Data scientists are at the epicenter of managing, processing, and interpreting big data that informs effective decision-making across various businesses and industries. Data that any business collects – from customer preferences and behaviors, to supply chain inefficiencies – is a treasure trove of information. Leveraging this data is what data scientists do to improve processes, influence decisions, and increase efficiency that transforms business operations including marketing and growth strategy, finance and investments, tech innovations and more for the better.
To effectively leverage data science techniques and make big data actionable, a person must be equipped with a broad computational and math skillset. A day in the life of a data scientist includes using advanced computer science concepts, statistics, and other technical and soft skills -- and the work s/he does has the potential to become a company's competitive advantage. An effective data scientist also needs business acumen (or the intelligence to understand the industry and customer context of the data), strong analytical skills, programming skills, and the ability to clearly communicate plans, findings, and recommendations.
While core competencies are important for all Data scientist roles, the data itself partially influences what a Data scientist does. Labor market analytics firm, Emsi, used job postings data to identify "data science skills clusters" and how they vary across industries and markets. For example:
- New York City: Beyond the core data science skills clusters of data and business analytics, software/web apps, and business intelligence, Data scientists in the Tri-state area are expected to develop Machine Learning systems that use big data continuously and at scale. This need is driven largely by Product, Investments and Underwriting teams in Fintech, Financial Services and Insurance, as well as by tech companies like Amazon, Oracle, and IBM.
- Washington, D.C.: Not surprisingly, the most common application of data science in Washington D.C. is social science research, alongside big data management and cloud computing. Top data science employers in the area include the Census Bureau, Northrop Grumman, and information and consulting firms like CACI and Deloitte. Additional data science applications that are important in this region include software and web application and business intelligence.
- Houston, TX: Across Energy/Oil & Gas, Healthcare and other heavily-regulated local industries, Data scientists are fueling the transition from data analytics to the end-to-end management of big data. Building this ecosystem involves problem-solving and collaboration as Data scientists, Data engineers and other professionals identify or create sources of unstructured data, build cloud solutions to store big data in a secure and compliant manner, and communicate the economic value and promise of innovation.
As the proliferation of big data continues, we're seeing growth in Data scientist jobs, but also the rise of specialized data roles. For example, Data engineers, Machine Learning engineers or specialists, Data Architects, or Business intelligence developers can all work together to meet evolving demands.
The availability of data has fundamentally changed many disciplines. Soon, virtually all disciplines will require data scientists who are well versed in statistics, computer science, and mathematics and how they apply to the study of data.
– Chris Jermaine, Rice University's Computer Science Chair.
As the business world transforms to realize the promise of digitalization, Data scientists are key to unlocking a wealth of data available to all businesses, helping companies gain a competitive advantage, and improving society.
What Effect Does a Data Scientist Have on the World?
Data science matters in every industry and can positively impact society. By cleaning and analyzing data, identifying themes and extracting insights, Data scientists are at the forefront of advancing business and industry and finding practical solutions to problems that affect the health, safety, and well-being of people around the world.
Here are a few industry-specific examples of the day-to-day responsibilities of Data scientists:
- Energy/Oil & Gas: Data scientists are tackling the energy industry’s challenges of commodity pricing and environmental performance via data-driven analytics and advanced data science. The work data scientists do is key to identifying productivity bottlenecks to improve efficiency, better maintaining equipment to prevent downtime and accidents, and more effectively gauging the availability of oil resources and market trends.
- Healthcare: From improving outcomes for people and streamlining service provision to driving new medical discoveries, data scientists are transforming patient health and the healthcare industry. Data scientists can help tailor treatments and make earlier detection of disease possible, accelerate the development of new therapies and allow them to reach more people, and improve how healthcare is delivered around the world.
- Bioengineering: Biomedical engineers have played an important role in advancing diagnostics and treatment interventions to vastly improve patient care. Now, in collaboration with data scientists, bioengineering is decoding how the body and disease mechanisms work, gaining more insight into how the brain works and supporting the development of next-generation diagnostics.
- Manufacturing: Smoother production processes optimize output in manufacturing and increase ROI. Data scientists can ensure a facility best manages human capital for increased efficiency, monitor and protect machinery and technology, better leverage the benefits of automation, increase quality control, and monitor logistics and the supply chain in real-time for increased customer service and satisfaction.
- Financial Services & Insurance: By applying data science techniques to the financial services and investments industry, data scientists are able to better understand financial data and solve a number of challenges. Data science can identify unusual transactions to help prevent credit card or insurance fraud, analyze information to better manage credit and market risk exposure, provide valuable insight to tighten credit allocation, and personalize the customer experience and ensure customers are treated fairly and inclusively.
Roles and Responsibilities of Data Scientists
Data scientists are involved in the many steps an organization must take to solve a problem, from discovery and data gathering and organization to analyzing, modeling, and measuring that data. As data science experts, they know how to apply various practices in natural language processing, machine learning, or other techniques that ladder up to the broad category of artificial intelligence. Data scientists use these techniques to sort data and extract meaning. Then they produce data visualizations to help clearly communicate their findings to others.
The role of a data scientist can encompass a variety of activities including:
- Discovery: At this phase, the data scientist works collaboratively with stakeholders to identify a problem and risks associated with that problem. Their insight is important in helping to craft a project plan and identify necessary resources to complete the project.
- Data acquisition: The data scientist then acquires the necessary data to solve the problem, processes and cleans the data to ensure validity and to make sure it is useable, and then organizes the data for initial examination and future modeling.
- Data exploration: Initial exploration of the data happens next, when the data scientist learns about the various values the data presents and different subsets that could be important to explore further.
- Model planning, building and deployment: To mine the value from the data, the data scientist devises and applies models, algorithms, machine learning, and artificial intelligence to the data sets.
- Analysis: The resulting data is then reviewed to extract insights like characteristics, patterns, and trends specific to the problem to be solved. This step allows the data scientist to draw reasonable conclusions from the insight.
- Communication: Data scientists then use visualization techniques to present the technical findings and their observations to stakeholders in a way that is clearly understandable.
Essential Data Science Skills
To become a data scientist, you need extensive knowledge and skills in the following areas: machine learning algorithms, statistics, mathematics, programming, and data visualization. Fundamentally, effective data scientists excel in two core areas: strong math/statistical modeling skills and computer science/programming.
Prospective MDS@Rice students should possess strong math and statistical skills but will learn computer science and programming.
Essential skills necessary to build a rewarding career in data science include:
- Computer science – how computers and communications systems work.
- Advanced mathematics and statistics – foundational knowledge in applied mathematics that helps you effectively collect, organize, analyze, interpret and present data, form hypotheses, and create models.
- Data processing – how to develop strategy and use software for generating, collecting, and consuming data.
- Statistical analysis and modeling – using mathematical models to assess, understand and make predictions about data.
- Machine learning – how algorithms work and how to use them in programming so that a computer can make classifications, predictions, or uncover insights and learn and improve without direction.
- Data visualization – how to graphically represent data in charts, graphs, and other visualizations to make data understandable to others.
- Effective communication and presentation skills – how to clearly convey to a non-technical audience the insight and importance of the information that the data represents.
Rapid Innovation Will Require Evolving Skill Sets
Foundational skills help increase your confidence in data science leadership. But rapid innovation and digital transformation across all industries mean you’ll need to keep your technical skills current and your business skills —like problem-solving, critical thinking, and communication — sharp in order to drive your career development and success. Data science is a versatile and rewarding career for data scientists who, on a day-to-day basis, leverage a variety of skills.
Rapid developments in technologies and data science tools mean that data scientists need to have critical thinking skills that will help them learn as they develop their careers. The role of the data scientist is one of constant learning, from keeping current on software updates and data science research to learning new approaches to problem-solving.
Specialization Skills Are Becoming Increasingly Important for Data Scientists
Take your pick of any industry and you’ll find a place as a data scientist. Mastering particular specialization skills can help you stand out in the field and establish yourself as an exceptional data scientist in a specific industry. The MDS@Rice degree program includes specialty electives like ethics in data science, deep learning and network science, image processing and medical imaging, cybersecurity, and data-driven marketing, finance, and operations management. Plus, you can choose a career specialization in business analytics or machine learning to enhance your career opportunities.
Starting a Career in Data Science
The demand for skilled data scientists is on the rise. Ready to start a career in data science or propel your current career further? Adding a master’s degree in data science from Rice University can help you reach those goals. MDS@Rice offers engaging and multidisciplinary instruction, the opportunity to customize your academics with an industry-specific focus, and hands-on technical skill development in online or on-campus formats. Get a superior data science education that will launch your career. Learn more about MDS@Rice.