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Data science

Dataconomy

Overview of core disciplines Data science encompasses several key disciplines including data engineering, data preparation, and predictive analytics. Data engineering lays the groundwork by managing data infrastructure, while data preparation focuses on cleaning and processing data for analysis.

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Data Science Career Paths: Analyst, Scientist, Engineer – What’s Right for You?

How to Learn Machine Learning

Data Storage and Management Once data have been collected from the sources, they must be secured and made accessible. The responsibilities of this phase can be handled with traditional databases (MySQL, PostgreSQL), cloud storage (AWS S3, Google Cloud Storage), and big data frameworks (Hadoop, Apache Spark).

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Advanced analytics

Dataconomy

Big data analytics Big data analytics involves processing vast amounts of structured and unstructured data, extracting key insights that drive business decisions. Machine learning Integrating machine learning enhances the accuracy of predictive analytics applications, continuously learning from new data inputs.

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Data science vs data analytics: Unpacking the differences

IBM Journey to AI blog

And you should have experience working with big data platforms such as Hadoop or Apache Spark. Additionally, data science requires experience in SQL database coding and an ability to work with unstructured data of various types, such as video, audio, pictures and text.

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Use of Data Analytics by Uber to Enhance Supply Efficiency and Service Quality

Pickl AI

Read More: Use of AI and Big Data Analytics to Manage Pandemics Overview of Uber’s Data Analytics Strategy Uber’s Data Analytics strategy is multifaceted, focusing on real-time data collection, predictive analytics, and Machine Learning.

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Data Science Cheat Sheet for Business Leaders

Pickl AI

There are three main types, each serving a distinct purpose: Descriptive Analytics (Business Intelligence): This focuses on understanding what happened. Think of it as summarizing past data to answer questions like “Which products are selling best?” ” or “What are our customer demographics?”

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Data science vs. machine learning: What’s the difference?

IBM Journey to AI blog

The fields have evolved such that to work as a data analyst who views, manages and accesses data, you need to know Structured Query Language (SQL) as well as math, statistics, data visualization (to present the results to stakeholders) and data mining.