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

Pickl AI

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. Statistical Analysis: Hypothesis testing, probability, regression analysis, etc. Cloud Platforms: AWS, Azure, Google Cloud, etc.

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Roadmap to Learn Data Science for Beginners and Freshers in 2023

Becoming Human

In Inferential Statistics, you can learn P-Value , T-Value , Hypothesis Testing , and A/B Testing , which will help you to understand your data in the form of mathematics. For Data Analysis you can focus on such topics as Feature Engineering , Data Wrangling , and EDA which is also known as Exploratory Data Analysis.

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Building ML Platform in Retail and eCommerce

The MLOps Blog

To store Image data, Cloud storage like Amazon S3 and GCP buckets, Azure Blob Storage are some of the best options, whereas one might want to utilize Hadoop + Hive or BigQuery to store clickstream and other forms of text and tabular data. are captured and compared by formulating a hypothesis test to conclude with statistical significance.

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