learning

Learning Philosophy: Master Adjacent Disciplines, The Power of Tiny Gains, T-shaped skills

Develop a business acumen

• Book: Delivering Happiness: A Path to Profits, Passion, and Purpose
• Book: Good to Great: Why Some Companies Make the Leap...And Others Don't
• Book: Hello, Startup: A Programmer's Guide to Building Products, Technologies, and Teams
• Book: How Google Works
• Book: Learn to Earn: A Beginner's Guide to the Basics of Investing and Business
• Book: Rework
• Book: The Airbnb Story
• Book: The Personal MBA - Master the Art of Business
• Facebook: Digital marketing: get started
• Facebook: Digital marketing: go further
• Google Analytics for Beginners
• Google: Fundamentals of Digital Marketing
• Moz: The Beginner's Guide to SEO
• Smartly: Marketing Fundamentals
• Treehouse: SEO Basics
• Thoughtbot: Analytics for Developers
• Udacity: App Monetization
• Udacity: App Marketing
• Udacity: Get Your Startup Started
• Udacity: How to Build a Startup

Be able to frame a ML problem

• AWS: Types of Machine Learning Solutions
• Book: AI Superpowers: China, Silicon Valley, and the New World Order
• Book: A Human's Guide to Machine Intelligence
• Book: The Future Computed
• Book: Machine Learning Yearning by Andrew Ng
• Book: Prediction Machines: The Simple Economics of Artificial Intelligence
• Coursera: AI For Everyone
• Datacamp: Case Studies in Statistical Thinking
• Datacamp: Data Science for Everyone
• Datacamp: Machine Learning with the Experts: School Budgets
• Datacamp: Machine Learning for Everyone
• Datacamp: Analyzing Police Activity with pandas
• Datacamp: Data Science for Managers
• Facebook: Field Guide to Machine Learning
• Google: Art and Science of Machine Learning
• Google: How Google does Machine Learning
• Google: Introduction to Machine Learning Problem Framing
• Microsoft: Define an AI strategy to create business value
• Microsoft: Discover ways to foster an AI-ready culture in your business
• Microsoft: Identify guiding principles for responsible AI in your business
• Microsoft: Introduction to AI technology for business leaders
• Pluralsight: How to Think About Machine Learning Algorithms
• Udacity: Problem Solving with Advanced Analytics
• Youtube: Vincent Warmerdam: The profession of solving (the wrong problem) | PyData Amsterdam 2019

Be able to annotate data efficiently

• Youtube: Snorkel: Dark Data and Machine Learning - Christopher Ré
• Youtube: Training a NER Model with Prodigy and Transfer Learning
• Youtube: Training a New Entity Type with Prodigy – annotation powered by active learning

Be able to manipulate data with Numpy

• Datacamp: Intro to Python for Data Science
• Pluralsight: Working with Multidimensional Data Using NumPy

Be able to manipulate data with Pandas

• Datacamp: pandas Foundations
• Datacamp: Pandas Joins for Spreadsheet Users
• Datacamp: Manipulating DataFrames with pandas
• Datacamp: Merging DataFrames with pandas
• Datacamp: Data Manipulation with pandas
• Datacamp: Optimizing Python Code with pandas
• Datacamp: Streamlined Data Ingestion with pandas
• Datacamp: Analyzing Marketing Campaigns with pandas

Be able to manipulate data in spreadsheets

• Datacamp: Spreadsheet basics
• Datacamp: Data Analysis with Spreadsheets
• Datacamp: Intermediate Spreadsheets for Data Science
• Datacamp: Pivot Tables with Spreadsheets
• Datacamp: Data Visualization in Spreadsheets
• Datacamp: Introduction to Statistics in Spreadsheets
• Datacamp: Conditional Formatting in Spreadsheets
• Datacamp: Marketing Analytics in Spreadsheets
• Datacamp: Error and Uncertainty in Spreadsheets
• edX: Analyzing and Visualizing Data with Excel
• Youtube: Jake VanderPlas - Exploratory Data Visualization with Vega, Vega-Lite, and Altair - PyCon 2018
• UWData: Data Visualization Curriculum

Be able to manipulate data in databases

• Book: Learn SQL the hard way
• Codecademy: SQL Track
• Codecademy: SQL: Table Transformation
• Codecademy: SQL: Analyzing Business Metrics
• Datacamp: Intro to SQL for Data Science
• Datacamp: Introduction to MongoDB in Python
• Datacamp: Intermediate SQL
• Datacamp: Exploratory Data Analysis in SQL
• Datacamp: Joining Data in PostgreSQL
• Datacamp: Querying with TransactSQL
• Datacamp: Introduction to Databases in Python
• Datacamp: Reporting in SQL
• Datacamp: Applying SQL to Real-World Problems
• Datacamp: Analyzing Business Data in SQL
• Datacamp: Data-Driven Decision Making in SQL
• Datacamp: Database Design
• Khan Academy: SQL
• Launch School: Introduction to SQL
• Treehouse: Using Databases in Python
• Udacity: SQL for Data Analysis
• Udacity: Intro to relational database
• Udacity: Database Systems Concepts & Design

Be able to use the command line

• Bash Academy
• Bash Programming
• Codecademy: Learn the Command Line
• CONQUERING THE COMMAND LINE
• Datacamp: Introduction to Shell for Data Science
• Datacamp: Data Processing in Shell
• LaunchSchool: Introduction to Commandline
• Learn Enough Command Line to be dangerous
• Thoughtbot: Mastering the Shell
• Thoughtbot: tmux
• Udacity: Linux Command Line Basics
• Udacity: Linux Web Servers
• Udacity: Shell Workshop
• Udacity: Web Tooling & Automation
• Web Bos: Command Line Power User

Be able to perform feature engineering

• Datacamp: Analyzing Social Media Data in Python
• Datacamp: Dimensionality Reduction in Python
• Datacamp: Preprocessing for Machine Learning in Python
• Datacamp: Data Types for Data Science
• Datacamp: Cleaning Data in Python
• Datacamp: Feature Engineering for Machine Learning in Python
• Datacamp: Importing Data in Python (Part 2)
• Datacamp: Importing & Managing Financial Data in Python
• Datacamp: Manipulating Time Series Data in Python
• Datacamp: Working with Geospatial Data in Python
• Datacamp: Web Scraping in Python
• Datacamp: Analyzing IoT Data in Python
• Datacamp: Dealing with Missing Data in Python
• Datacamp: Exploratory Data Analysis in Python
• edX: Data Science Essentials
• Google: Feature Engineering
• Udacity: Creating an Analytical Dataset

Be able to experiment in notebook

• Pluralsight: Getting Started with Jupyter Notebook and Python

Be able to visualize data

• Datacamp: Introduction to Data Visualization with Python
• Datacamp: Introduction to Seaborn
• Datacamp: Introduction to Matplotlib
• Datacamp: Intermediate Data Visualization with Seaborn
• Datacamp: Visualizing Time Series Data in Python
• Datacamp: Improving Your Data Visualizations in Python
• Datacamp: Visualizing Geospatial Data in Python
• Datacamp: Interactive Data Visualization with Bokeh
• Udacity: Data Visualization in Tableau

Be able to to read research papers

• Paper: A Neural Probabilistic Language Model
• Paper: Efficient Estimation of Word Representations in Vector Space
• Paper: Sequence to Sequence Learning with Neural Networks
• Paper: Neural Machine Translation by Jointly Learning to Align and Translate
• Paper: Attention Is All You Need
• Paper: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
• Paper: XLNet: Generalized Autoregressive Pretraining for Language Understanding
• Paper: Synonyms Based Term Weighting Scheme: An Extension to TF.IDF
• Paper: RoBERTa: A Robustly Optimized BERT Pretraining Approach
• Paper: GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding
• Paper: Amazon.com Recommendations Item-to-Item Collaborative Filtering
• Paper: Collaborative Filtering for Implicit Feedback Datasets
• Paper: BPR: Bayesian Personalized Ranking from Implicit Feedback
• Paper: Factorization Machines
• Paper: Wide & Deep Learning for Recommender Systems
• Paper: Neural Factorization Machines for Sparse Predictive Analytics
• Paper: Multiword Expressions: A Pain in the Neck for NLP
• Paper: PyTorch: An Imperative Style, High-Performance Deep Learning Library
• Paper: ALBERT: A LITE BERT FOR SELF-SUPERVISED LEARNING OF LANGUAGE REPRESENTATIONS
• Paper: Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey
• Paper: A Simple Framework for Contrastive Learning of Visual Representations
• Paper: Self-Supervised Learning of Pretext-Invariant Representations
• Paper: FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
• Paper: Self-Labelling via Simultaneous Clustering and Representation Learning
• Paper: A Survey on Contextual Embeddings
• Paper: A survey on Semi-, Self- and Unsupervised Techniques in Image Classification
• Paper: Shortcut Learning in Deep Neural Networks
• Paper: Multi-document Summarization by using TextRank and Maximal Marginal Relevance for Text in Bahasa Indonesia
• Paper: Train Once, Test Anywhere: Zero-Shot Learning for Text Classification
• Paper: Zero-shot Text Classification With Generative Language Models
• Paper: How to Fine-Tune BERT for Text Classification?
• Paper: Universal Sentence Encoder
• Paper: Enriching Word Vectors with Subword Information
• Paper: Deep Learning Based Text Classification: A Comprehensive Review
• Paper: Beyond Accuracy: Behavioral Testing of NLP models with CheckList
• Whitepaper: Architecting for the Cloud AWS Best Practices
• Whitepaper: AWS Well-Architected Framework
• Whitepaper: AWS Security Best Practices
• Whitepaper: Blue/Green Deployments on AWS
• Whitepaper: Microservices on AWS
• Whitepaper: Optimizing Enterprise Economics with Serverless Architectures
• Whitepaper: Practicing Continuous Integration and Continuous Delivery on AWS
• Whitepaper: Running Containerized Microservices on AWS
• Whitepaper: Serverless Architectures with AWS Lambda

Be able to model problems mathematically

• Book: Basics of Linear Algebra for Machine Learning
• Book: Doing Math with Python
• Datacamp: Foundations of Probability in Python
• Datacamp: Statistical Thinking in Python (Part 1)
• Datacamp: Statistical Thinking in Python (Part 2)
• Datacamp: Statistical Simulation in Python
• edX: Essential Statistics for Data Analysis using Excel
• Essence of Linear Algebra
• Computational Linear Algebra for Coders
• Khan Academy: Precalculus
• Khan Academy: Probability
• Khan Academy: Differential Calculus
• Khan Academy: Multivariable Calculus
• Khan Academy: Linear Algebra
• MIT: 18.06 Linear Algebra (Professor Strang)
  • Lecture 1
  • Lecture 2
  • Lecture 3
  • Lecture 4
  • Lecture 5
  • Lecture 6
  • Lecture 9
  • Lecture 11
  • Lecture 14
  • Lecture 15
  • Lecture 16
  • Lecture 17
  • Lecture 21
• Udacity: Algebra Review
• Udacity: Differential Equations in Action
• Udacity: Eigenvectors and Eigenvalues
• Udacity: Linear Algebra Refresher
• Udacity: Statistics
• Udacity: Intro to Descriptive Statistics
• Udacity: Intro to Inferential Statistics

Be able to structure machine learning projects

• Coursera: Structuring Machine Learning Projects
• Datacamp: Conda Essentials
• Datacamp: Conda for Building & Distributing Packages
• Datacamp: Creating Robust Python Workflows
• Datacamp: Software Engineering for Data Scientists in Python
• Datacamp: Designing Machine Learning Workflows in Python
• Datacamp: Object-Oriented Programming in Python
• Datacamp: Command Line Automation in Python
• Datacamp: Introduction to Data Engineering
• Datacamp: Experimental Design in Python
• Full Stack Deep Learning Bootcamp: March 2019
  • Lecture 1: Introduction to Deep Learning
  • Lecture 2: Setting Up Machine Learning Projects
  • Lecture 3: Introduction to the Text Recognizer Project
  • Lecture 4: Infrastructure and Tooling
  • Lecture 5: Tracking Experiments
  • Lecture 6: Data Management
  • Lecture 7: Machine Learning Teams
  • Lecture 9: Lukas Biewald
  • Lecture 10: Troubleshooting Deep Neural Networks
  • Lecture 11: Labs 6-9: Detection, Data Labeling, Testing and Deployment
  • Lecture 12: Testing and Deployment
  • Lecture 13: Research Directions
  • Lecture 14: Jeremy Howard
  • Lecture 15: Richard Socher
  • Guest Lecture - Chip Huyen - Machine Learning Interviews - Full Stack Deep Learning
• MIT: The Missing Semester of CS Education
• Treehouse: Object Oriented Python
• Treehouse: Setup Local Python Environment
• Udacity: Writing READMEs

Be familiar with a breadth of models and algorithms

• AWS: Semantic Segmentation Explained
• AWS: The Elements of Data Science
• AWS: Understanding Neural Networks
• Book: Pattern Recognition and Machine Learning
• Coursera: Neural Networks and Deep Learning
• Datacamp: AI Fundamentals
• Datacamp: Kaggle Competition
• Datacamp: Extreme Gradient Boosting with XGBoost
• Datacamp: Introduction to PySpark
• Datacamp: Building Recommendation Engines with PySpark
• Datacamp: Foundations of Predictive Analytics in Python (Part 1)
• Datacamp: Foundations of Predictive Analytics in Python (Part 2)
• Datacamp: Ensemble Methods in Python
• Datacamp: HR Analytics in Python: Predicting Employee Churn
• Datacamp: Predicting Customer Churn in Python
• Elements of AI
• edX: Principles of Machine Learning
• edX: Data Science Essentials
• edX: Implementing Predictive Analytics with Spark in Azure HDInsight
• DeepMind: Inefficient Data Efficiency
• DeepMind: DeepMind x UCL | Deep Learning Lecture Series 2020
  • Lecture 7: Deep Learning for Natural Language Processing
• Google: Launching into Machine Learning
• Book: Grokking Deep Learning
• Book: Make Your Own Neural Network
• MIT: 6.S191: Introduction to Deep Learning
• Pluralsight: Understanding Algorithms for Recommendation Systems
• Pluralsight: Deep Learning: The Big Picture
• Udacity: A Friendly Introduction to Machine Learning
• Udacity: Intro to Data Analysis
• Udacity: Intro to Data Science
• Udacity: Intro to Machine Learning
• Udacity: Reinforcement Learning
• Udacity: Deep Learning
• Udacity: Intro to Artificial Intelligence
• Udacity: Classification Models
• Youtube: DETR: End-to-End Object Detection with Transformers (Paper Explained)
• Youtube: Sebastian Ruder: Neural Semi-supervised Learning under Domain Shift
• Youtube: How do we check if a neural network has learned a specific phenomenon?
• Youtube: What is Adversarial Machine Learning and what to do about it? – Adversarial example compilation

Be able to apply unsupervised learning algorithms

• CS294-158-SP20 Deep Unsupervised Learning Spring 2020
• Datacamp: Customer Segmentation in Python
• Google: Clustering
• Google: Recommendation Systems
• Udacity: Segmentation and Clustering
• Youtube: BYOL: Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning (Paper Explained)
• Youtube: A critical analysis of self-supervision, or what we can learn from a single image (Paper Explained)
• Youtube: Week 10 – Lecture: Self-supervised learning (SSL) in computer vision (CV)
• Youtube: CVPR 2020 Tutorial: Towards Annotation-Efficient Learning
• Youtube: Yuki Asano | Self-Supervision | Self-Labelling | Labelling Unlabelled videos | CV | CTDS.Show #81

Be able to implement computer vision models

• Book: Deep Learning for Computer Vision with Python
• Book: Practical Python and OpenCV
• Coursera: Convolutional Neural Networks
• Datacamp: Biomedical Image Analysis in Python
• Datacamp: Image Processing in Python
• Google: ML Practicum: Image Classification
• Udacity: Introduction to Computer Vision

Be able to implement NLP models

• A friendly introduction to Recurrent Neural Networks
• Coursera: Sequence Models
• Coursera: Natural Language Processing in TensorFlow
• Datacamp: Advanced NLP with spaCy
• Datacamp: Building Chatbots in Python
• Datacamp: Clustering Methods with SciPy
• Datacamp: Feature Engineering for NLP in Python
• Datacamp: Machine Translation in Python
• Datacamp: Natural Language Processing Fundamentals in Python
• Datacamp: Natural Language Generation in Python
• Datacamp: RNN for Language Modeling
• Datacamp: Regular Expressions in Python
• Datacamp: Sentiment Analysis in Python
• Datacamp: Spoken Language Processing in Python
• fast.ai Code-First Intro to Natural Language Processing
• RNN and LSTM
• Spacy Tutorial
• Stanford CS224U: Natural Language Understanding | Spring 2019
• TextBlob Tutorial Series
• Treehouse: Regular expression
• Youtube: BERT Research Series
• YouTube: Intro to NLP with Spacy
• Talk: Practical NLP for the Real World
• YouTube: Level 3 AI Assistant Conference 2020
  • Youtube: Conversation Analysis Theory in Chatbots | Michael Szul
  • Youtube: Designing Practical NLP Solutions | Ines Montani
  • Youtube: Effective Copywriting for Chatbots | Hans Van Dam
  • Youtube: Distilling BERT | Sam Sucik
  • Youtube: Transformer Policies that improve Dialogues: A Live Demo by Vincent Warmerdam
  • Youtube: From Research to Production – Our Process at Rasa | Tanja Bunk
  • Youtube: Keynote: Perspective on the 5 Levels of Conversational AI | Alan Nichol
• Youtube: A brief history of the Transformer architecture in NLP
• Youtube: The Transformer neural network architecture explained. “Attention is all you need” (NLP)
• Youtube: How does a Transformer architecture combine Vision and Language? ViLBERT - NLP meets Computer Vision
• Youtube: Strategies for pre-training the BERT-based Transformer architecture – language (and vision)
• Youtube: Ilya Sutskever - GPT-2
• Youtube: NLP Masterclass | Modeling Fallacies in NLP
• Youtube: What is GPT-3? Showcase, possibilities, and implications
• Youtube: TextAttack: A Framework for Data Augmentation and Adversarial Training in NLP
• Youtube: Learning to Rank: From Theory to Production - Malvina Josephidou & Diego Ceccarelli, Bloomberg
• Youtube: Learning "Learning to Rank"
• Youtube: Learning to rank search results - Byron Voorbach & Jettro Coenradie [DevCon 2018]

Be able to model graphs and network data

• Datacamp: Network Analysis in Python (Part 1)
• Datacamp: Network Analysis in Python (Part 2)

Be able to implement models for timeseries and forecasting

• Datacamp: Machine Learning for Finance in Python
• Datacamp: Introduction to Time Series Analysis in Python
• Datacamp: Machine Learning for Time Series Data in Python
• Datacamp: Intro to Portfolio Risk Management in Python
• Datacamp: Financial Forecasting in Python
• Datacamp: Predicting CTR with Machine Learning in Python
• Datacamp: Intro to Financial Concepts using Python
• Datacamp: Fraud Detection in Python
• Datacamp: Forecasting Using ARIMA Models in Python
• Datacamp: Introduction to Portfolio Analysis in Python
• Datacamp: Credit Risk Modeling in Python
• Datacamp: Machine Learning for Marketing in Python
• Udacity: Machine Learning for Trading
• Udacity: Time Series Forecasting

Be familiar with Reinforcement Learning

• DeepLizard: Reinforcement Learning - Goal Oriented Intelligence

Be able to implement ML models with scikit-learn

• Datacamp: Supervised Learning with scikit-learn
• Datacamp: Unsupervised Learning in Python
• Datacamp: Machine Learning with Tree-Based Models in Python
• Datacamp: Introduction to Linear Modeling in Python
• Datacamp: Linear Classifiers in Python
• Datacamp: Generalized Linear Models in Python
• Pluralsight: Building Machine Learning Models in Python with scikit-learn
• Youtube: Applied Machine Learning 2020

Be able to implement models in Tensorflow and Keras

• Coursera: Introduction to Tensorflow
• Coursera: Convolutional Neural Networks in TensorFlow
• Coursera: Getting Started With Tensorflow 2
• Coursera: Customising your models with TensorFlow 2
• Deeplizard: Keras - Python Deep Learning Neural Network API
• Book: Deep Learning with Python
• Datacamp: Deep Learning in Python
• Datacamp: Convolutional Neural Networks for Image Processing
• Datacamp: Introduction to TensorFlow in Python
• Datacamp: Introduction to Deep Learning with Keras
• Datacamp: Advanced Deep Learning with Keras
• Google: Intro to Tensorflow
• Google: Machine Learning Crash Course
• Pluralsight: Deep Learning with Keras
• Udacity: Intro to TensorFlow for Deep Learning

Be able to implement models in PyTorch

• Datacamp: Introduction to Deep Learning with PyTorch
• Deeplizard: Neural Network Programming - Deep Learning with PyTorch
• Udacity: Intro to Deep Learning with PyTorch

Be able to use managed ML services on the cloud

• AWS: Amazon Transcribe Deep Dive: Using Feedback Loops to Improve Confidence Level of Transcription
• AWS: Build a Text Classification Model with AWS Glue and Amazon SageMaker
• AWS: Deep Dive on Amazon Rekognition: Building Computer Visions Based Smart Applications
• AWS: Hands-on Rekognition: Automated Video Editing
• AWS: Introduction to Amazon Comprehend
• AWS: Introduction to Amazon Comprehend Medical
• AWS: Introduction to Amazon Elastic Inference
• AWS: Introduction to Amazon Forecast
• AWS: Introduction to Amazon Lex
• AWS: Introduction to Amazon Personalize
• AWS: Introduction to Amazon Polly
• AWS: Introduction to Amazon SageMaker Ground Truth
• AWS: Introduction to Amazon SageMaker Neo
• AWS: Introduction to Amazon Transcribe
• AWS: Introduction to Amazon Translate
• AWS: Introduction to AWS Marketplace - Machine Learning Category
• AWS: Machine Learning Exam Basics
• AWS: Neural Machine Translation with Sockeye
• AWS: Process Model: CRISP-DM on the AWS Stack
• AWS: Satellite Image Classification in SageMaker
• edX: Amazon SageMaker: Simplifying Machine Learning Application Development

Be able to optimize model performance

• Coursera: Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
• Datacamp: Model Validation in Python
• Datacamp: Hyperparameter Tuning in Python
• Google: Testing and Debugging
• Troubleshooting Deep Neural Networks

Be able to serve and scale ML services

• Acloudguru: AWS Certified Machine Learning - Specialty
• Acloudguru: AWS Certified Developer - Associate
• Acloudguru: AWS Certification Preparation Guide
• AWS: Exam Readiness: AWS Certified Developer – Associate
• AWS: Thirty Serverless Architectures in 30 Minutes
• Codecademy: Deploy a Website
• Datacamp: Parallel Computing with Dask
• Pluralsight: Docker and Containers: The Big Picture
• Pluralsight: Docker and Kubernetes: The Big Picture
• Pluralsight: AWS Developer: The Big Picture
• Pluralsight: AWS Networking Deep Dive: Virtual Private Cloud (VPC)
• Pluralsight: AWS VPC Operations
• Pluralsight: Building Applications Using Elastic Beanstalk
• Servers for Hackers Series
• The Hacker's Guide to Scaling Python
• Udacity: HTTP & Web Servers
• Udacity: Intro to DevOps
• Udacity: Developing Scalable Apps in Python
• Udacity: Configuring Linux Web Servers
• Udacity: Scalable Microservices with Kubernetes
• Udemy: AWS Concepts
• Udemy: Serverless Concepts
• Udemy: AWS Certified Developer - Associate 2018
• Youtube: ML Research and Production Pipelines with Chip Huyen

Be able to perform A/B test

• Datacamp: Customer Analytics & A/B Testing in Python
• Udacity: A/B Testing
• Udacity: A/B Testing for Business Analysts

Be able to write unit tests

• Datacamp: Unit Testing for Data Science in Python
• Pluralsight: Test-driven Development: The Big Picture
• Test Driven Development with Python
• Thoughtbot: Fundamentals of TDD
• Treehouse: Python Testing
• Udacity: Software Analysis & Testing
• Udacity: Software Testing
• Udacity: Software Debugging

Be proficient in Python

• Book: A Byte of Python
• Book: Learn Python The Hard way
• Book: Python 201
• Book: Python Anti-Patterns
• Book: Real Python
• Book: The Python 3 Standard Library By Example
• Book: Writing Idiomatic Python 3
• Codecademy: Learn Python
• Cognitiveclass.ai: Python for Data Science
• Datacamp: Python for R Users
• Datacamp: Python for Spreadsheet Users
• Datacamp: Python for MATLAB Users
• Datacamp: Importing Data in Python (Part 1)
• Datacamp: Intermediate Python for Data Science
• Datacamp: Python Data Science Toolbox (Part 1)
• Datacamp: Python Data Science Toolbox (Part 2)
• Datacamp: Intro to Python for Finance
• Datacamp: Writing Efficient Python Code
• Datacamp: Writing Functions in Python
• Datacamp: Working with Dates and Times in Python
• edX: Introduction to Python for Data Science
• edX: Programming with Python for Data Science
• Google's Python Class
• Treehouse: Python Basics
• Treehouse: Python collections
• Treehouse: Date and Time
• Treehouse: CSV And JSON
• Treehouse: Functional Programming with Python
• Treehouse: Python Decorators
• Treehouse: Write Better Python
• Thoughtbot: Regular Expressions
• TheNewBoston: Python Programming Tutorials
• Udacity: Introduction to Python Programming
• Udacity: Programming Foundations with Python
• Udacity: What is Programming?

Be familiar with compiled languages

• Codecademy: Learn Java
• Udacity: C++ For Programmers
• Udacity: Java Programming Basics

Be able to utilize version control

• Codecademy: Learn Git
• Code School: Git Real
• Datacamp: Introduction to Git for Data Science
• Learn enough git to be dangerous
• Thoughtbot: Mastering Git
• Udacity: GitHub & Collaboration
• Udacity: How to Use Git and GitHub
• Udacity: Version Control with Git

Be able to implement APIs and services

• Book: Refactoring UI
• Codecademy: HTML Projects
• Codecademy: Learn HTML
• Codecademy: Learn Color Design
• Codecademy: Learn SASS
• Codecademy: Make a website
• Codecademy: Learn ReactJS: Part I
• Codecademy: Learn ReactJS: Part II
• Codecademy: Learn JavaScript
• Codecademy: Jquery Track
• Codecademy: Learn Ruby
• Code School: Fundamentals of Design
• Code School: Blasting Off with Bootstrap
• Django Best Practices
• (ES6) - Beau teaches JavaScript
• Pluralsight: UX Fundamentals
• Pluralsight: HTML, CSS, and JavaScript: The Big Picture
• Pluralsight: CSS Positioning
• Pluralsight: Introduction to CSS
• Pluralsight: CSS: Specificity, the Box Model, and Best Practices
• Pluralsight: CSS: Using Flexbox for Layout
• Pluralsight: Using The Chrome Developer Tools
• Thoughtbot: Design for Developers
• Treehouse: HTML
• Treehouse: Javascript Booleans
• Udacity: Authentication & Authorization: OAuth
• Udacity: Designing RESTful APIs
• Udacity: Client-Server Communication
• Udacity: ES6 - JavaScript Improved
• Udacity: Intro to Javascript
• Udacity: Object Oriented JS 1
• Udacity: Object Oriented JS 2
• Udemy: Understanding Typescript
• Youtube: PyConBY 2020: Sebastian Ramirez - Serve ML models easily with FastAPI
• Youtube: FastAPI from the ground up

Be familiar with fundamental Computer Science concepts

• Codecademy: Big O
• Crashcourse: Computer Science
• Grokking Algorithms
• Khan Academy: Data Structures
• Udacity: Intro to Algorithms
• Udacity: Intro to Computer Science
• Udacity: Intro to Theoretical Computer Science
• Udacity: Programming Languages
• Udacity: Networking for Web Developers

Be able to apply proper software engineering process

• Launch School: Agile Planning
• Pluralsight: Product Owner Fundamentals
• Pluralsight: Scrum Master Fundamentals - Foundations
• Pluralsight: Security Awareness: Basic Concepts and Terminology
• Pluralsight: Secure Software Development
• Pluralsight: Clean Architecture: Patterns, Practices, and Principles
• Thoughtbot: Software Development Process
• Thoughtbot: Refactoring
• Udacity: Design of Computer Programs
• Udacity: Product Design
• Udacity: Rapid Prototyping
• Udacity: Software Architecture and Design
• Udacity: Software Development Process
• Udacity: Full Stack Foundations

Be able to efficiently use a text editor

• Learn Enough Text Editor to Be Dangerous
• Mastering Pycharm

Be able to communicate and collaborate well

• Google: Technical Writing
• Book: Emotional Intelligence
• Book: How to Win Friends & Influence People
• Book: Influence: The Psychology of Persuasion
• Book: Leaders Eat Last: Why Some Teams Pull Together and Others Don't
• Book: Multipliers: How the Best Leaders Make Everyone Smarter
• Book: Soft Skills: The software developer's life manual
• Book: The New One Minute Manager
• Youtube: Building a psychologically safe workplace | Amy Edmondson | TEDxHGSE

Be familiar with the hiring pipeline

• Datacamp: Preparing for Statistics Interview Questions in Python
• Datacamp: Preparing for Coding Interview Questions in Python
• Udacity: Optimize your GitHub
• Udacity: Strengthen Your LinkedIn Network & Brand
• Udacity: Data Science Interview Prep
• Udacity: Full-Stack Interview Prep
• Udacity: Refresh Your Resume
• Udacity: Craft Your Cover Letter
• Udacity: Technical Interview
• Youtube: Stanford CS230: Deep Learning | Autumn 2018 | Lecture 8 - Career Advice / Reading Research Papers

Broaden Perspective

• Book: Atomic Habits
• Book: Deep Work
• Book: Outliers: The Story of Success
• Book: Rich Dad Poor Dad
• Book: The Power of Broke
• Book: The 10X Rule: The Only Difference Between Success and Failure
• Book: The Millionaire Fastlane
• Book: The Subtle Art of Not Giving a F**k