Wanna land a data science job?

Build ONE project that solves ONE real-world problem 🚀

3 steps to find the right problem for you 🤗↓
Step #1. Make a list of N (1 < N < 5) problems that GENUINELY interest you ❤️

Examples:
1. Air quality prediction
2. Earthquake prediction
3. Cryptocurrency prediction

Building a project is hard. You will go through ups and downs.

Better pick something that interests you.
Step #2. Check data availability 📊

Without a solid dataset, there is not much Machine Learning you can do.

Hence, start with a pool of ideas but then pick the one for which you can find a GOOD dataset, for example, in Kaggle.
kaggle.com/datasets
Step #3. Check data quality 🔎

Check data quality and quantity before spending too much time on the problem.

For that, you can use a Python library like Sweetviz to run a quick exploratory analysis of the data.

This article explains how↓
datamachines.xyz/2022/06/12/fas…
Building a real-world project is hard, but the effort is worth it.

This is why I am preparing a hands-on tutorial to help you build your OWN Machine Learning project.

Join my e-mail list to be notified when the tutorial is out ↓
datamachines.xyz/subscribe/
Every week I share real-world Data Science/Machine Learning content.

Follow me @paulabartabajo_ so you do not miss what's coming next.

Wanna help?
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More from @paulabartabajo_

Nov 15
Wanna learn MLOps?

Stop reading blog posts.
Build a prediction service instead 🚀

Here is a project you can build (for free) 👩🏽‍💻👨‍💻↓↓↓
Let's build a Machine Learning service to predict the Air Quality Index (AQI) in your city in the next 3 days, using a 100% serverless stack.

You will learn a lot, AND you will build something useful for society.

Win-win 🏆🏆

These are steps to build this ↓
Step 1: Feature generation script 🐍

1 → fetches raw weather and pollutant data from an external API like aqicn.org

2 → computes features from this raw data (aka model inputs), and targets (aka model outputs)

3 → stores these features in the *Feature Store*
Read 10 tweets
Nov 8
Junior data scientists are told the problems they need to solve 👶

Senior data scientists find the problems that need to be solved 😎

Here is how they do it ↓↓↓
#Example: Imagine today is your first day at Spotify.

You are now part of the data science team responsible to maximize paid subscriptions and drive revenue.

The key question is:

"How can you increase subscription revenues with data science?" 🤔
#1 Junior data scientist's idea 💡

Spotify is famous for its smart recommendations, and you completed a course on recommender systems with advanced Deep Learning techniques.

So you think:

"Let's build a better recommendation engine and increase subscriptions"

Wait a second...
Read 11 tweets
Nov 3
The one skill every professional data scientist must have? 🤔

Don't look for it in online courses, it's not there.

Read the thread below and find out ↓↓↓
In the real world, data science projects start from a business problem.

They are born to move a key business metric (KPI):

And you, as a data scientist, need to

1 → Understand the business problem

2 → Transform the business problem into a data science problem

3 → Solve it Image
To transform a business problem into *the right* data science problem you

1 → ask questions and gather business context.
2 → explore the data to find clues.

There is nothing more frustrating than building a great data science solution... to the wrong business problem.
Read 14 tweets
Nov 1
Tired of training lots of Machine Learning models, and not getting better results? 😵‍💫

This is how you solve this 🧠↓
A Machine Learning model is the output of a 3-step workflow where you:

1 → Fetch raw data, for example from an external database.

2 → Process the data into a tabular format, so you have N features and 1 target.

3 → Train ML models (e.g. XGBoost) and tune hyper-parameters.
If your ML model does not work, you have at least 1 of these 2 problems:

1 → The model is too simple to capture the patterns in the training data, and you need a more powerful model (step 3).

2 → The Training data has no patterns, so no model will work (steps 1 and 2).
Read 11 tweets
Oct 27
Overwhelmed by the massive amount of Data Science courses to choose from? 🤯

There is a better way to learn data science 🧠
And to land a job 💼

Here it is ↓
Stop taking courses.

No more passive reading.
No more "easy" paths, that do not stand you out from the crowd.

Instead, focus on BUILDING something you care about.
For example, if you are into Natural Language Processing, you can set yourself this goal:

"I want to build an AI tool to write comedy".

The world definitely needs more laughs 😄
Read 11 tweets
Oct 25
Wanna become an NLP engineer?

Stop taking online courses.
Build something instead 🏗️

Here is a project you can build 👩🏽‍💻👨‍💻↓↓↓
Reading blog posts about multi-billion-parameter Language Models is very cool.

However, building real-world NLP products from these models is where the real business value is. And this is what companies look for in the job market.

So, here is a PRO project you can build ↓
"An app that recommends what ML paper to read"

Imagine an app where you can describe what paper you are interested in reading today. For example

🧑‍🔬: "I want a paper about Transformers in Computer Vision"

Stop imagining. Instead, build this system ↓ in 4 steps
Read 10 tweets

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