Welcome to the intro-level DS class, where we will learn about python basics and how to use python for exploratory data analysis. Hope you'll enjoy the class and learn something from it.
If you can, try to go throug the following reading and set up your local environment before class:
- Anaconda set up
- Git introduction
- Set up you forked repo for commit and push + Homework submisstion instructions
Note: If you're getting the following error while cloning or pushing to GitHub, try to set up PAT following this instruction.
Error message:
remote: Support for password authentication was removed on August 13, 2021. Please use a personal access token instead.
remote: Please see https://github.blog/2020-12-15-token-authentication-requirements-for-git-operations/ for more information.
fatal: unable to access 'https://github.com/DS-XL/ds-intro-class-2022.git/': The requested URL returned error: 403
You can run python in different settings, for example, you can use jupyter notebook for interactive exploration, use interpreter in command line by typing python in terminal (you'll see >>> prompt appear), or run python script in command line by python <your_script>.py. We will be using notebooks for the class as it's easy to follow with markdown and easy to interact with.
0.0 Introduction + Setting expectations for the class
0.1 Environment set-up (material in section 0)
0.2 Git quick intro
1. Assign values to variables and simple arithmetics
2. `Print` and simple string manipulation
3. Value comparison and conditions using `if-elif-else`
4. Collections: list, tuple, set, and dictionary
* HW01
* Git - Cloning, Committing, Pushing, and Pull Request
5. Iteration: loops and comprehensions
6. Writing functions
6. Writing functions (cont'd)
7. Reading and writing files
7. Reading and writing files (cont'd)
8. Intro to code complexity and performance (high level)
9. Coding challenge example using HackerRank and LeetCode
* [kick off] Intro to Pandas
1. Data exploration: Intro to `pandas`
2. Data wrangling basics
2. Data wrangling basics
3. Using `pandas` for EDA(exploratory data analysis)
3. Using `pandas` for EDA(exploratory data analysis)
4. Plotting in python
4. Plotting in python, EDA example
5. [Advanced EDA topics] -- if time permits
6. Mock take-home case study
Time Series Data Introduction and Analysis
class2):
Time Series Data Wrangling
class3):
Traditional Time Series Models
class4):
Advanced Time Series Models
Time Series Model Cross Validation and Hyperparameter Tuning.
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