Data Scientists
Who they are and why they matter
What is a data scientist?
Data scientists are people who use their statistical, programming and industry domain expertise to transform data into insights. Put another way, data scientists are part mathematician, part computer scientist and part trendspotter. They use their IT smarts to help companies calculate risk and drive positive results.
Evolution of the data scientist role
On the most basic level, a data scientist is a new breed of analytical data expert who has the technical skills to solve complex known problems and the curiosity to explore what unidentified problems might need to be solved next.
Data scientists use data science to glean insights from untold amounts of data, both structured and unstructured. As automation and machine learning become increasingly important components in the IT strategies of large organisations, data analysis grows in stature. The reason? The enormous value practitioners can bring by translating mountains of data into insights that help businesses maximise their potential.
The role of the data scientist and the importance of data science itself took root and grew alongside the rise of big data. As data growth increased exponentially, it became apparent to many organisations that they were sitting on a gold mine, but they were not always positioned to reap the benefits and derive business value from their data. If data is indeed the gold of the information age, data scientists emerged as the ones who could deftly distinguish the real treasures from the fool’s gold.
When the COVID-19 pandemic swept the globe, it accelerated existing trends toward digital transformation, driving a higher-than-expected number of people online to work, shop and entertain themselves. This only made the role of the data scientist more prominent and their function more obviously relevant.
An October 2020 McKinsey study confirmed that the COVID crisis accelerated the digitisation of customer interactions by several years. It makes sense, then, that a 2021 SAS survey found that 91% of data scientists believed their work was as important or more important than it was before the pandemic.
As tools like ChatGPT take off as code generators, some experts are asking, will generative AI replace data scientists? The simple answer is no. Instead, generative AI technologies may automate routine data tasks and help data scientists spend more time using their domain knowledge to explore data, build models and deliver results.
With the ripple effect of adaptations in processes, practices, operating parameters and assumptions, the role of the data scientist looks set to continue its growth trajectory for the foreseeable future. Staff shortages, supply chain disruptions and a surge in e-commerce and cloud services all point to that same conclusion.
The future of data science
Data scientists, developers and modelers need tools with faster startup times, flexibility and choice. Discussing what's next for data science, Dan Soceanu explains the importance of an environment that can scale up and down, in the programming language of choice, and with little IT support. Watch to learn more about the market requirements, risks, accountability and compliance within the field.
Why data scientists are important
The role of the data scientist and the importance of data science itself took root and grew alongside the rise of big data. As data growth increased exponentially, it became apparent to many organizations that they were sitting on a gold mine, but they were not always positioned to reap the benefits and derive business value from their data. If data is indeed the gold of the information age, data scientists emerged as the ones who could deftly distinguish the real treasures from the fool’s gold.
When the COVID-19 pandemic swept the globe, it accelerated existing trends toward digital transformation, driving a higher-than-expected number of people online to work, shop and entertain themselves. This only made the role of the data scientist more prominent and their function more obviously relevant.
An October 2020 McKinsey study confirmed that the COVID crisis accelerated the digitization of customer interactions by several years. So it makes sense that a 2021 SAS survey found that 91% of data scientists believed their work was as important or more important than it was before the pandemic.
As tools like ChatGPT take off as code generators, some experts are asking: Will generative AI replace data scientists? The simple answer is no. Instead, generative AI technologies may automate routine data tasks and help data scientists spend more time using their domain knowledge to explore data, build models and deliver results.
With the ripple effect of adaptations in processes, practices, operating parameters and assumptions, the role of the data scientist looks set to continue its growth trajectory for the foreseeable future. Staff shortages, supply chain disruptions and a surge in e-commerce and cloud services all point to that same conclusion.
Data scientists in today’s world
Hear from data scientists about what they do, and discover what it takes to be a data scientist yourself.
Data scientist skills
Let’s look more closely at the life of the data scientist – their roles and responsibilities in the organisation and the skills that help them excel. As previously mentioned, data scientists are primarily tasked with using software to organise and analyse data. They must also be adept at translating the findings of their analysis into terms that are easily understood by stakeholders who are likely to be a mix of techies and nontechies.
What do data scientists actually do on a day-to-day basis? This graphic from our SAS survey not only provides a window into how they spend their time, it also gives a fairly chronological view of their processes as well. Gathering data is an important first step here, but just one of many that are critical in transforming data into usable insight.
If you're curious about the top programming languages data scientists should know, read this article by ZDNET
Wondering what data scientists actually do on a day-to-day basis? This graphic from our SAS survey not only provides a window into how they spend their time, it also gives a fairly chronological view of their processes as well. Gathering data is an important first step here, but just one of many that are critical in transforming data into usable insight.
In the AI era, data scientists prepare and explore data, develop, train and deploy models, and contribute toward innovation and research. They're also experts at presenting complex analyses in a simplified visual manner.
One note: Be sure not to mistake a data scientist, who tends to work with a long-term view, for a data analyst. Analysts support real-time and short-term decision making. Data scientists take a company or a department’s goals and look further down the road, creating prediction engines and optimisation algorithms to drive efficiencies over the long haul.
What about skills? The best data scientists possess a mix of soft and hard skills in programming, quantitative analysis, intuition, communication and teamwork. And teamwork is growing in importance:
A 2022 SAS survey reveals an ongoing skills shortage for advanced data scientist skills. As many as 63% of decision makers don’t have enough employees with AI and ML skills, even though 54% use these technologies already and 43%-44% plan to do so over the next couple of years.
Discussing data science work
Data scientist Robert Blanchard says he’s a coder by heart, and he loves to code, but he also sees the benefits of drag-and-drop models. Learn how he’s working with colleagues to combine SAS® and Python to deploy a computer vision model on a drone.
Where you’ll find data scientists
Without question, today’s AI technologies have the potential to transform entire industries. As a result, data scientists are increasingly being called on to solve complex problems and help companies better serve their customers.
Data scientists in ...
Banking are helping people visualise the sustainability performance of portfolios.
Agriculture are helping model carbon offset data to encourage sustainable fertiliser production.
Academic research is modelling major risk factors affecting coral reef health and sharing them with other conservationists.
Energy are helping predict the weather to maximise renewable energy sources such as wind and solar power.
Health care and life sciences are helping streamline processes to deliver more effective care and connect data sources to improve the lives of patients along with the effectiveness of providers and governments.
Insurance are helping providers assess risk, detect fraud and refine product offerings to drive more business and better serve customers.
Manufacturing are applying machine learning to anticipate machinery maintenance events or failures and keep the manufacturing line cranking.
Retail are using AI to help shoppers find the shortest line in the store.
Public sector are saving lives by helping coordinate traffic lights for first responders.
Telecom and media are helping to optimise networks and better tailor customer experiences.
Meet three data scientists
An advanced degree, an internship and a certification led this data scientist to land her dream career in New Zealand.
Driven, passionate and curious, data scientist Jessica Rudd earned a PhD so she can make an impact on the future of technology.
Bowtell's story proves it's never too late to change careers if you have enough ambition. Find out how – and why – he switched from engineering to data science.
How to become a data scientist
Looking to position yourself for a career in data science? You’re not alone. This discipline continues to gain relevance. The good news is that the market is nowhere near saturated for data scientist job roles. Here are a few thoughts on the education and training required to get into it.
Students looking to become data scientists
If you’re entering the job market straight from school, consider an undergraduate degree in data science or a related field, like statistics, computer science, computer engineering or information systems. Be sure to choose a university that offers a data science degree or, at least, classes in data science and analytics.
Examples of schools with data science programs include Oklahoma State University, University of Alabama, Kennesaw State University, Southern Methodist University, North Carolina State University and Texas A&M. In many cases, SAS skills are included in the data science curriculum.
Mid-career professionals looking to become data scientists
Many professionals are interested in making a career move into data science. While most data scientists have backgrounds in data analysis or statistics, others come from nontechnical fields in business or economics.
No matter your background, consider whether you possess the basic skills that help data scientists excel – namely, a knack for solving problems, an ability to communicate well and an insatiable curiosity about how things work.
Consider specialising in subtopics like artificial intelligence, research, database management or machine learning. Be prepared to have a solid understanding of:
- Statistics and machine learning.
- Coding languages such as SAS, R or Python.
- Databases such as MySQL and Postgres.
- Data visualisation and reporting technologies.
- Hadoop and MapReduce.
Note that a number of universities now offer a master's in data science.
Career paths for data scientists
The top data scientist job in a large organisation is the chief data officer, or CDO. The CDO oversees all data-related functions and is responsible for helping managers and executives derive business value from all that data. For ambitious go-getters, the path from a junior data scientist to CDO might look something like this:
- Data analyst.
- Midlevel data scientist.
- Senior data scientist.
- Data science manager.
- Data science director.
- Chief data officer.
You can learn the skills you need to become a data scientist on your own, through an online course or a boot camp. Networking helps, too. You can connect with other data scientists or find an online community. Finally, the SAS Academy for Data Science provides an opportunity for aspiring data scientists to master key skills with in-depth training and professional certifications in:
- Data curation.
- Advanced analytics.
- AI and machine learning.
Data science solutions
SAS® Viya® capabilities and offerings feature robust data management, visualisation, advanced analytics and model management to accelerate data science at any organisation.
SAS Visual Data Mining and Machine Learning enables you to solve the most complex analytical problems with a single, integrated, collaborative solution – now with its own automated modelling API.
SAS Visual Analytics provides you with the means to quickly prepare reports interactively, explore your data through visual displays and perform your analyses on a self-service basis.
These solutions and more are powered by SAS Viya, SAS’ market-leading data science platform that runs on a modern, scalable, cloud-enabled architecture.
Software Enquiries: 01628 490 972
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