Data Analytics vs. Data Science - 4 Vital Comparisons To Know

Written by Vyasraj Vaidya | Jun 2, 2021 11:02:15 PM

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The world now is more data-driven than ever. Historical data is conspicuously the trigger for data-driven decisions; banners, advertisements, emails, and spam are all the results of data analysis. The data explosion has created the demand for analysts and scientists and thus, calls for the need to understand the difference between the roles.

The following key points are the differences between the roles of a data analyst and data scientist.

Comparisons	Data Analyst	Data Scientist
Definition	Deals with exploratory analysis, identification of trends and patterns, and the creation of visualizations that interpret data so that business leaders can make data-driven strategic decisions.	Deals with the design, construction, and implementation of data modeling using algorithms and predictive models.
Roles and Responsibilities	Captures insights to solve problems such as sales forecasting in a given region, the effect of marketing campaigns on revenue, and more.	Asks questions about defining and improving the quality of data by using data analysis algorithms (machine learning) to understand the information gathered. Solves the text/image classification and product recommendation problems by using deep learning or natural language processing algorithms (NLP).
Skills Required	Tackles responsibilities such as DB administration and maintenance along with reporting and statistical tools. Specialization tools may include but are not restricted to R, SAS, SQL, and SPSS.	Must have skills in data design/modeling, coding, problem-solving with software development using Python, Java, and Big Data. Should also possess some development skills and use deep learning frameworks, such as TensorFlow, Caffe, Torch, and Keras.
Expectations	Analyzes data to answer questions, data mining, and uses statistical tools to identify trends and patterns for descriptive, predictive, diagnostic, and prescriptive analytics. The analyst in most cases is not involved in data extraction, but they use them to create insights for an organization to formulate its strategy to reach its objectives.	Focuses on developing, cleaning, and organizing datasets by asking the right questions about the sources of data, and establishing a pipeline for data acquisition. Leverage algorithms (machine learning) and statistical models to gather and shape accumulated data that can be easily understood. Followed by the repeated deployment of experiments, fine-tuning them, and measurement metrics to achieve better results.

An academic perspective on the roles related to data is provided here by Northwestern University.

Why Do We Need to Understand the Difference?

Although they are similar, knowing the difference helps you select the right role for your project. Business decisions and organizational priorities stay clear when the roles of an analyst and scientist are differentiated correctly.

How Nisum Can Help

The data analysts at Nisum support in delivering smarter insights for quick business decisions and data scientists help in delivering data transformations and empower the business teams to make the best use of data and build for the future. Nisum helps in accelerating data transformations and empower organizations to make smart decisions based on real-time, fact-based intelligence, and insights.

If you are looking to accelerate your data transformation journey, check out our proprietary Data Ingestion Accelerator that will help in making the diverse data sources ready to quickly capture the most potential and extract the most value from the data.

We are a flexible and holistic partner that provides clients an accelerated competitive edge by delivering smarter insights at scale. To learn more about our Insights & Analytics services, contact us.

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