Data Topics

Fall 2024 workshop information now available at:

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

This integrated calendar contains information on all open workshops offered by the New Brunswick Libraries. Topics include Python, R, Digital Humanities, GIS, NVivo, and the Data Science Basics Workshop Series. 

Tanya Khanna, Data Science Graduate Specialist, will be presenting the following workshops (Videos of the workshops will be added after the events):

Workshop materials (Jupyter notebooks and slides) are here:https://github.com/Tanya-Khanna/DataScienceWorkshop_Fall-2024_NBL

Fall 2024 Python/ML workshops - register at the link above

1. Introduction to Python Programming [Video]
This workshop is designed for beginners with little to no experience in programming, aiming to provide a rapid yet comprehensive introduction to the world of Python, one of the most popular and versatile programming languages today. Introduction to Jupyter Notebook and Google Collab. Learners will quickly grasp Python syntax, script execution, and fundamental constructs like variables, data types, and operators. They will also explore control structures like if-else statements, loops, and functions, gaining practical skills in data structures such as lists, tuples, sets, and dictionaries.

2. Advanced Python Programming [Video]
The workshop covers debugging and error handling in Python to help participants understand common programming errors and how to fix them. Object-oriented Programming. Functional Programming in Python: Concepts of functional programming: immutability, first-class functions, and pure functions. Using lambda, map, filter, reduce for functional style programming. One-two data structures.

3. Web Scraping with Python: Techniques and Ethics [Video]

This workshop will teach participants how to programmatically gather data from the internet using Python, focusing on legal and ethical considerations.

4. Mastering Data Analysis: Pandas and NumPy Essentials [Video]
This workshop is designed to equip learners with powerful tools for data analysis in Python.
Participants will delve into the world of NumPy, exploring its efficient arrays and array operations, which form the backbone of numerical computing in Python. The workshop then shifts to Pandas, where learners will get hands-on experience with its fundamental data structures - Series andDataFrame. This comprehensive session is ideal for anyone looking to enhance their data analysis skills, offering the tools needed to unlock insights from data with efficiency and precision.
 

5. Data Management with Python Workshop [Video]
Equip participants with the skills to effectively interact with both SQL and NoSQL databases using Python, highlighting the appropriate use cases and best practices for each.

6. Unveiling Data Stories: Python for Visualization and Exploration [Video]
This workshop is designed to guide participants through the process of revealing hidden stories in data using Python. It focuses on using Matplotlib, for effective exploratory data analysis (EDA). This workshop emphasizes the creation of engaging visual narratives, enabling participants to transform complex data insights into compelling and understandable visual formats. Interactive visualizations using libraries like Plotly, which allow for dynamic, browser-based graphs. Also, crafting narratives and storytelling with data - best practices for presenting data insights.

7. Introduction to Machine Learning: Supervised Learning [Video]
This workshop is tailored for beginners in machine learning. It focuses on supervised learning algorithms that are a cornerstone of machine learning, where the algorithm learns from labeled training data, helping to predict outcomes for unforeseen data. Classification and Regression will be introduced. Participants will learn about key algorithms like Linear Regression and ensemble methods like Random Forests and Gradient Boosting Machines for improved prediction accuracy.
 

8. Introduction to Machine Learning: Unsupervised Learning [Video]
This workshop is designed to introduce the concepts of unsupervised learning, a branch of machine learning where algorithms infer patterns from unlabelled data. The course covers clustering methods like K-means and DBSCAN, used to identify inherent groupings in data. It also explores dimensionality reduction techniques such as PCA, which simplify complex data sets while preserving
their key features. Additionally, the session introduces association rules, a method for finding interesting relationships within data sets. This workshop is ideal for those interested in learning how to extract insights from data without predetermined labels or categories.
 

9. Deploying Machine Learning Models
This workshops will equip participants with the knowledge and skills necessary to transition machine learning models from development to production, focusing on modern deployment technologies and practices.

10. Ethical AI and Responsible Data Science
This workshop will explore the ethical considerations in AI and data science, focusing on designing and implementing fair, transparent, and accountable systems.

11. Large Language Models and ChatGPT

This workshop offers a thorough exploration of cutting-edge language models, with a spotlight on ChatGPT. Attendees will delve into the design, training techniques, and practical uses of these models. Discussions on ethical usage and best practices will be a key part of the learning experience. By the workshop's end, participants will gain a deep understanding of large language models and how to effectively apply ChatGPT and similar technologies.

New! Integrated guide to all Spring 2024 workshop materials - slides, videos, and code available at

https://bit.ly/NBL_Data_Science_Workshops_Spring_2024

Tanya Khanna, Data Science Graduate Specialist, will be presented the following workshops:

Workshop materials (Jupyter notebooks and slides) are here: https://github.com/Tanya-Khanna/DataScienceWorkshop_2024_NBL/

1. Introduction to Python Programming [Video]:

2023 Python/ML workshop materials from Harshith Thonupunoori are available at

https://github.com/NBLGraduateSpecialistProgram/Python2022_Thonupunoori

Python Basics and Data Exploration ()This workshop will be an 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.

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

Machine Learning: Neural Networks (Video) This workshop will give an introduction to machine learning methods with neural networks.

Machine Learning: Deep Learning and Convolutional Neural Networks (Video) This workshop will discuss Deep Learning and Convolutional Neural Networks as used in Machine Learning.

Machine Learning: Decision Trees and Random Forests (Video) This workshop will give an introduction to machine learning methods with decision trees and random forests.

Code files and PDF handouts by Data Science Graduate Specialist Robert Palmere are available at

https://github.com/NBLGraduateSpecialistProgram/DataScience2022_Palmere

These cover the following topics:

1. Introduction to Python and Introduction to C (session 1_C)
2. Data Manipulation and Analysis with Python and Basic Data Manipulation and Analysis with C/C++ (session 2_C)
3. Data Visualization with Python and Data Visualization with C (session 3_C)
4. additional practice problems
5. Data Visualization with Python, continued and Object-oriented Programming with C++ (session 5_C)
6. Cython and Python
7. Data Mining in the Protein Data Bank
8. Small Applications Development with Python
9. Introduction to Machine Learning with Python
10. Molecular Dynamics with Python

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

There are two separate series of Python workshops listed here, 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)

• Recording of Session (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.

Python Basics and Data Exploration (Beginners 1) 

• Recording of Session (Instructor, Sly Zhong)

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.

• Recording of Session  (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 Manipulation and Analysis with Python (Beginners 2)

• Recording of Session (Instructor, Sly Zhong)

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.

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

• Recording of Session  (Instructor, Sanket Badhe)

Data Visualization (Beginners 3)

• Recording of Session (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. 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

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

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.

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].

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

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.

Further additions to the workshop content, including topics on statistical inference, machine learning, and HPC with Amarel, were added by Sanket Badhe (Github) and Ziqiu (Sly) Zhong (Github), Quantitative Data Graduate Specialists from Fall 2019 to Fall 2020.

From Spring 2021 to Spring 2022, Robert Palmere (Github) served as Data Science Graduate Specialist, contributing Python and C workshop content.

From Fall 2022 to Fall 2023, Harshith Thonupunoori served as Data Science Graduate Specialist, contributing Python workshop content.  See his Github page.

Also see the general Github archive for prior NBL Graduate Specialist Material, and the Data Science Basics page for additional coverage of Python and other data tools.

Workshop recordings and other materials more than five years old will be removed from this guide.