Hello!

My name is Miguel Ferreira and I’m a web developer and data scientist from Brazil, especialized in CRISP-DM data science methodology and predictive Machine Learning for time series.

I hold the IBM Data Scientist certificate (Coursera) and several others professional certificates from Udemy, Unimed (network of hospitals) and Digital Innovation One (online tech profissionalization network) in different tech areas as Data Science, Machine Learning, Databases, MERN web development and Java Web Development. I’m proficient in tasks that envolves:

• CRISP-DM Data Science Methodology 📊
• Python frameworks for web development 📋
• Java frameworks for web development 📑
• Version control 🛠️
• Deploy and hosting 🍻
• Java OOP 👾

My most important tool is my capability to link different fields of knowledge on approaching the problem to be solved.

When I’m not being a computer nerd, I like reading old books (the older the better), playing chess, swimming, drawing and studying and working on new good but impracticable ideas.

Pic I took

You can check my curriculum here.

Check all my certificates here.

Below, you can find some of my projects:

IBM Capstone Space-X Data Science Final Project

“In this Professional Certificate, learners developed and honed handson skills in Data Science and Machine Learning. Learners started with an orientation of Data Science and its Methodology, became familiar and used a variety of data science tools, learned Python and SQL, performed Data Visualization and Analysis, and created Machine Learning models. In the process they completed several labs and assignments on the cloud including a Capstone Project at the end to apply and demonstrate their knowledge and skills.”

In this project, a full data science methodology is displayed in order to provide an insight about the launch of a rocket by Space-X. The purpose of this project is to establish data based information about the costs and the success rate of a lauch in order to realize if it is or if it isn’t an affordable mission.

Check full project here.

LSTM/RNN fo Movie Review Predictor with Flask

In this small project using Flask, I deploy a Machine Learning LSTM Neural Networks to create a algorithm that can understand a movie critic and define if it’s a good or bad review. The usage is very simples, as seen below.

Check out the code here.

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// Javascript code with syntax highlighting.
var fun = function lang(l) {
  dateformat.i18n = require('./lang/' + l)
  return true;
}

# Ruby code with syntax highlighting
GitHubPages::Dependencies.gems.each do |gem, version|
  s.add_dependency(gem, "= #{version}")
end

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1. This is an ordered list following a header.
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3. This is an ordered list following a header.

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head1	head two	three
ok	good swedish fish	nice
out of stock	good and plenty	nice
ok	good oreos	hmm
ok	good zoute drop	yumm

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Here is an unordered list:

• Item foo
• Item bar
• Item baz
• Item zip

And an ordered list:

1. Item one
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And a nested list:

• level 1 item
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    • level 3 item
    • level 3 item
• level 1 item
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• level 1 item
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• level 1 item

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Definition lists can be used with HTML syntax.

Name

Godzilla

Born

1952

Birthplace

Japan

Color

Green

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The final element.