This is the beginning of my trajectory to become an awesome data scientist.

I have no degree on computer science and I'm not a "formal" software engineer. I have however a bachelor degree in Physics, so my math background is pretty solid.

I already know Python (thanks to MIT course on edX) but I have A LOT to improve, and I plan to learn Go too.

This is what I figured so far for my development:

deeplearning.ai's Deep Learning Specialization

• Course 1 - Neural networks and deep learning
• Course 2 - Improving deep neural networks
• Course 3 - Structuring machine learning projects
• Course 4 - Convolutional neural networks
• Course 5 - Sequence models

Book "Feature Engineering for Machine Learning"

Summary here

• Chapter 1 - The Machine Learning Pipeline
• Chapter 2 - Fancy Tricks with Simple Numbers
• 3. Text Data: Flattening, Filtering, and Chunking
• 4. The Effects of Feature Scaling: From Bag-of-Words to Tf-Idf
• 5. Categorical Variables: Counting Eggs in the Age of Robotic Chickens
• 6. Dimensionality Reduction: Squashing the Data Pancake with PCA
• 7. Nonlinear Featurization via K-Means Model Stacking
• 8. Automating the Featurizer: Image Feature Extraction and Deep Learning
• 9. Back to the Feature: Building an Academic Paper Recommender

PGA Study

There's a repo for that

• Some descriptive analytics for PGA index file
• Descriptive analytics for siva files on PGA according to some criteria
  • Download siva files
  • Examine siva files
  • Use gitbase to query siva files

Data Science from Scratch

Following O'Reilly book

• Chapter 01 - Introduction
• Chapter 02 - A Crash Course in Python
• Chapter 03 - Visualizing Data
• Chapter 04 - Linear Algebra
• Chapter 05 - Statistics
• Chapter 06 - Probability
• Chapter 07 - Hypothesis and Inference
• Chapter 08 - Gradient Descent
• Chapter 09 - Getting Data
• Chapter 10 - Working with Data
• Chapter 11 - Machine Learning
• Chapter 12 - k-Nearest Neighbors
• Chapter 13 - Naive Bayes
• Chapter 14 - Simple Linear Regression
• Chapter 15 - Multiple Regression
• Chapter 16 - Logistic Regression
• Chapter 17 - Decision Trees
• Chapter 18 - Neural Networks
• Chapter 19 - Clustering
• Chapter 20 - Natural Language Processing
• Chapter 21 - Network Analysis
• Chapter 22 - Recommender Systems
• Chapter 23 - Databases and SQL
• Chapter 24 - MapReduce
• Chapter 25 - Go Forth and Do Data Science

Using source{d} stack

Following "Introduction to Code As Data & Machine Learning On Code"

• Getting started with Babelfish
• Analyzing Git Repositories
• Getting started with gitbase & gitbase web
• MLonCode Pre-trained Models
• Training MLonCode Models

Playing with Kaggle's Titanic dataset

There's a repo for that

• Acquire data
• Analyze by describing data
• Analyze by pivoting features
• Analyze by visualizing data
• Wrangle data
• Model, predict and solve
  • Logistic Regression
  • KNN or k-Nearest Neighbors
  • Support Vector Machines
  • Naive Bayes classifier
  • Decision Tree
  • Random Forrest
  • Perceptron
  • Artificial neural network
  • RVM or Relevance Vector Machine

Lean Analytics Book

Following the book

• To be developed

Natural Language Processing with Python

Following O'Reilly book

• To be developed

Introducing Go

Following the book by Caleb Doxsey

• Chapter 01 - Getting Started
• Chapter 02 - Types
• Chapter 03 - Variables
• Chapter 04 - Control Sctructures
• Chapter 05 - Arrays, Slices and Maps
• Chapter 06 - Functions
• Chapter 07 - Structs and Interfaces
• Chapter 08 - Packages
• Chapter 09 - Testing
• Chapter 10 - Concurrency
• Chapter 11 - Next Steps

Misc

Concepts that I have no clue about and have to study/practice

• reflog
• git bisect
• binaries
• packfile
• namespace
• xpath
• testing
• SDK
• debug
• protobuf
• rpc