LEGAL RULES AND ETHICAL PRINCIPLES REGARDING DATA ACCESS AND USE
I will always be mindful of legal rules and ethical principles regarding data access and use. Because the goal of Project Autodidact is to develop specific knowledge and skills, I will only use publicly available non-governmental data and synthetic data for Project Autodidact.
OVERVIEW: FUNDAMENTALS
I’m a disciplined and motivated autodidact. I created a self-study plan to achieve the goal of Project Autodidact. I’m a strong believer in the importance of fundamentals in any endeavor, so the self-study plan begins with a deep review of applicable mathematics, probability, and statistics starting from basic arithmetic. Most of these topics are a review for me. Math is very easy for me. I earned top grades in advanced/gifted math classes from elementary school through high school. I completed calculus classes at a local junior college as a high school senior. I earned a grade of A in calculus as a college freshman, which was a review of calculus that I had already learned in high school. I identify the problem type (e.g., pattern recognition) and apply relevant rules and procedures to solve it.
OVERVIEW: STAGES AND MODULES
Project Autodidact is structured into four stages. Each stage is a set of modules. Each individual module is planned to be completed in one week, but the one week is flexible based on my available time. There are a total of 116 modules among all four stages, including modules for mini-projects and rest (i.e., rest weeks). Stage 1 is 3 modules. Stage 2 is 74 modules. Stage 3 is 3 modules. Stage 4 is 36 modules.
PROGRESS REPORTS
Specific details of each module will be shared in progress reports at www.InsightsBySE.com
TEMPORARY PAUSE AND RESUMPTION
Project Autodidact is designed for temporary pause and resumption if necessary.
ADAPTATION
I stay current on developments in the use of data science and artificial intelligence to create predictive and forecasting tools. Project Autodidact will be adapted to address any significant developments, especially new methods or technologies.
CONTINUOUS LEARNING
I’ve created a continuous learning plan to begin after the completion of Project Autodidact.
PROJECT AUTODIDACT’S GOAL
Project Autodidact’s goal is to develop the knowledge and skills to use data science and artificial intelligence to create predictive and forecasting tools for the purposes described below.
- Predictive tools for military and veteran mental health (e.g., predict probability of PTSD during and after service)
- Forecasting tools for veterans’ mental health (e.g., forecast veterans’ mental health issues in the next decade)
- Predictive tools for the socioeconomic effects of proposed government policies on veterans (e.g., predict probability of veteran homelessness)
- Forecasting tools for the socioeconomic effects of proposed government policies on veterans (e.g., forecast labor participation of veterans over the next decade)
PROJECT AUTODIDACT DETAILS
STAGE 1 (3 MODULES): REVIEW OF APPLICABLE MATHEMATICS, PROBABILITY, AND STATISTICS FOR DATA SCIENCE
Module S1-1: Foundations of Arithmetic and Algebra
Module S1-2: Statistics, Probability, and Advanced Algebra
Module S1-3: Linear Algebra, Calculus, and Applications
STAGE 2 (74 MODULES): DATA SCIENCE
Stage 2: Cluster 1: Introduction to Data Science
Module S2-1: Cluster Preparation
Module S2-2: Data Science Tools
Module S2-3: Data Ethics
Module S2-4: Mini-Project
Module S2-5: Rest Week
Stage 2: Cluster 2: Python Programming Fundamentals
Module S2-6: Cluster Preparation
Module S2-7: Python Basics
Module S2-8: Python Data Structures
Module S2-9: Python Libraries
Module S2-10: Mini-Project
Module S2-11: Rest Week
Stage 2: Cluster 3: R Programming Fundamentals
Module S2-12: Cluster Preparation
Module S2-13: R Basics
Module S2-14: R Data Manipulation
Module S2-15: R Visualization
Module S2-16: Mini-Project
Module S2-17: Rest Week
Stage 2: Cluster 4: Julia Programming Fundamentals
Module S2-18: Cluster Preparation
Module S2-19: Julia Basics
Module S2-20: Julia DataFrames
Module S2- 21: Julia Performance
Module S2-22: Mini-Project
Module S2-23: Rest Week
Stage 2: Cluster 5: Databases and SQL Fundamentals
Module S2-24: Cluster Preparation
Module S2-25: SQL Basics
Module S2-26: SQL Joins
Module S2-27: Advanced SQL
Module S2-28: Mini-Project
Module S2-29: Rest Week
Stage 2: Cluster 6: Descriptive Statistics
Module S2-30: Cluster Preparation
Module S2-31: Central Tendency and Dispersion
Module S2-32: Data Distributions
Module S2-33: Mini-Project
Module S2-34: Rest Week
Stage 2: Cluster 7: Inferential Statistics
Module S2-35: Cluster Preparation
Module S2-36: Hypothesis Testing
Module S2-37: ANOVA and Chi-Square Tests
Module S2-38: Mini-Project
Module S2-39: Rest Week
Stage 2: Cluster 8: Regression Analysis
Module S2-40: Cluster Preparation
Module S2-41: Linear Regression
Module S2-42: Multiple Regression
Module S2-43: Mini-Project
Module S2-44: Rest Week
Stage 2: Cluster 9: Probability Fundamentals
Module S2-45: Cluster Preparation
Module S2-46: Probability Distributions
Module S2-47: Conditional Probability
Module S2-48: Bayesian Probability
Module S2-49: Mini-Project
Module S2-50: Rest Week
Stage 2: Cluster 10: Linear Algebra Fundamentals
Module S2-51: Cluster Preparation
Module S2-52: Vectors and Matrices
Module S2-53: Linear Transformations
Module S2-54: Eigenvalues and Eigenvectors
Module S2-55: Mini-Project
Module S2-56: Rest Week
Stage 2: Cluster 11: Calculus Fundamentals
Module S2-57: Cluster Preparation
Module S2-58: Limits and Derivatives
Module S2-59: Integrals
Module S2-60: Applications of Calculus
Module S2-61: Mini-Project
Module S2-62: Rest Week
Stage 2: Cluster 12: Data Visualization
Module S2-63: Cluster Preparation
Module S2-64: Basic Visualizations
Module S2-65: Advanced Visualizations
Module S2-66: Mini-Project
Module S2-67: Rest Week
Stage 2: Cluster 13: Machine Learning Fundamentals
Module S2-68: Cluster Preparation
Module S2-69: Supervised Learning
Module S2-70: Unsupervised Learning
Module S2-71: Mini-Project
Module S2-72: Rest Week
Stage 2: Capstone Project
Module S2-73: Capstone Project Development
Module S2-74: Capstone Presentation and Reflection
STAGE 3 (3 MODULES): REVIEW OF APPLICABLE MATHEMATICS, PROBABILITY, AND STATISTICS FOR ARTIFICIAL INTELLIGENCE
Module S3-1: Foundations of Arithmetic and Algebra
Module S3-2: Precalculus and Introductory Statistics
Module S3-3: Calculus, Linear Algebra, and Optimization
STAGE 4 (36 MODULES): ARTIFICIAL INTELLIGENCE
Stage 4: Cluster 1: Advanced Machine Learning
Module S4-1: Cluster Preparation
Module S4-2: Ensemble Methods
Module S4-3: Hyperparameter Tuning
Module S4-4: Model Interpretability
Module S4-5: Mini-Project
Module S4-6: Rest Week
Stage 4: Cluster 2: Deep Learning Fundamentals
Module S4-7: Cluster Preparation
Module S4-8: Neural Network Basics
Module S4-9: Convolutional Neural Networks
Module S4-10: Regularization and Optimization
Module S4-11: Mini-Project
Module S4-12: Rest Week
Stage 4: Cluster 3: Natural Language Processing
Module S4-13: Cluster Preparation
Module S4-14: Text Preprocessing
Module S4-15: Word Embeddings
Module S4-16: Sequence Models
Module S4-17: Mini-Project
Module S4-18: Rest Week
Stage 4: Cluster 4: Time Series Analysis
Module S4-19: Cluster Preparation
Module S4-20: Time Series Basics
Module S4-21: Advanced Time Series Models
Module S4-22: Time Series Decomposition
Module S4-23: Mini-Project
Module S4-24: Rest Week
Stage 4: Cluster 5: Data Engineering and Deployment
Module S4-25: Cluster Preparation
Module S4-26: Data Pipelines
Module S4-27: Model Deployment
Module S4-28: Mini-Project
Module S4-29: Rest Week
Stage 4: Cluster 6: Stakeholder Communication and Ethics
Module S4-30: Cluster Preparation
Module S4-31: Data Storytelling
Module S4-32: Ethical AI for Stakeholders
Module S4-33: Mini-Project
Module S4-34: Rest Week
Stage 4: Capstone Project
Module S4-35: Capstone Project Development
Module S4-36: Capstone Presentation
Never Doubt A Marine On A Mission
Insights By Scott Ernst 