Analytics Insight takes you through the basics of data analytics vs data science to give a broad outlook.

What makes the 21st century different from the 20th century? Just 21 years have beaten the past hundred years. Yes, the main driver of this shift is the debut of data. Big data has become a major component of everyday life, thanks to the actionable insights and results in which businesses can glean. From big data emerged the big two trends: Data analytics and data science. Data analytics vs data science, the two sides of the technology are fighting over dominance. Even though both are very important trends in the digital sphere, people can’t choose both at a time.

The evolution of big data has moved out of the technology sector long back. Today it is everywhere. Without the help of big data, almost all the industries will get jeopardized. The World Economic Forum stated that by the end of 2020, the daily global data generation will reach 44 zettabytes, and it will further surge to 463 exabytes in 2025. However, the creation of such large datasets also requires understanding and having proper tools on hand to parse through them to uncover the right information. They can’t be directly used in any sector.

Data undergoes many routine processes before it is used effectively in an organization. To better comprehend big data, the fields of data science and data analytics have escalated their stance. The duo technologies have moved out of the academic, to instead become a core element of business intelligence and big data analytics tools. But the war between data analytics vs data science is still on. While some organizations can afford to choose both data analytics and data science for their routine functionalities, some others can’t do that. When people are bound to select and support one of the technologies, the conflict breaks out. Picking a career option between these two is also a pain point. Henceforth, this article will take you through the basics of data analytics vs data science and let you know which one will better suit your strategy.

Description

Data analytics: Data analytics is the concept of processing and performing statistical analysis of existing datasets. It is seen as the initial step that analysts create to capture, process, and organize data to uncover actionable insights for business problems. In a nutshell, data analytics provides an answer for complicated data-based or data-related questions which could lead to immediate improvement. It also encompasses a few different branches of broader statistics and analysis which help combine a diverse source of data and locate connections while simplifying the results. Some of the main purposes why the technology is leveraged are listed below,

• By assessing a company’s historical revenue, sales, and costs with its goals, an analyst could identify the budget and investments required to make those goals a reality.
• A data analyst can make cost-effective recommendations to help mitigate business risks.
• With the help of data, marketing analysts can identify the number of leads their efforts must generate to fill the sales pipeline.

Data science: Data science is a versatile and multidisciplinary field focused on finding actionable insights from large sets of raw and structured data. The technology unearths answers for complicated business questions. Data scientists use several different techniques to obtain answers, incorporating computer science, predictive analytics, statistics, and machine learning to parse through massive datasets in an effort to establish solutions to problems that haven’t been thought of yet. Some of the reasons to pick data science are listed below,

• Data scientists identify and avoid mistakes that commonly arise while interpreting datasets, metrics, and visualization.
• The technology embraces data-driven decision-making and ensures that the business decisions are backed by numbers.
• Data science understands the market size, buyer trends, competition, and opportunities, and risks your business faces.

Skills

Data analytics: Data analysts are expected to show much importance to a single or a couple of topics and reflect on them with data. Some of the other skills are,

• Data wrangling
• Understand PIG/HIVE
• Fluent understanding of R and Python
• Knowledge of mathematical statistics

Data science: Since data science revolves around analytics, programming, and domain knowledge, the professionals are expected to be experts in the three departments. Some of the other mandatory skills are,

• Hands-on experience in SQL database coding
• Strong knowledge of Python, SAS, R, Scala, etc
• Understanding multiple functions
• Mandatory knowledge about machine learning

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