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Top 10 Data Science Websites to learn More

Knowledge Hut

Learning Data Analysis in Excel Data analysis is a process of inspecting, cleaning, transforming and modelling data with an objective of uncover the useful knowledge, results and supporting decision. Then, based on this information from the sample, defect or abnormality the rate for whole dataset is considered.

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Natural Language Processing: A Guide to NLP Use Cases, Approaches, and Tools

AltexSoft

And this technology of Natural Language Processing is available to all businesses. Available methods for text processing and which one to choose. What is Natural Language Processing? Natural language processing or NLP is a branch of Artificial Intelligence that gives machines the ability to understand natural human speech.

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An AI Chat Bot Wrote This Blog Post …

DataKitchen

ChatGPT> DataOps, or data operations, is a set of practices and technologies that organizations use to improve the speed, quality, and reliability of their data analytics processes. It involves bringing together people, processes, and technology to enable data-driven decision making and improve the efficiency of data-related workflows.

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Complete Guide to Data Ingestion: Types, Process, and Best Practices

Databand.ai

Complete Guide to Data Ingestion: Types, Process, and Best Practices Helen Soloveichik July 19, 2023 What Is Data Ingestion? Data Ingestion is the process of obtaining, importing, and processing data for later use or storage in a database. However, the ultimate goal is simple: to prepare data for immediate use.

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What Are the Best Data Modeling Methodologies & Processes for My Data Lake?

phData: Data Engineering

However, to fully harness the potential of a data lake, effective data modeling methodologies and processes are crucial. With many data modeling methodologies and processes available, choosing the right approach can be daunting. Efficiency through being able to streamline data storage and retrieval processes.

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Big Data Technologies that Everyone Should Know in 2024

Knowledge Hut

In this blog post, we will discuss such technologies. In the past, this data was too large and complex for traditional data processing tools to handle. However, advances in technology have now made it possible to store, process, and analyze big data quickly and effectively. Data processing is where the real magic happens.

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The Five Use Cases in Data Observability: Mastering Data Production

DataKitchen

Overseeing multi-tool, multi-dataset, and multi-hop data processes ensures high-quality outputs. The Third of Five Use Cases in Data Observability Data Evaluation: This involves evaluating and cleansing new datasets before being added to production. This process is critical as it ensures data quality from the onset.