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Data science tools

Dataconomy

Weka: Features user-friendly data mining processes including pre-processing and classification tasks. Pandas: A crucial library in Python utilized for data wrangling with emphasis on numerical tables and time series data. Hadoop: A robust framework known for efficient data processing and storage of large datasets.

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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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Big Data vs. Data Science: Demystifying the Buzzwords

Pickl AI

Key Takeaways Big Data focuses on collecting, storing, and managing massive datasets. Data Science extracts insights and builds predictive models from processed data. Big Data technologies include Hadoop, Spark, and NoSQL databases. Data Science uses Python, R, and machine learning frameworks.

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How To Learn Python For Data Science?

Pickl AI

Familiarity with basic programming concepts and mathematical principles will significantly enhance your learning experience and help you grasp the complexities of Data Analysis and Machine Learning. Basic Programming Concepts To effectively learn Python, it’s crucial to understand fundamental programming concepts.

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The Evolving Role of the Modern Data Practitioner

ODSC - Open Data Science

From the Early Days of Data Science to Todays Complex Ecosystem Marcks journey into data science began nearly 20 years ago when the field was still in its infancy. In the early 2010s, the rise of Hadoop and cloud computing transformed the industry, introducing data practitioners to new challenges in scalability and infrastructure.

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Data science vs data analytics: Unpacking the differences

IBM Journey to AI blog

Overview: Data science vs data analytics Think of data science as the overarching umbrella that covers a wide range of tasks performed to find patterns in large datasets, structure data for use, train machine learning models and develop artificial intelligence (AI) applications.

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Big Data Syllabus: A Comprehensive Overview

Pickl AI

Big Data Technologies and Tools A comprehensive syllabus should introduce students to the key technologies and tools used in Big Data analytics. Some of the most notable technologies include: Hadoop An open-source framework that allows for distributed storage and processing of large datasets across clusters of computers.