“Everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it.” Dan Ariely, a Duke University Economics Professor, once said this about Big Data. The comment also holds true for the data-related terminology and roles.
To be honest, there is no clear distinction in industry standard usage of the terms “Data Analyst” and “Data Scientist”. A lot of ambiguity revolves around the roles; mostly speculating if it is statistics, science, programming, analytics, or some strange combination of all of these. However, the difference lies in the details. Although complementary to one another, these roles often span a wide variety of different skill sets and functional roles. Some companies do treat the titles of “data analyst” and “data scientist” as synonymous. But there is a lot of difference between the two in terms of experience and tasks they perform. Let’s look at each of them separately.
The responsibility of a data analyst varies across various industries and organizations. But the common function comprises using data to draw insights and solve problems. Data analysts usually analyze well-defined sets of data with the help of different tools to support and fulfill tangible business needs. Their tasks encompass market analysis, sales analysis, customer success analysis, pricing analysis, revenue analysis, etc.
Profile: Data analysts need not have a mathematical or research background to invent new algorithms, but what they need to have is a strong understanding of how to use existing tools to solve problems.
Skills: To have a baseline understanding of five core competencies, namely, statistics, programming, data visualization, machine learning, and data munging, is essential for data analysts. It is important for a data analyst to have the knowledge of data mining/data warehouse, data modeling, R or SAS, SPSS, and SQL.
A data scientist is essentially a researcher who conducts undirected research and handle open-ended problems. They ask questions, write algorithms, and build statistical models to identify the hidden patterns in data. Data scientists can tackle higher volumes and velocity of data using multiple tools and at the same time, can build their own frameworks and automation tools.
Profile: Data scientists typically possess advanced degrees in quantitative disciplines, such as applied mathematics, computer science, physics, or, statistics. They need to have the knowledge to build and invent new algorithms to solve unknown data problems.
Skills: Unlike data analysts, a data scientist explores data from different sources. Expertise to use tools like Hadoop, Apache Spark and the skills to maneuver with programming languages like Python, R, and Scala, are mandatory for a data scientist. They should have the competence to apply the practices of advanced math and statistics.
Difference Between The Two Roles
Since we know about the roles of a Data Analysts and a Data Scientist now, let’s look at the key differences between the two:
- A Data Analyst focuses on the movement and interpretation of data, typically with an emphasis on the past and present. However, a Data Scientist is primarily responsible for summarizing data to provide an insight into future based on the patterns identified from the past and the present.
- A data scientist formulates questions to identify the hidden problems in a business and then goes on to solve them, while a data analyst finds solutions for the questions given to them by the business team.
- A Data Analyst and a Data Scientist, both use data but, in different ways. A Data Scientist uses the historical data to predict the future, while a Data Analyst is concerned with reporting metrics.
- A data scientist builds statistical models and performs advanced programming to draw meaningful insights from data while a data analyst typically works on simple databases like structured SQL or with other BI tools/packages.
- A Data Scientist typically performs tasks involving manipulation of data and creation of models to improve the future while a data analyst works on data migration and visualization that focus on describing the past.
A lot of confusion surrounding the roles of a Data Analyst and a Data Scientist is simply because these are new terms emanating from a new field. While the two roles can be interchangeable, it is important to understand that the crux lies in the details. There is no denying that data analysis forms the foundation for working with data; whether as a Data Analyst, or a Data Scientist, or a Strategic Consultant, or a Business Intelligence Analyst, or any other role. Hone your data skills from a premium learning and development marketplace and be the expert.