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Big Data vs. Data Science: Demystifying the Buzzwords

Pickl AI

Summary: Big Data refers to the vast volumes of structured and unstructured data generated at high speed, requiring specialized tools for storage and processing. Data Science, on the other hand, uses scientific methods and algorithms to analyses this data, extract insights, and inform decisions.

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Big Data Syllabus: A Comprehensive Overview

Pickl AI

Summary: A comprehensive Big Data syllabus encompasses foundational concepts, essential technologies, data collection and storage methods, processing and analysis techniques, and visualisation strategies. Fundamentals of Big Data Understanding the fundamentals of Big Data is crucial for anyone entering this field.

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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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40 Must-Know Data Science Skills and Frameworks for 2023

ODSC - Open Data Science

Big Data As datasets become larger and more complex, knowing how to work with them will be key. Big data isn’t an abstract concept anymore, as so much data comes from social media, healthcare data, and customer records, so knowing how to parse all of that is needed.

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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. And you should have experience working with big data platforms such as Hadoop or Apache Spark. They may also use tools such as Excel to sort, calculate and visualize data.

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Top Data Analytics Skills and Platforms for 2023

ODSC - Open Data Science

Skills like effective verbal and written communication will help back up the numbers, while data visualization (specific frameworks in the next section) can help you tell a complete story. Data Wrangling: Data Quality, ETL, Databases, Big Data The modern data analyst is expected to be able to source and retrieve their own data for analysis.

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Data Science skills: Mastering the essentials for success

Pickl AI

R, with its robust statistical capabilities, remains a popular choice for statistical analysis and data visualization. Data wrangling and preprocessing Data seldom comes in a pristine form; it often requires cleaning, transformation, and preprocessing before analysis.