Workshop | Introduction to Data Analysis for Aspiring Data Scientists: Part 3

Join us for a four part learning series: Introduction to Data Analysis for Aspiring Data Scientists. This is the third of four online workshops for anyone and everyone interested in learning about data analysis. No previous programming experience required.

Part 3: Machine Learning with scikit-learn

Abstract: scikit-learn is one of the most popular open-source machine learning libraries among data science practitioners. This workshop will walk through what machine learning is, the different types of machine learning, and how to build a simple machine learning model. This workshop focuses on the techniques of applying and evaluating machine learning methods, rather than the statistical concepts behind them. We will be using data released by the New York Times (https://github.com/nytimes/covid-19-data). Prior basic Python and pandas experience is required.

Who should attend this workshop: Anyone and everyone, CS students and even non-technical folks are welcome to join. Please note, prior basic Python experience is recommended.

What you need: Although no prep work is required, we do recommend basic python knowledge. RSVP for Part 1 to learn about Python: https://www.meetup.com/data-ai-online/events/269814565/

LINK TO JOIN: https://databricks.zoom.us/j/507746308

Agenda: 10AM PDT - 11AM PDT (GMT-8)

10:00AM - 10:45AM - Workshop led by Niall Turbitt
10:45AM - 11:00AM - Q&A

Watch Part 1: Introduction to Python on Databricks - https://youtu.be/HBVQAlv8MRQ
Watch Part 2: Data Analysis with pandas - https://youtu.be/riSgfbq3jpY
RSVP Part 4 April 29: https://www.meetup.com/data-ai-online/events/270166620/

Instructor: Niall Turbitt is a Data Scientist on the Machine Learning Practice team at Databricks. Working with Databricks customers, he builds and deploys machine learning solutions, as well as delivers training classes focused on machine learning with Spark. He received his MS in Statistics from University College Dublin and has previous experience building scalable data science solutions across a range of domains, from e-commerce to supply chain and logistics.

TA: Amir Issaei is a Senior Data Science Consultant at Databricks, where he educates customers on how to leverage the company’s Unified Analytics Platform in machine learning (ML) projects. He also helps customers implement ML solutions and use advanced analytics to solve business problems. Previously, he worked in the Operations Research Department at American Airlines, where he supported the Customer Planning, Airport, and Customer Analytics Groups. He holds an MS in mathematics from the University of Waterloo and a BE in physics from the University of British Columbia.

TA: Kelly O’Malley is a Solutions Engineer at Databricks where she helps startups architect and implement big data pipelines. Prior to joining Databricks she worked as a Software Engineer in the defense industry writing network code. She completed her BS in Computer Science at UCLA. Outside of the tech world, Kelly enjoys cooking, diy projects, and spending time at the beach.

TA: Brooke Wenig is the Machine Learning Practice Lead at Databricks. She guides and assists customers in implementing machine learning pipelines, as well as teaching Distributed Machine Learning & Deep Learning courses. She received an MS in Computer Science from UCLA with a focus on distributed machine learning. She speaks Mandarin Chinese fluently and enjoys cycling.