industry sectors!
Data science was coined in 2001 by the financial industry, the first industry to understand the advantages of sorting, analyzing, and using large amounts of data to help companies reduce their losses.
They typical tasks of today’s data scientists include:
Most Used Tools for Data Science
Generally, data scientists need to know:
Today, data science can be leveraged and capitalized on in any industry, so long as the specific challenges within that industry are understood as well as how data characteristics can match the market needs with capabilities and solutions. A data science career largely depends on the individual, their experiences, and their passions.
Data science tasks are largely the same across industries, but how data science is used in each industry varies. Here we offer some examples of how data science is used in: banking/finance, media/advertising, healthcare, government, manufacturing, tech startups, and retail.
The industry that first understood the power of data science, banking and finance typically use data science for risk analysis and risk management. Companies are looking for data scientists for portfolio management, to analyze financial trends through data using business intelligence tools.
Typical job titles: Data Scientist, Data Analyst, Business Data Analyst, Financial Data Analyst, Data Scientist - Financial Services, Statistical Analyst / Risk Analytics
Example of data science in banking/finance:
In marketing, media, and advertising, data science is used to leverage social media and mobile content in order to understand real-time media content usage patterns from people that use that media. Employers in this industry like to hire data scientists who come from a marketing background.
Typical job titles: Data Scientist, Data Analyst, Data Scientist - Product Management, Product Manager for Marketing Analytics, Data Analyst - Digital Advertising, Marketing Science, Data Science - Customer & Marketing Analytics, Data Analyst (Growth Marketing).
Examples of data science in marketing:
Healthcare is a major field using data science, mainly to improve health and patient care. Data science is used to get physicians the most comprehensive information on their patient's well-being and be able to provide a more actionable plan for their health.
Typical job titles: Data Scientist, Data Analyst, Clinical Data Scientist, Product Data Analyst
Examples of data science in healthcare:
Big data has many applications in the public service field. Government positions can include work in financial market analysis, health-related research, environment protection, energy exploration, and fraud detection.
Typical job titles: Business Data Analyst - Government & Public Sector, Data Scientist, Data Analyst, Government Research Data Analyst.
Examples of data science in government:
The manufacturing industry is an overlooked industry for data science application, but they use data science to boost their production system and revenue. Manufacturing also depends on data science to detect the number of products being manufactured.
Typical job titles: Quality Data Analyst, Systems Analyst, Machine Learning Engineer, Data Scientist, Data Analyst
Example of data science in manufacturing:
Entering a tech startup as a data scientist means wearing many hats. Tech startups use data science as a way to enable faster growth and compete within the marketplace. There is risk involved in a startup, but being able to refer to numbers can greatly mitigate those risks and ensure the proper return on investment (ROI) so the company can grow.
Typical job titles: Data Scientist, Data Analyst, Data Engineer, Machine Learning Engineer, Data Professional, Data Science Specialist, Data Analyst - Growth, Data Support Specialist, Data Product Specialist, Data Strategist.
Examples of data science in tech startups:
Data science in retail is similar to media/advertising/marketing because it relies on analyzing customer trends. The difference in retail is they focus on a specific market rather than a broad spectrum of customers. Data science in retail is focused on brand specifics and customer purchasing habits to enable brand growth.
Typical job titles: Data Scientist, Data Analyst, Analytics and Insights Manager, Analytics Lead, Business Intelligence Analyst, Merchandise Analytics, Commercial Analytics, Products Data Analyst/ Scientist.
Example of data science in retail:
There isn’t a “best” industry sector to get started in as a data scientist. This career is highly tailored to the individual, their background, their passions, and where they can leverage their career goals within data science. At Lighthouse Labs, the industries that recruit the most data science students are healthcare, tech startups, and a mix of retail and media industries.
Typical Data Science Salaries in Different Sectors
The national average salary in Canada for a data scientist with some experience is $110K annually. A data science salary depends on a few different factors, such as which industry a data scientist works in:
Salary also differs based on region, due to factors like the local cost of living, the current job market, and company size.
Graduates coming out of Lighthouse Labs tend to acquire Junior data science roles. An entry-level data science position on average starts around $65K, depending on the individual and their background pre-bootcamp. Some Lighthouse Labs graduates with previous experience or higher degrees (like Master’s or Ph.Ds) are able to elevate their starting salary. Even though they are starting on a junior level with their data science experience, these graduates will see starting salaries of $80K-85K.
Interviewing for Data Science Roles in Different Sectors
As long as you have the data science knowledge, skills, and tools required, there’s no reason why you can’t pivot into different industries as opportunities arise and interests change. Many companies are satisfied with the knowledge bootcamp grads possess, while other companies want that on top of a higher education degree.
More niche industries like healthcare or finance do seek people with those backgrounds. Financial industries look for bachelor’s degrees in mathematics, statistics, or accounting. Healthcare industries vary in their requirements of background; if someone has a great personality without a technical health background, they’re more open to considering them.
The data science curriculum at Lighthouse Labs is based on the newest trends. It’s a three-month intensive bootcamp that requires 12-hour days, 6-days a week. We ensure that bootcampers learn the fundamental and most industry-relevant tools. The tools bootcamp students learn enable them to be competitive candidates in the job market. Lighthouse Labs has a 97% success rate of placing graduates in jobs within 6 months of graduating!
Besides the actual data science curriculum, students and graduates have access to our career advisors at Lighthouse Labs. Our Careers Services team supports bootcampers with:
Jess is the Content Manager for Course Report as well as a writer and poet. As a lifelong learner, Jess is passionate about education, and loves learning and sharing content about tech bootcamps. Jess received a M.F.A. in Writing from the University of New Hampshire, and now lives in Brooklyn, NY.
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