Workshop Materials for Data Science at Rutgers-New Brunswick Libraries.
Topics covered, by workshop number are:
1. Introduction to Python Programming:
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. 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. Additionally, the workshop covers file handling and text processing.
2. Mastering Data Analysis: Pandas and NumPy Essentials:
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 and DataFrame. 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.
3. Unveiling Data Stories: Python for Visualization and Exploration:
This workshop is designed to guide participants through the process of revealing hidden stories in data using Python. It focuses on using Matplotlib and Seaborn, two prominent visualization tools, 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.
4. Mathematical Foundations for Data Science:
This workshop offers a brief yet comprehensive overview of essential mathematics for data science. It covers foundational statistics and probability, crucial for model understanding, and basic hypothesis testing techniques. It also introduces linear algebra concepts like vectors and matrices, alongside fundamental calculus for derivatives and integrals.
5. Introduction to Machine Learning: Supervised Learning:
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 Decision Trees, exploring how these methods enable machines to learn from and make predictions based on data.
6. Introduction to Machine Learning: Unsupervised Learning:
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.
7. Introduction to Deep Learning:
This workshop offers an introduction to the fundamentals of deep learning, a highly influential branch of artificial intelligence. This session focuses on the core concepts of neural networks, including feedforward neural networks, the simplest type of artificial neural network architecture. The course also covers convolutional neural networks (CNNs), essential for image and video recognition, and recurrent neural networks (RNNs), which are crucial for handling sequential data like text and speech.
8. Deep Dive into Natural Language Processing:
Are you eager to learn how to communicate with computer systems using Natural Language Processing (NLP) techniques, or to make machines understand human sentiments? Do you aspire to build intelligent applications like Siri, Alexa, or chatbots, even if you're starting from scratch? This workshop introduces Natural Language Processing (NLP), teaching you to preprocess text, analyze sentiments, model topics, and use language generation models. It's perfect for anyone eager to build applications that interact naturally with human language.
9. 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.