In the past few years, the field of data science has grown exponentially. In today’s information-driven world, data is playing a crucial role in every industry — from cybersecurity, healthcare, online retail, banking and insurance, to digital marketing, SEO and several others. No wonder why businesses have started relying on data heavily. And this triggers a boom in diverse job openings related to data science. Among all these positions, perhaps the most overlapping two are that of a data scientist and a data analyst. There’re many who get confused between these two titles and some of them even think that data scientist is just another glammed up word for data analyst.
While the prefix of these titles may lead many to believe that professionals holding these titles carry out the same functions, it isn’t really so. The job descriptions may look somewhat similar, but there’re key differences between the careers. In this post, we’re going to highlight the individual aspects of both data scientist and data analyst and how they’re related to each other.
1- Difference by definition
A data scientist refers to a professional who analyzes massive sets of data from a business standpoint and is responsible for predicting potential trends, exploring disconnected and disparate data sources, and identifying better ways to analyze information in order to help businesses make accurate and informed decisions.
A data analyst focuses on collecting, processing, and obtaining statistical information out of the existing datasets. They focus on developing methods to gather, process, and organize data to reveal actionable insights for present issues, and establishing the best way to demonstrate this data. Put simply, a data analyst is directed toward solving problems that can obstruct immediate improvements.
2- Difference by responsibilities
A data scientist and a data analyst may share similar job responsibilities to some extent, but some notable differences do exist. Let’s take a look at them.
- Cleansing and processing of data
- Developing machine learning models and new analytical methods
- Finding new features by exploring the value of data
- correlating disparate datasets
- Identifying new business questions which can add value
- Data visualization and storytelling
- Identifying the root issues of an outcome
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- Analyzing and mining business data to discover patterns and identify correlations from different data points
- Implementing new metrics for identifying not so clearly understood business parts
- Coordinating with the engineering team to collect incremental new data
- Mapping and tracing the data from different systems to find out solutions to a given business problem
- Applying statistical analysis
- Designing and creating data reports to help stakeholders make better decisions
- Identifying partialities in data acquisition and data quality issues
3- Difference by skill sets
While both data scientist and data analyst positions require solid knowledge of mathematics together with knowledge of software engineering, understanding of algorithms and good communication skills, their actual skill sets differ significantly.
Data scientist skills:
- Programming languages like R, Python, SAS, SQL, Hive, Pig, MatLab, Spark, Scala etc
- Data visualization and storytelling
- Distributed computer frameworks such as Hadoop
- Machine learning and deep statistical insights
- Business acumen
Data analyst skills:
- Data visualization tools such as Tableau
- Data storing and retrieving tools and skills
- Robust exposure to SQL and analytics
- Spreadsheet tools
4- Difference by pay packet
Data scientists earn substantially more money than data analysts. On an average, the starting base salary of a data scientist is around $110,000 while for a data analyst, it stays around $65,000. However, the salary of the latter depends on the type of the analyst they’re — market research analyst, financial analyst, or operations analyst, among others. Learning data science is the first step for these jobs.
5- Difference by job roles
Both the groups are divided further based on their job roles.
- Data scientists are offered job roles like data developers, data researchers, data business and data creative people
- Data analysts are categorized into roles like database administrators, data architects, operations and analytics engineers