Prepare (due Monday 11/18) Content below Canvas quizzes Class engagement – See on the class forum Homework (due Sun 12/6) [Link] There are no worked examples Content 10 Deep Learning Neural Networks and Applications (16 min.) Forward Propagation (10 min.) Gradient Descent (14 min.) Back Propagation (11 min.) Convolutional Neural Network (15 min.) Introducing Pytorch (23 min.) Optional Supplements Pytorch Unlike […]
Posts in the Module category:
Module 09: Databases and SQL
Prepare (due Mon 11/04) Content below Canvas quizzes Class engagement – See on the class forum Homework (due Sun 11/10) [LINK] Worked Example [LINK] Content 09.A – Relational Database Relational Database (24 min.) 09.B – SQL Python and Pandas SQL Querying (21 min.) SQL with Python and Pandas (12 min.) Optional Supplements SQLite Command Line Interface If you have […]
Module 07: Statistical Inference
Prepare (due Mon 10/21) Content below Canvas quizzes Class engagement – See on the class forum Homework (due Sun 10/27) [Link] Worked Example [Link] Content Note: the slides for this module have been updated. Please switch to the “slides” panel when viewing the video in Panopto. DO NOT stay on the “screen” panel, as the recorded […]
Module 08: Prediction & Supervised Machine Learning
Prepare (due Mon 10/28) Content below Canvas quizzes Class Participation – See on the class forum Homework (due Sun 11/3, late due 11/7) [Link] Worked Examples [Link] Content (Slides in Box) 08. A Predictive Modelling and Regression Ordinary Linear Regression and Intro Scikit-Learn (21 min.) Nonlinear Regression and Scikit-Learn Preprocessing (13 min.) Binary Classification with Logistic Regression (22 […]
Module 06: Combining Data
Prepare (due Mon 10/7) Content below Canvas quizzes Class participation – See on the class forum Homework (due Sun 10/20, late due Sun/27) [Link] Worked Example [Link] Content (Slides in the Box Folder) 06.A – Summarizing Data Read Section 3.8 Aggregating and Grouping from Python Data Science Handbook. Read Section 3.9 Pivot Tables from Python Data Science Handbook. 06.B […]
Module 05: Probability
Prepare (due Mon 9/30) Content below Canvas quizzes Class engagement – See on the class forum Homework (due Sun 10/6, late due Thurs 10/10) [Link] Worked Examples [Link] Content (Slides in the Box folder) 5.A – Foundations of Probability (52 min.) Outcomes, Events, Probabilities (15 min.) Joint and Conditional Probability (11 min.) Marginalization and Bayes’ Theorem (15 min.) Random […]
Module 03: Visualization
Prepare (due Mon 9/16) Content below Sakai quizzes Class engagement – See on the class forum Homework (due Sun 9/22) [Link] Worked Examples [Link] Content 03.A – Data Visualization and Design Why Visualize? (11 min.) Basic Plot Types (17 min.) Dos and Don’ts (10 min.) 03.B – Visualization in Python Intro to Python Visualization Landscape (7 min.) Seaborn Introduction (17 […]
Module 02: Numpy & Pandas
Prepare (due Mon 9/9) Content below Canvas quiz Class engagement – See on the class forum Homework (due Sun 9/15) [Link] Worked Example [Link] Content (Slides in the Box folder) 2.A – Numpy (1 hour) Why Numpy (8 min.) Numpy Array Basics (15 min.) Numpy Universal Functions (20 min.) Numpy Axis (14 min.) 2.B – Pandas (45 min.) Why Pandas (7 min.) […]
Module 01: Python & Jupyter Notebook
Prepare (due Mon 9/2) Content below if you need a refresher on Python Canvas quizzes Install Anaconda (see the Resources page for more instructions) Class engagement – See on the class forum Homework (due Sun 9/8, 11:59 PM, late due Sun 9/15) [Link] Content (Slides in the Box folder) 1.A – Python3 (14 min.) Python vs. Java (3 min.) Data […]
Optional Module: Git and Jupyter Notebooks
This module is 100% optional. It is intended as supplementary material if you plan to use git with your Jupyter Notebooks. Content A. Git Mental Model B. Git with notebooks, how? Recommended Reading Coding habits for data scientists
