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How to become a data scientist – Key concepts to master data science

Data Science Dojo

Algorithms: Decision trees, random forests, logistic regression, and more are like different techniques a detective might use to solve a case. Hadoop and Spark: These are like powerful computers that can process huge amounts of data quickly. It’s like training a detective to recognize patterns and make predictions.

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How to become a data scientist – Key concepts to master data science

Data Science Dojo

Algorithms: Decision trees, random forests, logistic regression, and more are like different techniques a detective might use to solve a case. Hadoop and Spark: These are like powerful computers that can process huge amounts of data quickly. It’s like training a detective to recognize patterns and make predictions.

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Coding vs Data Science: A comprehensive guide to unraveling the differences

Data Science Dojo

This discipline takes raw data, deciphers it, and turns it into a digestible format using various tools and algorithms. Understanding algorithms is like mastering maps, with each algorithm offering different paths to solutions. Tools such as Python, R, and SQL help to manipulate and analyze data.

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Streaming Machine Learning Without a Data Lake

ODSC - Open Data Science

Commonly used technologies for data storage are the Hadoop Distributed File System (HDFS), Amazon S3, Google Cloud Storage (GCS), or Azure Blob Storage, as well as tools like Apache Hive, Apache Spark, and TensorFlow for data processing and analytics.

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How to become a data scientist

Dataconomy

Concepts such as linear algebra, calculus, probability, and statistical theory are the backbone of many data science algorithms and techniques. Coding skills are essential for tasks such as data cleaning, analysis, visualization, and implementing machine learning algorithms. Specializing can make you stand out from other candidates.

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Building Scalable AI Pipelines with MLOps: A Guide for Software Engineers

ODSC - Open Data Science

It isn’t just about writing code or creating algorithms — it requires robust pipelines that handle data, model training, deployment, and maintenance. Model Development: Selecting algorithms and building models that can solve specific business problems. Model Training: Running computations to learn from the data.

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Big Data Syllabus: A Comprehensive Overview

Pickl AI

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. It is built on the Hadoop Distributed File System (HDFS) and utilises MapReduce for data processing. Once data is collected, it needs to be stored efficiently.