technology_fundamentals

Companion Reading: The Python Data Science Handbook

Series Overview

Course 1: Fundamentals

• Data Science Topics
  • Introduction to python; lists, dictionaries, and flow control; functions and pandas; visualization: matplotlib, seaborn and pandas;
• Software Development Topics
  • Object oriented programming
• Project
  • Tech Fun C1 P1: Building TicTacToe in Python
• Sessions
  • Tech Fun C1 S1: Introduction to Python and Jupyter Notebooks
  • Tech Fun C1 S2: Data Structures and Flow Control
  • Tech Fun C1 S3: Functions and Pandas
  • Tech Fun C1 S4: Visualization and OOP
• Labs
  • Tech Fun C1 L1: Practice with Python and Jupyter Notebooks
  • Tech Fun C1 L2: Practice with Flow Control
  • Tech Fun C1 L3: Practice with Functions and Pandas
• Reading
  • JVDP chapters 1-2
• Non Contact Hour
  • learngitbranch Introduction Sequence
  • machinelearning 101 pandas exercises

Course 2: Statistics and Model Creation

• Data Science Topics
  • Bias-variance tradeoff; regression: linear, logistic, and multivariate; regularization: L1 and L2; inferential statistics: moods median, t-tests, f-tests, ANOVA; descriptive statistics: mean, median, mode, kurtosis, skew
• Software Development Topics
  • Debugging
• Project
  • Tech Fun C2 P2 PART I: Game AI, OOP and Agents (OOP)
  • Tech Fun C2 P2 PART II: Game AI, OOP and Agents (Random Agent)
  • Tech Fun C2 P2 PART III: Game AI, OOP and Agents (Debugging)
• Sessions
  • Tech Fun C2 S1: NumPy
  • Tech Fun C2 S2: Regression and Descriptive Statistics
  • Tech Fun C2 S3: Inferential Statistics
  • Tech Fun C2 S4: Model Selection and Validation
• Labs
  • Tech Fun C2 L1: Descriptive Statistics Data Hunt
  • Tech Fun C2 L2: Inferential Statistics Data Hunt
• Reading
  • JVDP chapters 3-4
  • INFO 370 Statistical Golems
• Non Contact Hour
  • machinelearning 101 numpy exercises

Course 3: Machine Learning I

• Data Science Topics
  • Supervised learning: classification; resampling methods; model selection and regularization; beyond regression coefficients: tree-based methods; unsupervised learning: clustering and dimensionality reduction; neural networks: the perceptron, feed forward neural networks
• Software Development Topics
  • Unit tests
• Project
  • Tech Fun C3 P3: Game AI, Statistical Analysis
  • Tech Fun C3 P4: Game AI, Heuristical Agents
• Sessions
  • Tech Fun C3 S1: Feature Engineering
  • Tech Fun C3 S2: Unsupervised and Supervised Learning
  • Tech Fun C3 S3: Multilayer Perceptron
  • Tech Fun C3 S4: Feed Forward Neural Networks with Tensor Flow
• Labs
  • Tech Fun C3 L1: Feature Engineering
  • Tech Fun C3 L2: Supervised Learners
• Reading
  • JVDP chapter 5

Course 4: Machine Learning II

• Data Science Topics
  • Computer vision: CNNs, importing and manipulating images; time series analysis: LSTMs, autocorrelation; reinforcement learning: defining environments in OpenAI Gym
• Software Development Topics
  • Servers (flask and fastAPI)
• Project
  • Tech Fun C4 P5 Game AI, 1-step Look Ahead
  • Tech Fun C4 P6 Game AI, N-step Look Ahead
  • Tech Fun C4 P7 Game AI, Reinforcement Learning
• Sessions
  • Tech Fun C4 S1: Computer Vision I
  • Tech Fun C4 S2: Computer Vision II
  • Tech Fun C4 S3: Time Series Analysis
  • Tech Fun C4 S4: Reinforcement Learning
• Labs
  • Tech Fun C4 L1: Neural Network Linearity
  • Tech Fun C4 L2: Unit Tests