A repo to store my walkthrough through the CodeNation's program, AceleraDev Data Science
| Role | Responsibility | Full name | |
|---|---|---|---|
| Data Scientist | Author | [Matheus de Almeida Silva] |
[ms.asilvas1@gmail.com] |
The main goal of this week is showing all the frameworks to develop a career as a Data Scientist. You can find the topics below:
- Springer Texts in Statistics: Here we can find one of the most important material to learn the foundations of Machine Learning, in such a statistical approach.
- Machine Learning Step-by-Step: Article written by Jason Brownlee showing us how to start solving a problem using Machine Learning techniques step-by-step.
- How to Become a Data Scientist: The Definitive Guide: Article written in KDNuggets showing the roadmap to become a Data Scientist, giving courses and links for each step.
- Building Data Science Teams: E-book written by DJ Patil sharing the whole process to begin a Data Science Team. Here we can find the roles of a data scientist, products, risks, services and the full context about having a successful team.
- Intro to Data Science - Numpy and Pandas: Article to show an introduction of these frameworks. Mostly how to handle with data using numpy and dataframes with pandas.
- Jupyter Notebook Best Practices: First steps using Jupyter Notebooks to develop a data science project and this article gives some hacking productivity tips.
- Deploy Machine Learning Models in Production as APIs: This tutorial shows how to deploy a model in production using Flask end-to-end to develop a API.
- Hidden Technical Debt in Machine Learning Systems: Paper explaining Machine Learning risk factors to account for in the system design.