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Top 50+ Data Analyst Interview Questions & Answers

Pickl AI

This comprehensive blog outlines vital aspects of Data Analyst interviews, offering insights into technical, behavioural, and industry-specific questions. It covers essential topics such as SQL queries, data visualization, statistical analysis, machine learning concepts, and data manipulation techniques.

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Data science vs. machine learning: What’s the difference?

IBM Journey to AI blog

Data from various sources, collected in different forms, require data entry and compilation. That can be made easier today with virtual data warehouses that have a centralized platform where data from different sources can be stored. One challenge in applying data science is to identify pertinent business issues.

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Big Data Syllabus: A Comprehensive Overview

Pickl AI

Understanding the differences between SQL and NoSQL databases is crucial for students. Data Warehousing Solutions Tools like Amazon Redshift, Google BigQuery, and Snowflake enable organisations to store and analyse large volumes of data efficiently. Students should learn how to train and evaluate models using large datasets.

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Claypot AI CEO on why you should deploy models the hard way

Snorkel AI

First, you generate predictions and you store them in a data warehouse. So we write a SQL definition. And then during prediction, we can use stream SQL to compute these SQL features. So you might have some data in a data warehouse, you might have some data in real-time transport, or you might have third-party data.

AI 52
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Claypot AI CEO on why you should deploy models the hard way

Snorkel AI

First, you generate predictions and you store them in a data warehouse. So we write a SQL definition. And then during prediction, we can use stream SQL to compute these SQL features. So you might have some data in a data warehouse, you might have some data in real-time transport, or you might have third-party data.

AI 52
article thumbnail

Claypot AI CEO on why you should deploy models the hard way

Snorkel AI

First, you generate predictions and you store them in a data warehouse. So we write a SQL definition. And then during prediction, we can use stream SQL to compute these SQL features. So you might have some data in a data warehouse, you might have some data in real-time transport, or you might have third-party data.

AI 52