Thread by @jsulopz

Introducing the #PythonResolver Roadmap to learn #Python + #DataScience 👀

During 3 years working as a Private Tutor, I have tried a variety of materials with more than 500 students from several industries:

· Medicine
· Finance
· Energy
· Psychology

Check it out ↓

#1 Resolving #Python for Data Science

Please, read the README files of every GitHub repository you want to interact with. They explain how to use the materials.

Otherwise, you'll go over the materials like a headless chicken 🍗

github.com/jsulopz/resolv…

If you have any doubts, please reply to the tweet you doubt with.

I will be more than happy to help you because you give me feedback to improve my content and grow as a content creator!

Win-win 😉

- Everything nowadays is information.

- Information is data.

Where is the data gathered to be later analysed?

- In a Data Table

How does Python work with Data Tables?

- The #DataFrame from Pandas library

How can you master the manipulation of the DataFrame?

#2 Practice with #Pandas Exercises

Guilherme Samora created this GitHub repo. It contains a sequential list of exercises that increase the difficulty as you advance the chapters.

Please, read the README to learn more about how to use them!

github.com/jsulopz/pandas…

It would help if you aimed to be pragmatic:

Apply the concepts you learn in #1 to #2 exercises simultaneously.

It would help if you aimed to be curious:

Don't focus on results; you have got the solutions already. Instead, focus on taking notes and solving your doubts.

Once you have learnt #1 Resolving Python for Data Science and have gotten used to working with the DataFrame in #2 Pandas Exercises, you are ready to jump into #MachineLearning

Companies, Organizations & Institutions are making decisions all the time.

They make decisions based on the data their Stakeholders generate. Then, they manipulate the DataFrame and create cool visualisations to understand what's going on.

Once they have understood, they make decisions based on guesses about what may happen in the future.

Nevertheless, it's tough for a human to predict what may happen in the future because you need to consider more than 10 factors/variables.

What can a human do then?

- Rely on #MachineLearning

Machines learn from historical data to create a Mathematical Equation that predicts the future.

https://twitter.com/jsulopz/status/1449735653328031745?s=20&t=AcDjccG585cUjlCRNIhS2Q

This Mathematical Equation can gather as many factors/variables as you want. Therefore, the machine will make better predictions than a human.

How can I learn #MachineLearning?

As I was teaching people "à la carte" when they asked me to learn, for example, the Decision Tree algorithm that the University assignment required, I realised I needed to explain more concepts to understand the whole picture.

The following materials cover the whole picture.

#3 Machine Learning

This GitHub repo is the fruit of all the University assignments I have helped my students with.

They gather the different types of Mathematical Equations the Machine can learn in sequential order of difficulty.

github.com/jsulopz/machin…

After you are done with this GitHub repo, you will know how to apply different Machine Learning algorithms based on the variable you'd like to predict.

What's next?

- Advanced Machine Learning for feature interpretation

- Deep Learning