Data Science Practicum

COM SCI 800.003
This project-based Data Science Practicum provides students with the opportunity to gain real-world experience working with our industry partners. Each practicum cohort is sponsored by a company or organization.
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What you can learn.

  • Gain real-world experience by working with our industry partners on data science projects
  • Gain analytics experience and reconcile mathematical theory with business practice
  • Use data science to identify, define, and analyze business problems

About this course:

The Spring 2018 Practicum will run from April 2 - June 9, 2018
Meeting schedule:

Mondays, 6:30 pm - 9:30 pm
Wednesdays, 6:30 pm - 9:30 pm
Saturdays, 10 am - 1 pm

This project-based Data Science Practicum provides students with the opportunity to gain real-world experience working with our industry partners. Each practicum cohort is sponsored by a company or organization. This collaboration allows students to work with partner companies/organizations to gain analytics experience and reconcile mathematical theory with business practice. Student groups — supervised by a UCLA Extension practicum instructor — work with the practicum company/organization to identify, define, scope, and analyze a business problem. Students will work in groups to solve real-world data analysis problems and communicate their results. Innovation and clarity of presentation will be key elements of evaluation.

It is assumed that students participating in this practicum have a thorough knowledge of basic machine learning concepts (classification, clustering, regression, dimensionality reduction, etc) and are proficient in R or Python. Students will be working on a real-world data science project from Day 1 of this Data Science Practicum. Very little time will be spent on lectures or introducing new machine learning concepts or explaining basic constructs of programming languages.

For the Spring 2018 cohort, students accepted into the practicum will be working on a real-world project to collect/scrape big data in real-time for all the regions in the U.S. and China. The data will be cleaned, standardized, and properly aggregated, measured, managed to become real-time indicators of various economic activities at the local and national levels. In countries like China, economic statistics are not available in real-time, available with a significant delay, or not trust-worthy. This project will focus on how to turn the collected big data into useful indicators to be comparable to the official economic statistics issued by government agencies, in retrospect. The indicators will be similar to GDP, payroll employment, housing prices, etc.

Spring 2018 Practicum Instructor: Dr. William Yu, UCLA Anderson Forecast
William Yu joined the UCLA Anderson Forecast in 2011 as an economist. At Forecast, he focuses on the economic modeling and forecasting of the Los Angeles economy. He also conducts research and forecast on China’s economy and its impacts on the US economy. Click here for more about William Yu. 

 

For students without previous experience in data science, we recommend completing our Data Science specialization. The Data Science specialization can be completed in as little as 10 weeks in our 10-Week Data Science Camp. Click here for more information.

 

Suggested Prerequisites

It is advisable that you complete the following (or equivalent) since they are prerequisites for Data Science Practicum.

For students without previous experience in data science, we recommend completing our Data Science specialization. The Data Science specialization can be completed in as little as 10 weeks in our 10-Week Intensive Data Science Camp. Click here for more information. 

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