Keynote by Emily Robinson on Creating a Strong Data Science Portfolio


Details
For June Data Matters, we're excited to be joined by Emily Robinson! She'll be telling us more about how to make a strong data science portfolio which will help you with your data career development. Don't miss it.
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PROGRAMME
18:30-19:00 Sign-in and get settled
19:00-19:45 Emily's presentation
19:45-20:00 Q&A
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Creating a Strong Data Science Portfolio
As data science explodes in popularity, more and more people are vying for entry-level data science jobs. How can you stand out from the crowd? If you're a junior or mid-level data scientist, how can you continue to grow your skills, meet other data scientists, and share what you've learned?
In this talk, Emily will show you how you can make a strong data science portfolio, including by giving talks, contributing to other's open source projects, writing tutorials, and building side projects, that will accelerate your career.
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About the Speaker
Emily Robinson
Emily is a data scientist at DataCamp, where she's built their A/B testing analytics system and analyzed over 100 experiments. Previously, she worked at Etsy as a data scientist where she worked with their search team to design, implement, and analyze experiments on the ranking algorithms, UI changes, and new features.
She's also writing a book with Jacqueline Nolis, Build Your Career in Data Science, which covers how to land and negotiate a great job offer, write an impactful analysis for your company, deal with stakeholders, and become a manager or independent consultant.
FREE TICKETS
Attendance is free for registered delegates, and you can register here: https://skillsmatter.com/meetups/11783-keynote-by-emily-robinson-on-creating-a-strong-data-science-portfolio
WHAT, WHEN, WHERE
WHAT: Date Matters: Keynote by Emily Robinson
WHEN: Monday 24th June
WHERE: Skills Matter, CodeNode
REGISTRATION(FREE): https://skillsmatter.com/meetups/11783-keynote-by-emily-robinson-on-creating-a-strong-data-science-portfolio

Keynote by Emily Robinson on Creating a Strong Data Science Portfolio