Data is the core of machine learning.

It should not surprise you that most of the work you'll have to do is related to capturing, managing, processing, and validating data.

A few recommendations for those who would like to start.

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As you get your feet wet, these are roughly some of the areas that you should cover:

• Data collection
• Data visualization
• Imputation
• Handling outliers
• Encoding
• Normalization and scaling
• Binning and grouping

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Here is a good, introductory, free course provided by Google:

"Data Preparation and Feature Engineering in ML." — https://developers.google.com/machine-learning/data-prep/

It covers the process of collecting, transforming, splitting, and creating datasets that machine learning algorithms can use.

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If you prefer books, check out "Feature Engineering for Machine Learning."

https://amzn.to/3usjyzD 

It's a practical introduction to the fundamental techniques for extracting and transforming features into a suitable format for machine learning models.

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Many people wonder whether it's a good idea to start their machine learning career as a data analyst.

Absolutely, yes!

People with a strong data background make a killing when they start learning and applying algorithms to that data.

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Yes, you should learn SQL.

I understand that non-relational databases are sexy. I understand that Mary says that they are better, and Johnny thinks nothing is like them.

Don't listen to them. Go and learn SQL. The "SELECT thing FROM there" type of SQL.

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There's a lot of creativity when dealing with data.

It's a powerful skill that makes the difference. It's a lot about tools and processes, but even more about brains.

Get good at it, and you'll be ahead of 90% of everyone else around you.

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