In this article, we are differentiating the three similar-sounding job roles of a data analyst, data scientist, and data engineer by discussing thier scope of work, job responsibilities, and required skill sets.

Data Analyst vs Data Scientist vs Data Engineer

In today’s data-driven world, it’s hard to imagine a business that isn’t using data for its operations. Whether it’s customer data or inventory data, it helps businesses make informed and calculated decisions that are less likely to go wrong. Data analysts, data engineers, and data scientists are the three kinds of professionals who handle different aspects of this data. These job roles are mostly different but may overlap with each other sometimes.

For instance, data cleaning may be carried out by both data analysts and data engineers. Moreover, in smaller businesses with fewer workforce, a data scientist might handle the responsibilities of the other two roles as well. It is only in well-established and large-scale businesses that have a distinct set of roles for the three.

That said, let’s begin data analyst first…

Data Analyst

Data analysis is the process of analyzing data and drawing valuable insights out of it. For example, analyzing the product reviews to understand the plus points or weak points of the product. Data analysis can further be categorized into 4 types based on its scope of work:

• Descriptive analysis: describes past data through reports and visualizations
• Diagnostic analysis: finds causes behind faults or any event in general
• Predictive analysis: predicts future trends based on past patterns
• Prescriptive analysis: provides a solution to a business problem

That said, data analysts are people who perform analysis (descriptive, diagnostic, predictive, or prescriptive) on data that may be structured, semi-structured,  or unstructured and may come from a variety of sources like surveys, customer reviews, etc. The results of this analysis are valuable insights that the analysts then present to the company in an easy-to-understand format. This process involves little to no coding making data analyst suitable as an entry-level job. Hence, many people begin their careers as data analysts before switching to data engineers or data scientists.

Roles & responsibilities

• Organizing raw data into structured databases like Excel sheets
• Filtering out inaccurate, conflicting, or false data from the database – this is known as data cleaning
• Identify patterns and use them for predictive analysis
• Generating reports and creating visualizations to make the data understandable
• Drawing and presenting valuable insights to the company
• Collaborating with data engineers and scientists to help the business make informed decisions

Skillsets

• Mathematics and statistics
• SQL, Google Sheets, and MS Excel
• Data visualization tools like Tableau and Hadoop
• Communication and presentation skills
• Coding knowledge is a plus

Data Engineer

Dremio defines data engineering as “…the process of designing and building systems that let people collect and analyze raw data from multiple sources and formats.” A typical data engineering process can be explained as an ETL (Extract, Transform, Load) pipeline:

• Extract: Collecting data from a variety of sources – databases, data warehouses, files, APIs, etc.
• Transform: Standardizing different data formats to make them easy to use.
• Load: Making this processed data available to data analysts and scientists.

Through this process, data engineers provide quality data to data analysts and data scientists, thus making their jobs much easier and quicker.

Roles & responsibilities

• Acquire appropriate data sets for the business
• Cleaning the data of any errors or inconsistencies
• Converting all the data into a consistent and common format
• Develop methods and algorithms for different processes like analysis, validation, transformation, correction, etc.
• Implementing security measures for securing sensitive data

Skillsets

• Proficiency in programming languages is a must (python, SQL, Java, etc.)
• ETL tools
• Automation tools
• Cloud computing
• Big data analytics and tools
• Basics of AI and machine learning algorithms

Data Scientist

Data science is an umbrella term for all things related to data including data analysis and engineering. Hence, data scientists can do the job of both, data analysts and data engineers, and in addition, are also apt in AI and machine learning.

Predictive modeling refers to “…a statistical technique using machine learning and data mining to predict and forecast likely future outcomes with the aid of historical and existing data.” (NetSuite) Data scientists may use predictive modeling to predict things like customer behavior, and market trends.

Thus, a data scientist is the most advanced and senior-most role among the three.

Roles and responsibilities

• Building predictive models using AI and machine learning algorithms
• Helping businesses make strategic decisions based on predicted trends
• Performing experiments by A/B testing
• Staying updated with the latest innovations in data science

Skillsets

• Data analytics
• Programming languages like Python, R, SQL, SAS, etc.
• AI, deep learning, and machine learning
• Big data analytics

Conclusion

Data analysts, data engineers, and data scientists are the three job roles that handle all kinds of data in a business. Data analysts analyze data and provide valuable insights while data scientists go a step further and predict upcoming trends using AI models. Data engineers support the two by providing them with quality data and a stable infrastructure to work on. Together, these roles help businesses make strategic, data-driven, and profitable decisions.