Class plan, teaching objects, assignments, textbook, dates, classroom, office hour and more
This is a non-credit course. The only requirement is:
- Build up your resume with Python skills
- Have your individual data analytics project finished and post in student project showcase server (building in progress)
- Can talk about basic statistics, machine learning, natural language processing and deep learning in tech interview
- Know how to approach the analytics problem for take-home interview assignment
- Can teach other students and friends who ever want to learn programming
This would be a 10-week Introduction to Python for data analytics programming course for TC and Columbia students, every Sunday from 1 pm - 3 pm.
Location: 424 Horace Mann @Teachers College, Columbia University, 120th street cross with Broadway, NYC
More detail:
- Sep 23rd 1 - 3 pm Intro to Python, Data Structure, Data Clearning and Exploration
- Sep 30th 1 - 3 pm Intro to Statistics Analysis: Chi Square Test, Lineaer Regression (HW1: Research proposal)
- Oct 7th 1 - 3 pm Multivariate Analysis: Correlation, PCA and Visualization with Matplotlib and Seaborn
Oct 14th 1 - 3 pm Midterm - Prep
- Oct 21th 1 - 3 pm Intro to Machine Learning for Classification: Logistic Regression, SVM, and Naive Bayes (HW2: Data collection, clearning and exploration, HW1 due)
- Oct 28th 1 - 3 pm Machine learning topic: Trees, Random Forest, Gradient Boosting
- Nov 4th 1 - 3 pm Intro to deep learning: tensorflow & Keras (HW3: Data visualization and analysis, proposal hearing)
- Nov 11th 1 - 3 pm Deep learning topic: Computational Vision
- Nov 18th 1 - 3 pm Intro to natural language processing: nltk (HW4: Algorithm selection for your data analysis, HW3 due)
- Dec 2nd 1 - 3 pm NLP Topic: NLP with deep learning
- Dec 9th 1 - 3 pm Let's vote for the topic: Web scrabing, Web app development with Flask, Recommendation System, or Time series analysis (HW5: Final Write-up @conference paper format, HW4 due)
Dec 16th 1 - 3 pm Student Final Project presentation (HW1-5 project demo)
Each weekly topic could be extented into a semester course. This course is served as an introduction to the programming world. I bring you to the door and tell you how to look at the road signs. You need to walk along the path on your own.
There is no required textbook for this course, and so as the real world. This is a project-based learning course. Each of you might have individual specific interested industry and research method. For each track, I recommend textbooks as below:
Python Data Science Handbook: Essential Tools for Working with Data by VanderPlas
Hands-On Machine Learning with Scikit-Learn and TensorFlow by Géron
Deep Learning with Python by Chollet
Natural Language Processing with Python by Bird, Klein and Loper
Please reserve my office hour appointment in the link: https://calendar.google.com/calendar/selfsched?sstoken=UUVhd3J6RjJBeF92fGRlZmF1bHR8NTkzODQ0MTliYTQ4Y2QyOTg4YWFkZGYxMTVmOTNiNWE
Xiaoting Kuang, Ed.D Candidate in Teachers College, Columbia University, Adjunct Associate Faculty in School of Professional Studies, Columbia University. Python programmer, R statistician, iOS developer. Interested in natural language processing, knowledge mapping and cognitive science. Applied statistics, deep learning /neural network modelings, and machine learning methods for problem-solving. Email: [email protected]
Jiaxi Yang, Ph.D Candidate in Measurement and Evaluation in Teachers College, Columbia University. Email: [email protected]