Covering multilevel modeling theory and developing Python data science skills in more depth.

• Data Analysis Using Regression and Multilevel/Hierarchical Models
• Hands-On ML with Scikit-Learn & Tensorflow
• Designing Data-Intensive Applications
• Elements of Statistical Learning

Interested in learning more about data collection methods, data engineering, and putting models into production.

Resources to consume:

• Building Machine Learning Powered Applications
• The Missing Semester of Your CS Education
• Interpretable ML Book
• Statistical Inference
• Doing Bayesian Data Analysis

This year, I want to focus on deepening my knowledge in applied statistics, becoming a data scientist more generally, and being a better citizen in my local and global community. To that end, here are my goals for 2020.

• Publish 24 good blog posts
• Read 10 technical books
• Publish 6 GitHub projects
• Read 24 books
• Learn Chinese at high-school reading/speaking proficiency
• Build personal website for blogging

I hope that by the end of 2020, I accomplish 80% of these goals. To bake these goals into my lifestyle, I'm planning to learn/study for 40 minutes daily and write to process for 20 minutes daily.

• The Causal Inference Book
• Statistical Inference
• Mostly Harmless Econometrics
• Econometric Analysis
• An Introduction to Generalized Linear Models
• Bloomberg Foundations of Machine Learning

Useful links to review for learning advice. Saw this blog about learning languages - thought the advice was great.

• Motivation
• Self teaching
  • Self education
  • Process for learning machine learning
  • Building a learning curriculum
• Job hunting
  • Data Science Career Map
  • Jobs for New Data Scientists

• https://medium.com/@jasonkgoodman/advice-on-building-data-portfolio-projects-c5f96d8a0627
• https://towardsdatascience.com/how-to-build-an-effective-data-science-portfolio-56d19b885aa8