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How to Use Anaconda Notebooks 00:01:02
- Overview
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Author Introduction 00:01:38
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Course Overview 00:01:44
- Build Your Data Science Portfolio
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Data Science Portfolio Basics 00:05:31
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Showcase Your Skills 00:08:01
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Showcase Your Skills Continued 00:03:51
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Anaconda Notebooks 00:03:29
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Present Your Data Science Project with Notebooks 00:10:43
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Tell a Compelling Story With Data 00:03:05
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Storyboarding 00:05:57
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Storyboarding Example: Wind Turbine Dashboard 00:04:18
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Storyboarding Exercise 00:01:54
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Options for Sharing Code 00:04:21
- Conclusion
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Conclusion 00:01:22
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End of Course Survey
Build Your Data Science Portfolio
Best practices and strategies to launch your data science career.
This course is designed to prepare data scientists to stand out when applying for their first data science role. We will cover portfolio development best practices to demonstrate you have the right skills and experience and explain how to present yourself and your achievements (LinkedIn, certifications, personal site) so that hiring managers and recruiters will notice you. We will also explore how to use the Anaconda notebooks and data catalog to quick-start your data science project and find useful data sets.
What you'll learn—and how you can apply it
By the end of this hands-on course, you’ll understand:
- Why you need a portfolio
- Best practices for developing a data science portfolio
- What a good data science portfolio looks like
- How to start a data science project and get data sets
- How to present yourself to stand out as a candidate
- How to showcase your portfolio
And you’ll be able to:
- Curate your projects and online presence to attract hiring managers
- Find useful data sets to start your project
- Evaluate data science portfolios
- Develop a portfolio that follows best practices
- Tailor the portfolio to get your dream data science job
This training is for you because…
- You want an overview of the data science industry and its career trends.
- You’ve taken some data science courses but don’t have professional experience.
- You want to switch careers.
- You want to become a data scientist.
Prerequisites
- You’ve heard of high-level data science concepts, such as Notebooks, Python, data analysis, data cleaning, data visualization, data engineering, and machine learning.
- You’ve studied one or more of those areas and are ready to start building your portfolio.
About the instructor
Jose Mesa is a Senior Software Engineer at Anaconda who supports development, data science, modeling, analytics, and educational partnerships (outreach). Before joining Anaconda, Jose was a Facilities Engineer at Chevron's Subsea, Civil, and Marine Engineering unit, where he integrated data science techniques in the design optimization, analysis, and real-time monitoring of offshore structures. He received his Ph.D. from the University of Michigan Naval Architecture and Marine Engineering (NA&ME) department and held two MSE degrees in Aerospace Engineering and NA&ME. He completed his dual Bachelor's in Civil Engineering and Land Surveying at the University of Puerto Rico.
Questions? Issues? Contact [email protected].