Data Science Techniques and Algorithms

This workshop will cover some of the most common algorithms for general data science and the best practices that go along with data science as a whole. Specifically, we'll discuss feature engineering, cover a range of important algorithms and techniques such as Cross Validation, Principal Component Analysis, and K-Nearest Neighbors. Emphasis will be placed on the process and pipeline that one may follow while encountering a new data science project.

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Please fill the sign-up sheet below https://forms.gle/cVtJpZYyQNsJDKqM9

Installation

1. While in your command line, move to a directory that you want to clone the workshop into.
2. Simply type git clone https://github.com/delug/Workshop3.git in your command line to clone the repository
3. Run jupyter notebook and navigate to where you cloned the workshop repository
4. Open the notebook and enjoy!

Note: Before the workshop, please make sure you have the most up-to-date version of this repository. This can be assured by running git pull within the repository close to the workshop day. Preferably the day of, just to be safe!

Required Software

Before coming to the workshop, please ensure that you have the following softwares downloaded:

1. Python (We recommend downloading Python along with Anaconda: https://www.anaconda.com/distribution/)
2. Jupyter (https://jupyter.org/install)
3. SKlearn (In conda, enter: conda install scikit-learn)
4. Git (https://git-scm.com/downloads)
5. Pandas (In conda, enter: conda install pandas)
6. Seaborn (In conda, enter: 'conda install seaborn')

Feedback

Deep Learning at UGA is a club that began as a small organization and is rapidly expanding to service as many people as possible. This is a difficult task, as we're often breaking new ground and sometimes it shows. We want to ensure that everything we offer is of the highest possible quality, but that requires help from you! If you've got a spare second, it would mean a lot if you could take the survey below to share your feedback with us. We go through every single response and work to meet your needs. Please fill out the survey in the link below!

https://tinyurl.com/y39b87uy

Workshop Series

1. Intro to Python, Git, and Data Science

2. The Mathematics Behind Data Science

3. Data Science Techniques and Algorithms

4. Intro to Neural Networks

5. Layers, Modules & More

6. Neural Models and Architectures