Is data science stressful? Learn about the roles, skills, and responsibilities of a data scientist in this online guide.
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Data scientists leverage big data to generate insights for companies and organizations, helping them discover inefficiencies, customer pain points, and opportunities.
To become a data scientist, you’ll need a solid foundation of technical skills in math, statistics, programming languages like R and Python, as well as an aptitude for visualizing data using statistical and graphical techniques.
A data scientist is someone who extracts and interprets data to strengthen or align with a business’s overall goals. Data scientists are constantly “wrangling” (or “munging”) data from its raw state into a cleaner, more interpretable presentation.
Data scientists work in big data, machine learning, or AI companies. However, experience in these types of organizations is not required as far as what you need to be a data scientist.
Data science is an extremely broad field, entailing everything from cleaning data to deploying predictive models. It’s rare for a data scientist to do it all. Most data scientists specialize in a specific function within the data processing cycle.
Data scientists work in tandem with data analysts, data engineers, business intelligence specialists, and data architects to create and maintain databases, analyze data, and communicate business insights. Their job is to identify the data analytics problems that offer the greatest opportunities to the organization.
Data scientists earn an average salary of $123,390/year, according to Indeed. Salaries vary widely by city, commensurate with the cost of living.
The highest-paying cities include San Francisco ($159,217); New York City ($139,037); and San Diego ($136,767).
As for entry-level data scientists, they still earn a decent living at $104,995, per Glassdoor.
A typical data scientist job description might look something like this:
Learn more about a typical data scientist job description in this video.
Data scientists need a wide range of skills, from technical proficiency in programming languages and statistical software to communication and storytelling. Companies that are looking to hire data scientists will expect potential candidates to perform statistical analysis and help them uncover vast amounts of information to drive smarter decisions and deliver better products.
Data scientists need two main types of skill sets: technical and interpersonal.
To qualify for an entry-level data science role, you’ll need high proficiency in SQL, intermediate to good statistical/mathematical computing in R/Python, data visualization skills (Excel or Tableau is fine, but experience with more advanced statistical tools could help you stand out).
You’ll also need to demonstrate a knowledge of machine learning algorithms and when to apply them, as well as an understanding of traditional and Bayesian statistics.
In terms of soft skills, recruiters will want to see that you’re able to communicate complex data science concepts to non-technical stakeholders.
Recruiters also want to see examples of projects that demonstrate these different skill sets. For instance, an active Github profile could show off your coding skills, while reports, graphs, and other data visualizations in your portfolio indicate that you’re familiar with statistical modeling software. A website with an interactive table or chart shows you have both.
Below are a number of examples of data science projects by former Springboard students.
Machine-powered investing. For his capstone project, Springboard data science alum Paul Brume (2018) decided to investigate peer-to-peer lending services, which allow users to lend and borrow money outside of the traditional banking system. Specifically, he was interested in how these companies evaluate credit risk differently from traditional banks. He investigated several factors relating to the likelihood of loan default, such as:
For each factor, Paul made a series of plots to find relationships within the data. When he found a trend, he quantified the likelihood that the trend was not due to chance by using frequent statistics. Using the data models he identified, Paul built a supervised machine learning algorithm to predict whether an individual would likely default on their loan. Because of his work, Paul landed a job as data engineer at Synthego before he even graduated from Springboard.
Using predictive analytics to improve sales outcomes. Now a data scientist at Livongo, delivering key data insights to boost the company’s growth marketing efforts, Springboard alum Mikiko Bazeley decided to build a predictive model for the organization she was working for at the time. She used XGBoost, an open-source software library for applied machine learning, to classify the qualification of sales demo calls in order to improve pipeline forecasting.
"The capstone is a demonstration of the value that seemingly low-hanging fruit data science projects can have on business organizations like revenue operations," Bazeley said. "This is the kind of collaboration that can happen between data scientists and business partners."
Data scientists tend to be well trained in quantitative techniques, given the steep learning curve required to master such a wide range of skills. The most common fields of study are mathematics and statistics followed by computer science and engineering.
However, you can still become a data scientist even if you didn’t study one of these fields. In fact, in an analysis of thousands of data scientist resumes, Indeed found that data scientists had the widest variety of educational backgrounds compared with data engineers, data analysts, and software engineers. Only about 20% studied computer science, and another 20% earned a degree in business or economics. The rest came from math/stats, natural sciences, engineering, data science or other majors.
Data scientist roles require you to perform a number of key functions in different areas. Here are six of the most common responsibilities of a data scientist.
Data scientist roles are becoming increasingly specialized as sophisticated software automation takes over tasks like cleaning data and finding redundancies. Different types of data scientist roles focus on a specific part of the data processing cycle, from analyzing data to building the underlying data warehousing infrastructure.
Is data science the right career for you?
Springboard offers a comprehensive data science bootcamp. You’ll work with a one-on-one mentor to learn about data science, data wrangling, machine learning, and Python—and finish it all off with a portfolio-worthy capstone project.
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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