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Graduate Specialist Program (New Brunswick Libraries): Python

Home page for the New Brunswick Libraries' Graduate Specialist Program

Python Workshop Materials

Provide feedback for Python workshops here

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, Data Everywhere

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.

Fall 2019 Workshop schedule

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.

☞ RSVP for the Python workshops

 

Libraries and Data Visualization

  • Tuesday, November 5,  1:00pm-2:30pm  LSM Conference Room (Instructor, Sly Zhong)

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

  • Friday, November 8, 1:00pm-2:30pm Alexander Library, Room 415 (Instructor, Sanket Badhe)

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.

Python in Finance - Cryptocurrency Comparison Project

  • Tuesday, November 12,  1:00pm-2:30pm  LSM Conference Room (Instructor, Sly Zhong) 

Utilizing numpy, pandas and matplotlib, this workshop will show how to make a program that can compare the price, Log Returns, SMA (Simple Moving Average) of Bitcoin and Ethereum, and predict which one is a better investment choice with Python.  

Data Science with Python, part 1

  • Friday, November 15, 1:00pm-3pm Alexander Library, Room 415 (Instructor, Sanket Badhe)

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. 

Data Science with Python, part 2

  • Friday, November 22, 1:00pm-3pm Alexander Library, Room 415 (Instructor, Sanket Badhe)

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.

Interaction with API in Economics

  • Tuesday, November 26,  1:00pm-2:30pm  LSM Conference Room (Instructor, Sly Zhong) 

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. 

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PAST WORKSHOPS

Python Basics and Data Exploration

  • Tuesday, October 8,  1:00pm-2:30pm  LSM Conference Room (Instructor, Sly Zhong)
  • Friday, October 11, 1:00pm-2:30pm Alexander Library, Room 415 (Instructor, Sanket Badhe)

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

  • Tuesday, October 15,  1:00pm-2:30pm  LSM Conference Room (Instructor, Sly Zhong)
  • Friday, October 18, 1:00pm-2:30pm Alexander Library, Room 415 (Instructor, Sanket Badhe)

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

  • Tuesday, October 29,  1:00pm-2:30pm  LSM Conference Room (Instructor, Sly Zhong)
  • Friday, October 25, 1:00pm-2:30pm Alexander Library, Room 415 (Instructor, Sanket Badhe)

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.

Getting Started

Three popular options for installing Python on your computer:

  1. Download Anaconda at https://www.anaconda.com/download/. Anaconda comes with many useful packages including different integrated development environments (Jupyter, Spyder, etc.), libraries for analytics and scientific computing (NumPy, SciPy, pandas, etc.), libraries for visualization (matplotlib, bokeh, etc.), and libraries for machine learning (such as scikit-learn). The installation should only take ~ 5 minutes, but it is a fairly large software package (~ 3 GB), so make sure you have enough disk space.
  2. Download WinPython from https://winpython.github.io/. WinPython is similar to Anaconda; it has the added benefit that it can be run off a USB stick if you are using a public computer and can't install new programs, but it may have fewer included packages than Anaconda [1].
  3. Download directly from https://www.python.org/downloads/. Use IDLE shell and editor. Comes with standard library, but will need to install libraries such as NumPy, SciPy, matplotlib, etc. 
  4. Use Google colab through your scarletmail or personal gmail (https://colab.research.google.com/) , Google colab is Jupyter notebook environment that requires no setup to use and runs entirely in the cloud.

 

[1] 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].

Learning Resources

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)

Data Visualization

Machine Learning

Quantitative Data Graduate Specialist

Quantitative Data Graduate Specialist

Credits

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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