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Tom Mitchell Profile picture
@imtommitchell
Aug 5, 2023 • 7 tweets • 3 min read • Read on X
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GitHub offers the best free Data Science education on the internet.

But there are more than 372 million repositories to choose from.

How do you find the best ones?

Bookmark these 5 repositories and start learning fast:
1. Free Programming Books

Books are still an important source of knowledge for any field — and Data Science is no exception.

This GitHub repository contains a huge list of freely available books to learn anything related to programming.

🔗 https://t.co/VnC2HXnKAqgithub.com/EbookFoundatio…
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2. Data Science Roadmap

This repo covers everything from fundamentals to statistics and programming, and then on to machine learning, data visualization and beyond!

🔗 https://t.co/G0DTtEMCOzgithub.com/Moataz-Elmesma…
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3. Awesome Repo

The Awesome Github repository provides an organized list of machine learning libraries, frameworks and tools in many different languages.

🔗 https://t.co/nARAZNZgJAgithub.com/sindresorhus/a…
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4. Public APIs for Data

Finding datasets to practice on can be a challenge.

This repo contains a collective list of free APIs to use for data work

🔗 https://t.co/QSnYHkkAMLgithub.com/public-apis/pu…
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5. Project-Based Learning

A list of programming tutorials divided into primary programming languages like R and Python.

🔗 https://t.co/epotI5RLRxgithub.com/practical-tuto…
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And there you have it!

5 elite Github repos to get you started on your Data Science journey.

If you found this thread helpful, consider following me: @tommitchelldata

I post data-related content every day.

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More from @imtommitchell

Tom Mitchell Profile picture
Sep 10
The difference between SQL, NoSQL, MySQL, and Postgres explained in simple terms:
SQL (Structured Query Language) is the programming language used to manage and manipulate relational databases.
Before we get into the specifics, if you want to learn more about SQL, you should sign up for my (free) weekly newsletter The Data Dose.

I send 1 short, actionable email every week teaching 13,000+ professionals high paying data skills.

Check it out here:

Now, on with the breakdown...thedatadose.com
Read 11 tweets
Tom Mitchell Profile picture
Sep 3
I've worked with PowerBI for 6+ years.

Here are the 3 biggest mistakes I see beginners make (and how to avoid them):
1 - Not optimising data models

What's an easy way to kill user experience and force people to stop using your work?

Have a bloated, sluggish data model.

Instead, only select the data you need.

You can always add to it if needed.

Limit the amount of complex aggregations.

Need to include some complex operations?

Create tailored datasets beforehand.
2 - Cramming too much into visualisations

A simple trick here...

Got lots of categories?

Limit the number shown on each visualisation to top 3-5.

Make it easy for users to digest.

If you need to see the whole picture?

Group the values with least impact into "other".
Read 7 tweets
Tom Mitchell Profile picture
Aug 26
5 steps to write SQL queries that actually perform well:
1. Start with your WHERE clause

Why?

Because filtering early reduces the dataset size before expensive
operations like JOINs and GROUP BYs kick in.

Plan your filters first, then build around them.
2. Use Indexes

They help database find specific information much faster without having to search everything.

Pro tip: Composite indexes give you a huge benefit.

If you're regularly filtering on (date, status, region), create one index on all three columns in that order.
Read 9 tweets
Tom Mitchell Profile picture
Aug 25
If you want to work in data, you should know CI/CD.

Here's a dead simple breakdown that'll teach you fast:
What is CI/CD?

Continuous Integration (CI) + Continuous Deployment (CD).

In simple terms: automatically testing and deploying your data pipelines without breaking things.

Quality control for your code.
Why data teams need it:

Your SQL query works on your laptop.

But will it work in production?
Will it break when your teammate pushes code?
Will bad data crash your entire pipeline?

CI/CD prevents these disasters.

The 4 core components:
Read 12 tweets
Tom Mitchell Profile picture
Apr 30
A Data Analyst who understands segmentation will never be short of work.

Problem is, it's not covered often in courses.

Here's everything I know about customer segmentation condensed:
Customer segmentation helps businesses understand their customers better.

It allows them to tailor marketing strategies, product offerings, and customer experiences to meet the specific needs of each segment.

A segment is a group of customers that share characteristics.

You can segment based on factors like age, gender, location, income, and interests.

Here are some examples of different types of segmentation:
Demographic Segmentation divides customers based on characteristics like age, gender, education, and income.

Behavioural Segmentation categorises customers based on their interactions with your products or services.

Are they loyal, occasional buyers, or inactive customers?

Psychographic Segmentation looks at customer attitudes, values, and lifestyles.
Read 7 tweets
Tom Mitchell Profile picture
Mar 31
Learn SQL in 10 steps (the simple way):
1. The basics are your best mates.

From SELECT to WHERE, get familiar with them.

They’ll make the advanced stuff much easier to grasp.

Don't go too deep into windows functions etc yet. Plenty of time for that later.
2. Coding is an art.

It takes time and consistency to create masterpieces.

Don't rush it.

Look at how other professionals structure their code.

Readability, maintainability, efficiency is the aim of the game.
Read 12 tweets

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