AI is all the rage right now, and many people are looking at starting a career in the field. Whether you’re already experienced with programming to a degree or you’re coming from a totally different background, there are a number of core skills that you’ll need to know to get started with AI. Here, we outline nine different sessions coming to ODSC East this April 23-25 that will help you get started with a career in AI.

Idiomatic Pandas

Matt Harrison | Python & Data Science Corporate Trainer, Consultant | MetaSnake

Pandas can be tricky, and there is a lot of bad advice floating around. This tutorial will cut through some of the biggest issues the speaker has seen with Pandas code after working with the library for a while and writing three books on it.  We will discuss proper types, chaining, aggregation, debugging, and more.

Build Conversational AI and Integrate it Into Product Pages Using Watsonx Assistant

Tommy Chaoping Li and James Busche | Senior Software Developers | IBM

IBM watsonx Assistant is an AI-powered virtual agent that provides customers with fast, consistent, and accurate answers across any messaging platform, application, device, or channel. Using AI and natural language processing, watsonx Assistant learns from customer conversations, improving its ability to resolve issues the first time while removing the frustration of long wait times, tedious searches, and unhelpful chatbots.

This workshop provides an easy-to-follow guide on how to launch Watsonx Assistant, configure the settings for your first AI Assistant, and integrate AI Assistant into a product page.

Introduction to scikit-learn: Machine Learning in Python

Thomas J. Fan | Senior Machine Learning Engineer |

Scikit-learn is a Python machine learning library used by data science practitioners from many disciplines. We start this training by learning about scikit-learn’s API for supervised machine learning. First, we learn the importance of splitting your data into train and test sets for model evaluation. Then, we explore the preprocessing techniques on numerical, categorical, and missing data. We see how different machine learning models are impacted by preprocessing. After this training, you will have the foundations to apply scikit-learn to your machine learning problems.

Introduction to Math for Data Science

Thomas Nield | Instructor, Founder | University of Southern California, Nield Consulting Group and Yawman Flight

In this training, Thomas Nield will provide a crash course of carefully curated topics to jumpstart proficiency in key areas of mathematics. This includes probability, statistics, hypothesis testing, and linear algebra. Along the way, you’ll integrate what you’ve learned and see practical applications for real-world problems. These examples include how statistical concepts apply to machine learning, and how linear algebra is used to fit a linear regression. We will also use Python to explore ideas in calculus and model-fitting, using a combination of libraries and from-scratch approaches.

Data Automation with LLMs

Rami Krispin | Senior Manager – Data Science and Engineering | Apple

In today’s business environment, data plays a crucial role in decision-making. However, obtaining the required data can be challenging due to data engineering or data science resource constraints, leading to delays, inefficiency, and potential losses. This talk will focus on creating a self-serve bot (e.g., Slack bot) that can serve data requests and support ad-hoc requests by leveraging LLM applications. This involves building a natural language to SQL engine using tools such as OpenAI API or open-source models that leverage the Hugging Face API.

Build-a-Byte: Constructing Your Data Science Toolkit

Jarai Carter, PhD | Senior Manager | John Deere

Are you getting started in data science and want to know more about what tools are out there? Or maybe you have used the same tools for a while and you want to try something new? Then, get ready for a deep dive into building your very own data science toolkit! This talk is aimed at beginners who are entering this dynamic field and want to expand their data science tool knowledge. I will highlight 8 important components of a data science toolkit (since there are 8 bits in a byte), such as programming languages, integrated development environments (IDEs), text editors, online resources, and more.

At the end of the talk, you can expect to leave with not only a fundamental understanding of each toolkit component but also practical insights and resources so you can embark on your own toolkit journey. So, join me in constructing your data science toolkit and build those data masterpieces!

A Practical Introduction to Data Visualization for Data Scientists

Robert Kosara | Data Visualization Developer  | Observable

How does data visualization work, and what can it do for you? In this workshop, data visualization researcher and developer Robert Kosara will teach you the basics of how and why to visualize data, and show you how to create interactive charts using open-source tools. A few things you’ll learn include the fundamental building blocks of data visualization: visual variables, data mappings, etc; the difference between continuous and categorical data, and what it means for data visualization and the use of color;  what grammars of graphics are (the ‘gg’ in ‘ggplot’!) and how they help make more interesting visualizations, and more.

Introduction to Linear Regression using Spreadsheets with Real Estate Data

Roberto Reif | CEO and Founder | ScholarU

Over the course of this session, we’ll embark on a deep dive into the foundational principles of linear regression, a statistical machine learning model that aids in unraveling the intricate relationships between two or more variables. Our unique focus centers on the practical application of linear regression using real-world real estate data, offering a concrete context that will undoubtedly resonate with participants. The workshop kicks off with a thorough overview of linear regression concepts, ensuring a collective understanding of the fundamentals. As we progress, we transition into the practical realm, employing popular spreadsheet tools like Excel or Google Sheets to conduct insightful real estate data analyses. Participants will master the art of data input, application of regression formulas, model building, and interpretation of results, enriching their analytical toolkit.

Introduction to Machine Learning with Python

Sudip Shrestha, PhD | Data Science Lead/ Sr. Manager | Asi Government

This session is designed for those seeking to understand the growing field of machine learning (ML), a key driver in today’s data-centric world. This training offers foundational knowledge in ML, emphasizing its importance in various industries for informed decision-making and technological advancements.

Participants will learn about different ML types, including supervised and unsupervised learning, and explore the complete lifecycle of an ML model—from data preprocessing to deployment. The course highlights Python’s role in ML, introducing essential tools and libraries for algorithm implementation.

A practical component involves hands-on implementation of an ML use case, consolidating theoretical knowledge with real-world application. Ideal for beginners, this course provides a comprehensive yet concise introduction to ML, equipping attendees with the skills to apply ML concepts effectively in diverse scenarios.

Sign me up!

These of course aren’t the only immersive sessions coming to ODSC East this April 23-25! We have over 200 sessions in total across a dozen different tracks, so once you’re comfortable with the basics, you can move on to specialized sessions like machine learning, deep learning, generative AI, and plenty more. Tickets are running out, so get yours now!