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Tom | Data Mentor Profile picture
@TomMitchellData
Aug 5 • 7 tweets • 3 min read Twitter logo Read on Twitter
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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 @TomMitchellData

Tom | Data Mentor Profile picture
Aug 1
6 analysis techniques I used the most as a Data Analyst:
1/ Regression Analysis:

Understanding Relationships.

Regression helps find connections between variables.

For example, it can be used to analyse how sales change when advertising spending goes up or down, ultimately aiding in optimizing marketing budgets.
2/ Factor Analysis:

Simplifying Data

Factor analysis groups correlated variables to reveal hidden patterns.

For example, it can be utilised to identify customer preferences from survey responses for targeted marketing.
Read 8 tweets
Tom | Data Mentor Profile picture
Jul 31
5 must-haves on a Data Analyst resume you might not have thought of:
Domain Knowledge

Showcase industry-specific expertise (e.g., finance, healthcare) to offer deeper insights into data analysis.

Don't worry if it doesn't match the new company.

The fact you took the time to learn about the industry you were in shows you care.
Data Storytelling and Audience Considerations

Demonstrate that you can tailor your approach depending on the audience.

Describe different methods and how you think that improved the impact of your delivery.

Talk about how you crafted stories that inspired action.
Read 8 tweets
Tom | Data Mentor Profile picture
Jul 30
A question every new analyst faces:

Should I learn PowerBI or Tableau?

Your choice will form the backbone of your analytics career.

To help you decide, I've broken it down: 🧵
Power BI and Tableau are two of the most popular Business Intelligence (BI) tools available.

They are widely considered leaders and visionaries in the space and are locked in competition. Image
Power BI is a group of software services, apps, and connectors that work together to turn different sources of data into clear, visually engaging, and interactive insights. Image
Read 14 tweets
Tom | Data Mentor Profile picture
Jul 29
How to move to Data Engineering from Data Analysis:
Bridge the programming gap

Data engineering is very code heavy, more so than data analyst roles.

You need to become an advanced user of Python and SQL (it might even be worth picking up Java).
Learn about database management.

DE's spend a considerable part of their day operating databases, either to collect, store, transfer, clean, or just consult data.

Having expertise with some SQL variations including MySQL, SQL Server, and PostgreSQL is also important.
Read 12 tweets
Tom | Data Mentor Profile picture
Jul 28
A hard truth about working in data:

Not everybody wants to listen to insights.

To inspire action, you must master the art of persuasive data storytelling.

Here's my 5-step framework to do just that:
1/ Clear objective and plot.

A good data story has a clear, specific objective.

Once you've got your objective, craft the plot:

Introduction - Engaging Hook.
Body - Develop the Story.
Climax - Emotional Peak.
Resolution - Satisfying Conclusion.
2/ Make the story interesting.

We've all been there...death by PowerPoint!

Grab the attention of your audience and keep it.

Some ideas on how to do this:

Simplicity.
Surprises/shock factor.
Firmness.
Authenticity.

Be engaging, clear and know your objective.
Read 8 tweets
Tom | Data Mentor Profile picture
Jul 26
Data Science and Python are a match made in heaven.

But there are over 137,000 Python libraries to choose from.

Here are 8 you will use the most as a Data Scientist:
1/ Pandas

High-performance data structures & analysis tools:

Merge & join datasets
Time series support
Slice, index, & subset data
Insert & delete columns
Handle null values
Reshape & pivot data
Read & write data
Perform groupby operations
2/ Numpy

Fundamental library for numerical computing:

Powerful arrays
Mathematical operations
Broadcasting
Linear algebra
Random number support
Fourier transforms
Array manipulation
Read 10 tweets

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