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Data science vs data analytics: Unpacking the differences

IBM Journey to AI blog

To pursue a data science career, you need a deep understanding and expansive knowledge of machine learning and AI. For example, business analysts can use BI dashboards to conduct in-depth business analytics and visualize key performance metrics compiled from relevant datasets.

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Why SQL is important for Data Analyst?

Pickl AI

Data Analysts need deeper knowledge on SQL to understand relational databases like Oracle, Microsoft SQL and MySQL. Moreover, SQL is an important tool for conducting Data Preparation and Data Wrangling. For example, Data Analysts who need to use Big Data tools for conducting data analysis need to have expertise in SQL.

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5 Reasons Why SQL is Still the Most Accessible Language for New Data Scientists

ODSC - Open Data Science

But its status as the go-between for programming and data professionals isn’t its only power. Within SQL you can also filter data, aggregate it and create valuations, manipulate data, update it, and even do data modeling. One set of tools that are becoming more important in our data-driven world is BI tools.

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