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Pau Labarta Bajo Profile picture
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Aug 24 β€’ 8 tweets β€’ 4 min read
Wanna become a freelance data scientist? 😎

5 tips to help you become one ↓
#Tip 1: Start small

Clients donΒ΄t look for an all-in-one data scientist, but someone who can solve their SPECIFIC problems.

Identify the things you are already an expert in, e.g.

β†’ Dashboarding with Tableau, or
β†’ ML for computer vision, or
β†’ Scrapping

Apply only for these.
#Tip 2: Build a Minimum Viable Portfolio

Clients want to see real work you have done in the past. They want to see solid proof you can deliver.

Build a small public/private portfolio that focuses on your strengths (from #Tip 1 above).
#Tip 3: Fish in several ponds

There are lots of freelance platforms nowadays, so don't put all the eggs in the same basket.

The 2 platforms I would start with:

β†’ Toptal: toptal.com/Bxdpg6/worlds-…
β†’ Brainstrust: app.usebraintrust.com/r/pau1/
#Tip 4: Write proposals like a pro

Go straight to the point. Focus on the problem from the first paragraph, without preambles and presentations that can only make her yawn.

Decrease cognitive load by using bullet points, and close the proposal with a call-to-action.
#Tip 5: Pricing

Hourly pricing is the best option if you correctly set your hourly rate.

Data science hourly rates fluctuate between 40 USD/hour and 150 USD/hour.

Never go below 40, you will be leaving money on the table.
Wanna get more freelance career advice?

Subscribe to my newsletter and get for FREE my eBook

"How to become a freelance data scientist"

which has specific advice to help you get started on the freelance path.

↓↓↓
datamachines.xyz/subscribe/
That's all for today folks.

I hope you find this content useful for your path πŸ₯Ύβ›°οΈ

Wanna connect? ↓
Follow me @paulabartabajo_

Wanna help?
Like/Retweet the first tweet below to spread the wisdom
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More from @paulabartabajo_

Pau Labarta Bajo Profile picture
Aug 23
Most data scientists focus on algorithms.

So they fail.
Data science = code + DATA

You write code to process and understand the data.

However, if the data is bad, there is nothing that will help you.

Garbage in. Garbage out.
You can play with the code as much as you want.

But if the data is not

β†’ sufficient enough
β†’ complete enough
β†’ good enough

... you will fail.
Read 6 tweets
Pau Labarta Bajo Profile picture
Aug 22
When I talk to aspiring data scientists, they face 1 of 3 challenges
#1 They do not know where to start.

There are tons of educational resources online, that cover all kinds of data science topics. And this can be overwhelming.
Solution: Pick a topic you are interested in, and search for a Github repo or Kaggle notebook that builds a solution around it.

e.g. "Sentiment analysis of Tweets"

Start by reading and copying someone else's work.

Copy from the best and start narrow. Then expand.
Read 8 tweets
Pau Labarta Bajo Profile picture
Jul 27
Are you a data scientist trying to get a job? πŸ’Ό

What if I told you there is a better way than completing yet another online course? 🀯

Let me share with you the 4-step process I follow, to find new freelance projects in the Data Science world πŸ‘‡πŸΎ
Data science is a hot field. There is a huge demand for this role...
... and increased supply as well.

It is getting harder to land a data science job. The market is getting crowded, and competition is increasing.

The question is: how can I stand out from the crowd? πŸ€”
Most data scientists follow a passive approach to learning.

They read papers, blogs, and Twitter threads and collect online course certificates. They sometimes implement something.

If you wanna differentiate yourself you need to play this game differently.
Read 11 tweets
Pau Labarta Bajo Profile picture
Jul 21
Are you a data scientist using CSV files to store your data?

What if I told you there is a better way?

Can you imagine a

-> lighter πŸ¦‹
-> faster 🏎️
-> cheaper πŸ’Έ

file format to save your datasets?

Read this thread so you don't need to imagine anymore πŸ‘‡πŸΎ
Do not get me wrong. I love CSVs.

You can open them with any text editor, inspect them and share them with others.

They have become the standard file format for datasets in the AI/ML community.

However, they have a little problem...
CSV files are stored as a list of rows (aka row-oriented), which causes 2 problems:

- they are slow to query --> SQL and CSV do not play well together.

- they are difficult to store efficiently --> CSV files take a lot of disk space.

Is there an alternative to CSVs?

Yes!
Read 17 tweets
Pau Labarta Bajo Profile picture
Jul 19
How can you learn Machine Learning faster? πŸ€”

2 are the key ingredients.

Let me explain.
Ingredient 1: Exploration πŸ”Ž

If you wanna learn something new, you need to put yourself in a new situation (aka a new state).

Without going out of your comfort zone, there is no learning.

Without exploration, there is no challenge.

And without challenge, there is no happiness
If you are interested in learning Machine Learning, you need to build a habit of exploring something new every week.

You need to put yourself out of your comfort zone.

Pick on a new topic you have been thinking of for a while, but never find the time.

And give it a try.
Read 8 tweets
Pau Labarta Bajo Profile picture
Jun 15
Wanna learn how to train better ML models, by finding and fixing issues in your data? At the speed of light?

Let's see how in this mega 🧡

#data
#machinelearning
The problem

You need to generate your training data at the beginning of every real-world ML project.

Typically, you access an SQL-type database and write a long query that pulls data from several tables, aggregates it, and merges it into the final training set.
The dataset contains a set of features and a target metric you want to predict.

Once you have this data, you are very tempted to train your first ML model.

And this is a BIG mistake.

Instead, you should put a few minutes aside to run a #data exploration
Read 39 tweets

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