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Curated learning resources to improve your analytic skills

OpenIntro Statistics
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An Introduction to Statistical Learning
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Select * SQL Learning Resource
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LinkedIn's SQL Courses
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Understanding window functions
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dbt fundamentals
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Refactoring SQL for modularity
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Tutorialpoint's YAML resource
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Getting started with Snowflake
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Getting started with Snowsight
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Mike Kahan's Snowflake tutorial series
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Getting Started with Redshift
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Complete idiot's guide to YAML
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Get started developing workflows with Apache Airflow
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Kimball in the context of the modern data warehouse: what's worth keeping, and what's not
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Kimball’s Dimensional Data Modeling
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Git in VS Code
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Atlassian's learn Git with Bitbucket
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Toptal's Advanced Git
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LinkedIn's Git Courses
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Getting to know the Command Line
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Best practices with Airflow
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Airflow about subDAGs, branching and xcom
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Demystifying DataOps - Riding the Change
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How to Build a Modern Data Stack
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Metabase-getting-started
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Microsoft PowerBi Training
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Microsoft Power Bi Webinars
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Linkedin's Power Bi Courses
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DAX Guide
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Tableau Getting Started
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LinkedIn's Tableau Courses
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Working With QlickView
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Mike Kahan's Power Bi Tutorials
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What is Apache Superset? 3 critical factors
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Reading list

Love desserts? Explain Big Data Analytics with a Delicious Analogy; Baking Cakes

We all love cakes and whether we bake them ourselves or buy them off the shelf, we all know the general process. In the data world, that is not the case. Many people find it confusing to understand technical terms and lingo, particularly through a sea of indistinguishable sales pitches and marketing messages. So, let’s try to demystify this process using something we can all understand – baking.

Ultimate dbt - data build tool and jinja command cheatsheets

dbt is very powerful and coupled with Jinja macros you can do amazing things, but it's hard to remember all the commands. These handy resources will make your life easier.

You don’t need to build data lake, you need Omakase

The cloud revolution has brought in a new set of challenges for organizations which have nothing to do with delivering solutions. The main problem is that people are faced with having to select among many tools to build a trustworthy data stack and most people would be better served with Omakase.

Using dbt to Document and Test data transformed with other tools

As data practitioners, we often face the challenge of building trust in the insights we deliver. There is a lot of work to go from raw data to meaningful insight and the more people that are in the mix, the harder it is to assure data processes are robust. We often deal with end users finding issues with reports and this usually leads to firefighting trying to pin down the root cause of an error, and fixing it.