To download zipped files from GitHub repositories, click on the green "Clone or download" button on the upper right section of the repository page. Use Jupyter Notebook to open the .ipynb files in an interactive environment.
Data is all around us - in every industry and academic field, behind every online purchase recommendation and driving route calculation. Sometimes we have more data than we know what to do with. If solving data problems intrigues you (or if you just need some data for a class project...), check out the links below.
Workshops are offered in either Alexander Library or LSM (with identical content). Participants in LSM-based workshops must bring their own laptops. At Alexander, you can either bring your own laptop, or use the desktops in the lab.
This workshop will continue with Numpy and Panda libraries. Data visualization with matplotlib, a popular plotting library in Python, will also be covered.
Statistical Inference with Python
In this workshop, we will explore basic principles behind using data for estimation and for assessing theories. The workshop will focus on inference procedures, constructing confidence intervals, and hypothesis testing.
This workshop delves into a wider variety of basic supervised learning methods for both classification and regression (Linear Regression, Logistic Regression, Naive Bayes, k-nearest neighbor). In the last part, we will discuss unsupervised learning techniques namely k-Means, PCA. We will apply all techniques on a dataset and compare each of these techniques in terms of accuracy, inference, etc.
This workshop focuses on advanced supervised learning methods for both classification and regression (Decision Tree, Random Forest, Support Vector Machine, Ensemble learning, Neural Network). We will apply all these techniques on a dataset and compare the results of each technique.
An API, or application programming interface, is a common tool for interacting with data on the web. This workshop will present how APIs are used in Finance (Equity and Cryptocurrency) and Economics (FRED) industry.
Python Basics and Data Exploration
This workshop will be an accelerated introduction to fundamental concepts such as variable assignment, data types, basic calculations, working with strings and lists, control structures (e.g. for-loops), functions.
Data Manipulation and Analysis with Python
In this workshop, we will dive into the world of arrays and data frames using the NumPy and pandas libraries. We'll cover data cleaning and pre-processing, joining and merging, group operations, and more. If you work with tabular data, this workshop is for you!
Data Visualization and Machine Learning with Python
Interested in finding patterns and predicting unknown attribute values in your data? Join us for an overview of machine learning techniques implemented using the scikit-learn library. We'll also learn how to do data visualization with matplotlib, a popular plotting library in Python.
Three popular options for installing Python on your computer:
 S. Byrnes, "Python for scientific computing: Where to start," Steve Byrnes's Homepage, Oct. 2017. [Online]. Available: http://sjbyrnes.com/python/. [Accessed 27 Apr. 2018].
Since Python is open source, there are abundant online resources to help learners find their way around the language. If you have a specific programming task you need help to achieve, a Google search is often the best way to start. Here is a list of resources you may find helpful if you're interested in a particular topic!
General Python Learning
Visualizing Code Execution
Specific Topics in Python
NumPy and Pandas (Data Manipulation & Analysis)
This guide was originally created by Miranda So as the inaugural cohort of the Graduate Specialist Program. To follow Miranda's work, take a look at her GitHub page here.
Hang Miao served as Quantitative Data Graduate Specialist for the 2018-2019 Academic Year, and updated and expanded the workshop content. To follow Hang's work, see his Github page.
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