Data scientist vs data analyst: what’s the difference?
There is a lot of hype surrounding data scientists today, but the reality is that many companies are still in dire need of data analysts as well. Data analysts play a key role in helping business users keep tabs on the ball and solve day-to-day problems. In many ways, they can complement the work of data scientists, but they are also important, if not essential, when companies do not have a data science program.
It is helpful to consider the differences between data scientist and data analyst so that companies can build the right team and individuals can develop the most appropriate skills.
“Data analysts focus on data inspection and analysis [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 sector.
Same principles, different questions
The data analyst and data scientist use many of the same principles, often work with similar data sets, address similar questions, and encounter similar obstacles in their work. A fundamental difference between the data scientist and the data analyst 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 of SuperDataScience, an educational service on AI. .
Analysts excel at examining data for novel trends using descriptive and diagnostic analyzes. Conversely, a data scientist attempts to identify patterns in the datasets and then uses those patterns to predict how the data is likely to behave in the future using predictive and prescriptive analytics.
Kirill EremenkoFounder and Director, SuperDataScience
“Since jobs are so closely intertwined, a data analyst is in an excellent position to become a data scientist,” Eremenko said. However, this will require data analysts to change their approach, he said. They should acquire skills to formulate 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 mainly 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 algorithms for predictive and prescriptive analytics.
His role at Deloitte sometimes required him to answer 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 were doing most of the critical thinking for which data scientists are usually responsible.
Analysts understand the business
Data analysts tend to be closer to business users and tend to be experts in 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 area of inbound and outbound data. “They may not be mathematicians, but they can offer great information on how to acquire and manage data and interpret results,” she said.
There is often a fair amount of overlap between data analysts and data scientists. Both process the data with in-depth domain knowledge and math expertise. Over time, Silipo finds that sometimes seasoned business analysts can 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 wrangling tools, said that a useful metaphor for understanding the difference between data scientist and data analyst is that data scientists are the chefs, while analysts at data are line cooks. Chefs are capable of what line cooks can do, but they should come up with the general menu and the theme of the restaurant and meal. Line cooks specialize in the necessary prep work and collecting the necessary ingredients for these meals.
She said: “More often than not, data analysts are tasked with a specific problem where they will leverage the data to find 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 automation tools, data scientists play a key role in the development of the deep reinforcement learning and cognitive business automation platform of company, said Peter Piela, director of development at AI Foundry. Its team of data analysts perform a variety of tasks related to collecting, organizing and cleaning data to assess quality and trends. This team includes specialist business analysts who assist with testing and research activities to understand loan document automation issues. They also work with data retention specialists who apply attention to detail to prepare template training materials.
“It is the knowledge of the business domain offered by data analysts that is invaluable to the data science team,” said Piela.
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 like Python and R. It is also important to master visualization and dashboards in tools like Tableau, Looker, and Excel to deliver insight 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 that businesses find valuable. 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 results to internal stakeholders. Therefore, they both must 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 need to 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.