Data scientist and data analyst roles have become commonplace as data consumption increases among consumers and enterprises. According to Glassdoor, data scientist¹ hiring roles increased in 2019 and 2020 with data analyst roles following closely.
Enterprises need the services of a data scientist who can make predictive intelligence about the business and understand operation metrics from a data viewpoint. Unlike data scientists, data analysts spend their time reporting and making presentations which help businesses to break down numbers.
Companies need to make data driven decisions and as a competitive strategy, they are hiring data scientists and data analysts to fulfil these objectives. Even though data scientists and data analysts² deal with massive amounts of data, data scientists are more involved unlike data analysts who sit down with company teams. Data analysts play a role in the data science lifecycle³, but their operations are limited compared to data scientists.
The data scientist is involved more in the conceptualization of data frameworks for an organization including analyzing and learning data but data analysts do not have much scope of work like data scientists. This explains why data scientists earn more.
This paper will explore the roles of a data scientist, data analyst, their differences and how enterprises are using their expertise to increase productivity in their operations.
Data scientists have coding skills, which gives them advantage over data analysts who lack computational skills. From models, to #algorithms and frameworks, data scientists take charge of an organizations data system and support in predictive analytics for strategic management. Data scientists are also involved in visualizations with more roles including interpreting data for the organization. With a salary of over $120,000 per year, data scientists are among the most demanded in the tech industry because of their ability to address complex problems⁴.
Most data analysts understand business analytics and their role assists managers to see how their operations are running including performance indicators and revenue margins. Their interpretation of business analytics enables decision-making based on the current numbers. At the same time, they assist companies to measure results based on financial standing and variables such as sales. Despite their limited programming skills, data⁵ analysts can work with basic programs. They earn less than data scientists by a margin of $52000 according to Glassdoor.
Adata analyst is more of a problem solver at the final stage but a data scientist uncovers the problem and comes up with ideas to solve the challenge. Data analysts use statistics⁶ knowledge to look for patterns and communicate results to business management. The data scientist on the other hand goes in depth and plays around with data for intelligent decisions. A data scientist should be a good communicator because of communicating data to the C-Suite. The top executives rely on the ideas of data scientists to make sense of their numbers and execute towards the goal.
Often times, executives misunderstand #data and data scientists play this role better than data analysts because of their detailed understanding of the data. Data analysts have a sketchy idea of data and can translate the information to business decisions but lack understanding about other variables controlling this data. This is where data scientists come in and communicate a clear situation of data patterns to management.
Data scientists have more knowledge about machine learning compared to data analysts whose functions revolve around testing data and statistical patterns. #Machinelearning makes data scientists tick compared to data analysts because of building ML models and pipelines. Data analysts have no expertise in this area.
Forecasting is a strong suit of data scientists and business organizations use their skills to project their market performance and operations. Regression⁷ is an example of predictive models used by data scientists for machine learning projects. Data analysts use SQL in their work compared to data scientists who retrieve patterns from unstructured data by using #NoSQL.
Skill Differences: Data Scientists vs. Data Analysts
Data Scientist vs. Data Analyst Similarities
Data is at the core of operations for data analysts and data scientists but the distinction comes with their work using data. Data analysts understand the overview of data such as patterns and can tell the historical and present trends emerging from data.
On the other hand, data scientists deal with complex algorithms and machine-learning models that help them derive more intelligence from the data such as predicting the future. Data analysts have no expertise in #artificialintelligence and machine learning as data scientists. Companies like hiring data scientists because of communicating the data and storytelling.
The demand for data scientists and data analysts will continue rising as companies seek intelligent solutions for their operations. Data is critical for every company that aspires to become data driven and hiring data scientists and data analysts is the first step. Their collaboration with the C-Suite means that organizations can understand their metrics and make strategic decisions on how to become competitive.
With data volumes increasing, data analysts and data scientists will support businesses as they use data in productive ways that add value to operations. Becoming data driven is not simple as it sounds and enterprises have a role to play in supporting #datascientists and data analysts for them to achieve optimal productivity.
¹Data Scientist, ²Data Analyst, ³Data Science Lifecycle, ⁴Complex Problems, ⁵Discover Data, ⁶Statistics, ⁷Regression, ⁸Unstructured Data