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Graduate Specialist Program (New Brunswick Libraries): Python

Home page for the New Brunswick Libraries' Graduate Specialist Program

Python Workshop Materials

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.

 

Workshop Materials for Sly Zhong

Download from Google Drive

Download from Github

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.

Workshop Schedule and Materials

Fall 2020 workshop information now available at

NBL Workshop Calendar - https://libcal.rutgers.edu/nblworkshops

This integrated calendar contains information on all open workshops offered by the New Brunswick Libraries. Topics include Python, R, Digital Humanities, GIS, NVivo, Amarel/HPC, and more tools to help you with your research in the Science Workshop series. 

There are two separate series of Python workshops, with different instructors and different content.  Sly Zhong's series is more geared to beginners with the language (labeled "Beginners"), while Sanket Badhe's series will move at a faster pace (labeled "Accelerated").

YouTube Playlist for all of Ziqiu (Sly) Zhong's Python workshop series.

Python Basics and Data Exploration (Accelerated 1)

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.

Python Basics and Data Exploration (Beginners 1) 

This workshop will be a more deliberate 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 (Accelerated 2)

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 Manipulation and Analysis with Python (Beginners 2)

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.

Data Visualization  with Python (Accelerated 3)

This workshop will give an introduction to data visualization with matplotlib and seaborn library, popular plotting libraries in Python.

Data Visualization (Beginners 3)

This workshop will continue with Numpy and Panda libraries. Data visualization with matplotlib, a popular plotting library in Python, will also be covered. Turn data into line, bar, scatter plots etc. Environmental Science and Economics data will be used and examples.scikit-learn library. We'll also learn how to do data visualization with matplotlib, a popular plotting library in Python.

Cryptocurrency API, Visualization, and Comparison project

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. A real-time cryptocurrency interactive API will also be introduced in this workshop.
 

Statistical Hypothesis Tests - Basic Concepts and Implementation 

This workshop delves into a wider variety of basic and most commonly used statistical tests including Null Hypothesis Testing, Critical Value, p-value, Z-test, T-test and Chi-Square Test etc. We will also introduce some examples about how to implement those tests with given database. 

Intro to Tableau 1

The workshop will introduce the basics of using Tableau for Data Visualization. Design principles of quantitative and qualitive presenting and meaningful display methods. 

Statistical Inference with Python

Recording of Session (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.

 

Supervised Learning - Regression

Instructor, Sanket Badhe

In this workshop, we will give an introduction to machine learning, supervised learning and unsupervised learning. Next, we will discuss different methods for train and test split. Finally, we will deepen our understanding of regression, specifically simple linear regression, multiple linear regression.

 

Supervised Learning - Classification 1

Recording of Session (Instructor, Sanket Badhe)

Instructor, Sanket Badhe

This workshop will first give an introduction about classification problems and then discuss classification algorithms such as K Nearest Neighbour, logistic regression. The latter half of the workshop will focus on classification metrics such as Confusion Matrix, Accuracy, Precision, Recall etc. 

 

Exercise and Practice in Python (Beginner 4)

This workshop will go over some exercises and practice questions using Python for beginners. If you’re starting out with Python, this workshop is a good way to test your knowledge and learn how to make some small programs.

Intro to Tableau 2

 

More Tableau functions and data visualization options will be covered in this workshop. 

 

Data Science with Python, part 2

 

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

 

Neural Networks

This workshop describe Neural Network techniques for data analysis.

Interaction with API in Economics

 

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.Cryptocurrency) and Economics (FRED) industry. 

 

Statistical Hypothesis Tests in Python/SAS/R

This workshop will introduce how to run most commonly used statistical tests in different programming languages including Python and R and show comparison of each of the languages. 

Spark Introduction

This workshop will introduce you to  pyspark, its features and components.

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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