EDA, ETL and Hypothesis Testing - Data Science Current

The Data Dilemma: Exploring the Key Differences Between Data Science and Data Engineering

Pickl AI

JULY 25, 2023

They create data pipelines, ETL processes, and databases to facilitate smooth data flow and storage. Exploratory Data Analysis (EDA) EDA is a crucial step where Data Scientists visually explore and analyze the data to identify patterns, trends, and potential correlations. ETL Tools: Apache NiFi, Talend, etc.

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Building ML Platform in Retail and eCommerce

The MLOps Blog

MAY 31, 2023

This is the ETL (Extract, Transform, and Load) layer that combines data from multiple sources, cleans noise from the data, organizes raw data, and prepares for model training. Exploratory data analysis The purpose of having an EDA layer is to find out any obvious error or outlier in the data.

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Data Science Current

The Data Dilemma: Exploring the Key Differences Between Data Science and Data Engineering

Building ML Platform in Retail and eCommerce

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