OpenCourser brand icon
Sign in
We may earn an affiliate commission when you visit our partners.

Using Embeddings to Understand the Variance and Evolution of Data Science Skill Sets

WIA

Using Embeddings to Understand the Variance and Evolution of Data Science Skill Sets with Maryam Jahanshahi at the 2019 Women in Analytics Conference.

Using Embeddings to Understand the Variance and Evolution of Data Science Skill Sets with Maryam Jahanshahi at the 2019 Women in Analytics Conference.

This course is no longer available. Find a similar course by searching these:
embeddings variance evolution data science skill sets machine learning natural language processing

What's inside

Syllabus

Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Learners who are interested in the evolution of data science skills and variance will find this specialization relevant

Save this course

Create your own learning path. Save this course to your list so you can find it easily later.
Save

Activities

Coming soon We're preparing activities for Using Embeddings to Understand the Variance and Evolution of Data Science Skill Sets. These are activities you can do either before, during, or after a course.

Career center

Learners who complete Using Embeddings to Understand the Variance and Evolution of Data Science Skill Sets will develop knowledge and skills that may be useful to these careers:
Data Scientist
Data Scientists use methods from various fields (math, statistics, computer science, information science, etc) to extract knowledge and insights from data in various forms, both structured and unstructured, and provides this information to help decision makers.
See salaries and explore the career path for Data Scientist
Machine Learning Engineer
Machine Learning Engineers use methods from various fields (math, statistics, computer science, information science, etc) to extract knowledge and insights from data in various forms, both structured and unstructured, and provides this information to help decision makers.
See salaries and explore the career path for Machine Learning Engineer
Data Analyst
Data Analysts use methods from various fields (math, statistics, computer science, information science, etc) to extract knowledge and insights from data in various forms, both structured and unstructured, and provides this information to help decision makers.
See salaries and explore the career path for Data Analyst
Data Engineer
Data Engineers design and build the infrastructure and systems that allow Data Scientists to use to extract knowledge and insights from data in various forms, both structured and unstructured, and provides this information to help decision makers.
See salaries and explore the career path for Data Engineer
Business Analyst
Business Analysts use methods from business and technology to solve problems and improve decision-making.
See salaries and explore the career path for Business Analyst
Product Manager
Product Managers use methods from business and technology to build and launch new products and services.
See salaries and explore the career path for Product Manager
Software Engineer
Software Engineers design, develop, and maintain software systems.
See salaries and explore the career path for Software Engineer
Statistician
Statisticians use methods from mathematics, statistics, and computer science to collect, analyze, and interpret data.
See salaries and explore the career path for Statistician
Operations Research Analyst
Operations Research Analysts use methods from mathematics, statistics, and computer science to solve problems in business and industry.
See salaries and explore the career path for Operations Research Analyst
Financial Analyst
Financial Analysts use methods from finance, economics, and computer science to analyze financial data and make investment recommendations.
See salaries and explore the career path for Financial Analyst
Actuary
Actuaries use methods from mathematics, statistics, and computer science to assess risk and uncertainty.
See salaries and explore the career path for Actuary
Risk Analyst
Risk Analysts use methods from risk management, finance, and computer science to assess and mitigate risk.
See salaries and explore the career path for Risk Analyst
Compliance Analyst
Compliance Analysts use methods from law, finance, and computer science to ensure that organizations comply with regulations.
See salaries and explore the career path for Compliance Analyst
Auditor
Auditors use methods from accounting, finance, and computer science to examine and evaluate financial records.
See salaries and explore the career path for Auditor
Consultant
Consultants use methods from various fields (business, technology, etc) to provide advice and guidance to clients.
See salaries and explore the career path for Consultant

Reading list

We haven't picked any books for this reading list yet.

Share

Help others find this course page by sharing it with your friends and followers:
Facebook
LinkedIn
Reddit
X
Link
Email

Similar courses

Similar courses are unavailable at this time. Please try again later.
Time
35 hours
Level
Intermediate
Via
Pluralsight
Instructor
WIA
Language
English
This course belongs to a collection called Women in Analytics 2019. It's comprised of 19 courses:
  1. The Importance of Ethics in Data Science
  2. Measuring Analytics Maturity: What We Know 5 Years In
  3. Data-driven Product Improvements
  4. Ethics in Algorithms Panel
  5. Women in Analytics Speaker Q&A Day 1
  6. Women in Analytics Speaker Q&A Day 2
  7. Designing a User-centric Data Science Product
  8. Data Science Adoption: Bringing Insights to Business
  9. The Influence of the Chief Data Officer on Enterprise Analytics
  10. Diversity in Analytics
  11. Setting Expectations on Data Science Projects
  12. Implementing Vision based Deep Learning Solution Using Open Source Tools
  13. Level Up: Start Contributing to Open Source
  14. Transitioning from Academia to Industry
  15. Enhancing AI in Wearable Devices Using Topological Data Analysis
  16. Using Embeddings to Understand the Variance and Evolution of Data Science Skill Sets (viewing)
  17. Using Visual Diagnostic Tools to Gain Insight into the Model Selection Process
  18. Data Visualization with Tableau
  19. Introduction to Data Science in R
Traffic lights
Read about what's good
what should give you pause
and possible dealbreakers
Learners who are interested in the evolution of data science skills and variance will find this specialization relevant
Share this
Share to help others discover this course.
Facebook LinkedIn X Reddit Link Email
Begin learning today
Enroll now to gain full access to Using Embeddings to Understand the Variance and Evolution of Data Science Skill Sets.
Enroll now
Save for later
Add this course to your list. Find it anytime.
Save
Our mission

OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.

Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.

Find this site helpful? Tell a friend about us.

Affiliate disclosure

We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.

Your purchases help us maintain our catalog and keep our servers humming without ads.

Thank you for supporting OpenCourser.

© 2016 - 2025 OpenCourser