Here’s what you need to know about becoming a data scientist:
Data Science Uses and Applications
Data science is now influencing almost every department within every industry. From product development to sales and marketing to government operations, data scientists are emerging as important new players within businesses and organizations across the world.
- Healthcare: Notre Dame’s Data Science faculty developer, Steven Buechler, applied data science to the field of molecular biology to improve the accuracy of diagnoses and to identify targeted therapies to help breast cancer patients.
- Finance: Kathryn O’Donnell, Director of Data Science at Capital One, uses her experience leading data science and analytics projects to develop new approaches to combat credit card fraud.
- Sports: Notre Dame’s Data Science faculty developer, Alan Huebner, is employing data science techniques, such as collecting and storing 3D-motion capture data from the training room and game statistics, in order to enhance student-athlete performance and well-being at Notre Dame.
- Data acquisition, collection, and storage
- Discovery and goal identification (ask the right questions)
- Access, ingest, and integrate data
- Processing and cleaning data (munging/wrangling)
- Initial data investigation and exploratory data analysis (EDA)
- Choosing one or more potential models and algorithms
- Apply data science methods and techniques (e.g., machine learning, statistical modeling, artificial intelligence)
- Measuring and improving results (understanding the data and its quirks through validation and tuning)
- Teaching and training others
- Organizing and guiding team projects
- Identifying business or organizational problems to be solved with analytics
- Communicating findings to non-technical decision-makers and people outside the company or organization
- Knowing their business and guiding their leaders
- Average: $139,840 PER YEAR
- Low: $101,000 PER YEAR
- High: $183,000 PER YEAR
- Average: $132,419 PER YEAR
- Pay Range: $45,000 – 265,000 PER YEAR
- Average: $132,419 PER YEAR
- Pay Range: $45,000 – 265,000 PER YEAR
- Apply critical thinking — not just techniques
- Understand the ethical implications of data science
- Communicate actionable insights that can be understood by a non-technical team
- Remain agile as the data science field evolves
Data Scientist vs. Data Analyst
While the definitions of these two data jobs are not set in stone and some of the day-to-day activities between data analysts and data scientists overlap, typically, the leadership skills for data scientists are different.
All of the technical and machine learning skills both data analysts and data scientists use are not truly meaningful if teams cannot gather value from these data sets, and that’s where a data scientist comes in.
The data analyst is typically more focused on the known, i.e., analyzing historical data from a single source, such as business analytics software, and less focused on communicating data to internal stakeholders.
The data scientist is tasked with using their powerful data visualization skills and shrewd business acumen to communicate findings from data sets to non-technical audiences. They must also help drive business strategy in an ethical manner.
Data Science Career Opportunities
“The demand for analytical data science talent in the U.S. is projected to be 50% to 60% greater than supply by 2018.” — McKinsey
When contemplating your career in data science, it’s important to consider the specific job opportunities available and the skills and competencies employers are seeking.
In a KDNuggets article, Alex Castrounis described the process of a data scientist as being similar to the scientific method process used by scientists.
Data scientists “ask questions and/or define a problem, collect and leverage data to come up with answers or solutions, test the solution to see if the problem is solved, and iterate as needed to improve on, or finalize the solution.” The article goes on to define the typical responsibilities employers are looking for.
8 typical data scientist job responsibilities include:
In addition to strong technical and quantitative skills listed in job responsibilities, top-performing data scientists need well-honed soft skills in communication and leadership. The O’Reilly Data Scientist Salary Survey describes the specific skills these data scientists must have.
5 skills top-performing data scientists possess:
What Data Science Leaders are Saying
Notre Dame alumna Laura Godlewski, a data scientist for Facebook, described the skills and qualifications she believes successful data scientists must have:
“We can’t be successful without data communication. Otherwise it’s just ‘so what?’ You can come up with all numbers, but nobody understands it unless someone can translate it. That is a key part of your role as a data scientist. You can’t go on gut instinct. You have to turn to your evidence.
Why do you want evidence? Because it is going to support your actual conclusion. Evidence supports your decisions. You need to understand how important qualitative interpretation is to data, as well as the quantitative. You can’t have one without the other, especially at Facebook.
Why? We look at all these behavioral metrics. I understand the ones from the U.S., but I may not understand the nuance of what’s going on in Europe, India, Indonesia, and you are called upon to draw that conclusion and form that hypothesis. It’s really important to understand that qualitative learning is as important as quantitative learning.”
In addition to taking an evidential approach to data science, data science leaders must learn thought processes — not just techniques.Notre Dame faculty member Dr. Alan Huebner described the critical thinking and communication skills required of today’s data scientists:
“Take the analogy of driving a car. For most of us, we just drive it. If something goes wrong, we can’t fix it. Likewise, to become a top performer in data science, you need to get ‘under the hood’ of the algorithms and statistical models.This will help you diagnose problems, explain counterintuitive results, and compare competing results. And you need to be able to explain the problem and the solution to customers in a way they can understand.”
WHAT IS THE TYPICAL DATA SCIENTIST SALARY?
Like any other profession, not all data scientists make the same amount. However, the list below compiles pay averages and ranges to help give you a better sense of the typical data scientist salary.
Here are typical data scientist salaries:
Here are two important salary trend to keep in mind…
1) According to the 2017 Data Scientist Salary Survey from O’Reilly, salary increased with more time spent in meetings:
Because employers are increasingly valuing data scientists who can take on more responsibility beyond their coding or technical duties. Businesses want data scientists who can also participate in meetings to communicate their findings and influence strategic decision making.
2) Based on Burtch Works 2018 Data Scientist Salary Report, data scientists made at least 22% more as compared other individual contributors in predictive analytics:
Where are data science jobs?
According to the LinkedIn Workforce Report, there’s a shortage for data science skills across the US. New York City, San Francisco and Los Angeles have the largest supply and demand gap, but nearly every major US city is experiencing a skills gap. This translates to strong hiring and more job opportunities for those with data science expertise.
Table: The intensification of local shortages for data science skills, July 2015 to July 2018
Data Scientist Education and Training
With the unprecedented need for skilled data scientists in every industry, there’s no shortage of online courses, bootcamps and YouTube videos available. However, as industry seeks more leaders in data science, a higher-level of thinking and education requirements are needed.
When it comes to hiring data scientists, strong technical skills are an obvious requirement. In fact, according to a Glassdoor survey on the most frequently mentioned skills in data science job postings, 9 out of every 10 job postings in the sample required at least Python, R, and/or SQL skills.
But as data-driven decision making becomes more prevalent across all industries, businesses and organizations are increasingly looking for data scientists who can:
You can take a MOOC or rely on YouTube videos and perhaps learn enough without a degree to get a data science job because of the high demand, but if you’re looking for long-term career growth in data science, that’s where a master’s program comes in.As it turns out…
69% of respondents from a Notre Dame information session said they would like to pursue a data science master’s degree to get the skills and training they need for long-term, future growth.
Why a Master’s in Data Science is Worth It
According to Burtch Works’ Must-Have Skills You Need to Become a Data Scientist, 91% of data scientists have a Master’s degree.
Because in a data science master’s program, you don’t just learn techniques. You learn thought processes.
“If you’ve just learned how to perform a set of tasks [for example] in a software package, when things change you have to go back and redo that and you don’t really understand why things do what they do. Data science leaders have to be able to synthesize and go beyond just the repetition of things. It’s higher-level learning where you can think about things more deeply. That’s one of those things a master’s degree is going to give you. You’re not just thinking about topics for a couple of weeks, you’re thinking about them continually and in a synchronized way.” — Roger Woodard, Director of Online Program in Data Science
Do I need a math degree?
While a math degree will certainly help, you do not need one to join our data science master’s program.How can you become a data scientist ready to succeed?
Notre Dame’s online data science master’s program gives you a strong grounding in the quantitative and technical skills essential for your career success in data science.
Through a multi-disciplinary curriculum, you develop broad analytical and quantitative thinking skills, expertise in storytelling with data, and a deep understanding of the ethical complexities of the field.