Data Science vs. Data Analytics: What are the main differences?

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Big Data is a critical component of today’s corporate world, offering actionable insights and results that can be leveraged to expand business horizons. Creating large data sets requires tools that allow them to be analyzed and relevant information to be identified. This is where the concepts of Data Science and Data Analytics come into play.

Now, although data science and data analysis are core elements of Business Intelligence and are closely connected, they are not identical concepts. In fact, they pursue different approaches and provide different results.

In this article, we describe the characteristics and differences between the terms data science and data analytics.

Data Science: definition and scope
Data science is the application of diverse tools, processes and techniques—programming, statistics, machine learning and algorithms, among others—to combine, prepare and examine large sets of structured and unstructured data.

The core purpose of data science is to ask questions, locate potential avenues for study, identify patterns and trends, and develop actionable insights.

The data science process involves 6 essential phases:

Defining objectives . This field works with stakeholders to define the turkey phone number lead goals and objectives of the analysis. These objectives can be specific or broad.
Data collection . Where systems do not exist to collect and store source data, a systematic process is established to do so.

Data integration and management . Data integration best practices are then applied to transform raw data into clean, analysis-ready information. This process of integrating records involves data replication, ingestion, and transformation to combine different types of data into standardized formats that are then stored in repositories such as data lakes or data warehouses .
Data research and exploration . Initial research and exploratory analysis of the data is then carried out using a data analytics platform or business intelligence tool .

Model development . Based on the business objective and data exploration, one or more potential analytical models and algorithms are chosen and then these models are built using programming languages (Python, SQL, among others) and applying data science techniques (AutoML, machine learning or artificial intelligence ). Finally, the models are trained through iterative testing until they perform adequately.

Deploying and presenting the models . Once selected and refined, the models are run against the available data to gain insights. This information is then shared with stakeholders using dashboards and data visualization tools. Based on stakeholder feedback, necessary adjustments are made to the model.

What do we talk about when we talk about data analytics?
Data analytics involves using tools and processes to combine and examine data sets to detect patterns, develop actionable insights, and promote better decision making .

However, unlike data science, which focuses on open-ended exploration, data analytics examines data to extract value and find answers to specific questions that lead to immediate improvements.

It is an extremely useful tool for all businesses, as it helps to convert a large amount of data into simple information, that is, into conclusions that allow informed and, consequently, appropriate decisions to be made.