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40 Must-Know Data Science Skills and Frameworks for 2023

ODSC - Open Data Science

Computer Science and Computer Engineering Similar to knowing statistics and math, a data scientist should know the fundamentals of computer science as well. While knowing Python, R, and SQL are expected, you’ll need to go beyond that. Big Data As datasets become larger and more complex, knowing how to work with them will be key.

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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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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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How to Use Exploratory Notebooks [Best Practices]

The MLOps Blog

Example template for an exploratory notebook | Source: Author How to organize code in Jupyter notebook For exploratory tasks, the code to produce SQL queries, pandas data wrangling, or create plots is not important for readers. in a pandas DataFrame) but in the company’s data warehouse (e.g., documentation.

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Big Data Syllabus: A Comprehensive Overview

Pickl AI

NoSQL Databases These databases, such as MongoDB, Cassandra, and HBase, are designed to handle unstructured and semi-structured data, providing flexibility and scalability for modern applications. Understanding the differences between SQL and NoSQL databases is crucial for students.

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How to Shift from Data Science to Data Engineering

ODSC - Open Data Science

Data scientists typically have strong skills in areas such as Python, R, statistics, machine learning, and data analysis. Believe it or not, these skills are valuable in data engineering for data wrangling, model deployment, and understanding data pipelines. Learn more about the cloud.