Data scientist vs data analyst: what is the difference?

There’s a lot of hype around data scientists today, but the reality is that many companies are still in dire need of data analysts. Data analysts play a key role in helping business users keep an eye on the ball and troubleshoot day-to-day issues. In many ways, they can complement the work of data scientists, but they’re also important, even critical, when companies don’t have a data science program.

It is useful to consider the differences between data scientists and data analysts so that companies can build the right team and individuals can hone the most appropriate skills.

“Data analysts emphasize inspection and analysis of data [and] create reports, while data scientists focus on experiments, research and machine learning,” said Ji Li, director of data science at Clara Analytics, which provides an AI platform for the commercial insurance industry.

Same principles, different questions

The data analyst and data scientist use many of the same principles, often work with similar data sets, deal with similar questions, and face similar obstacles in their work. A fundamental difference between data scientists and data analysts is that analysts are usually given a set of questions to answer, while data scientists are usually expected to ask their own questions, said Kirill Eremenko, Founder and Director from SuperDataScience, an AI education service. .

Analysts excel at examining data to find new patterns using descriptive and diagnostic analysis. Conversely, a data scientist attempts to identify patterns in data sets and then uses those patterns to predict how the data is likely to behave in the future using predictive and prescriptive analytics.

Since the jobs are so intertwined, a data analyst is in a great position to become a data scientist.

Kirill EremenkoFounder and Director, SuperDataScience

“Because the jobs are so intertwined, a data analyst is in a great position to become a data scientist,” Eremenko said. However, this will force data analysts to change their approach, he said. They should learn skills to form their own hypotheses based on the data available to them, and then prove or disprove those theories.

Eremenko started as a data analyst at Deloitte Analytics in Australia. At Deloitte, he primarily used data to answer questions like, “What happened? and “Why did this happen?” He then got a job as a senior data scientist at Sunsuper, a pension management company, where he had to test various predictive and prescriptive analytics algorithms.

His role at Deloitte sometimes tasked him with answering more open-ended questions, such as “What’s going to happen?” and “How do we get there?” However, these issues were either clearly defined by managers and directors or dealt with directly by them. So managers and directors did most of the critical thinking that data scientists are typically responsible for.

Analysts understand the business

Data analysts tend to be closer to business users and tend to be experts on available data, said Rosaria Silipo, senior data scientist at Knime, a data science and analytics platform. They know the business case, the data collection process, and the domain of incoming and outgoing data. “They may not be mathematicians, but they can offer great insights into how to acquire and manage data and how to interpret results,” she said.

There is often a lot of overlap between data analysts and data scientists. Both process data with deep domain knowledge and mathematical expertise. Over time, Silipo finds that experienced business analysts can sometimes deepen their knowledge of statistics and machine learning to improve their value. On the other end of the spectrum, data scientists and engineers can learn more about the data collection process and business cases, especially after a few years in the field.

Chefs and line cooks

Cheryl To, data scientist at ThinkData Works, a provider of data management tools, said a useful metaphor for understanding the difference between data scientists and data analysts is that data scientists are the bosses. cooks, while data analysts are the line cooks. Chefs are capable of what line cooks can do, but they must come up with the overall menu and theme of the restaurant and meal. Line cooks specialize in the necessary prep work and gathering the ingredients needed for these meals.

She said, “Most often, data analysts are tasked with a specific problem where they will mine the data to derive a meaningful solution.” This complements the work of data scientists, who have more freedom to explore and generate their own questions based on their analysis.

For example, at AI Foundry, a provider of mortgage loan automation tools, data scientists play a key role in developing the deep reinforcement learning and cognitive business automation platform. company, said Peter Piela, director of development at AI Foundry. Its team of data analysts performs a variety of tasks related to collecting, organizing and cleaning data to assess quality and trends. This team includes specialist business analysts who participate in testing and research activities to understand loan document automation issues. They also work with data curation specialists who apply attention to detail to prepare training document templates.

“It’s the business domain knowledge that data analysts provide that is invaluable to the data science team,” Piela said.

Cultivate new skills

Data analysts can differentiate themselves by honing a variety of technical and soft skills. Vivek Ravisankar, CEO and co-founder of HackerRank, a developer recruiting service, recommended that analysts focus on improving their understanding of statistics and data management, especially using tools such as Python and R. Proficiency in visualization and dashboards in tools such as Tableau, Looker, and Excel is also important to provide insights and communicate effectively with key stakeholders.

He also recommended that they be aware of new technologies and new markets that can impact the types of data businesses find useful. For example, IoT data was not as valuable a few years ago as it is today.

Data scientists and data analysts will often present data findings to internal stakeholders. Therefore, they both need to have the ability to relate their work to diverse audiences, said Dr. Angel Durr, CEO and founder of DataReady, a data literacy program. “Good storytelling and organizational skills are an essential aspect of both careers,” she explained.

Additionally, data analysts and data scientists must be comfortable with a high degree of ambiguity. They must learn how to effectively manage and maintain data processes and document processes with the goal of constantly improving and developing processes.

Durr recommended that analysts cultivate some level of CRM expertise, as most organizations use these systems in combination with other sources to get the big picture. “Understanding the data and understanding the specific needs of your area of ​​expertise will make you invaluable to any organization,” she said.

Sean N. Ayres