How Qual and Quant Can Work Together
Don't miss this month's London Business Analytics Group where we're joined by Alice Daish and Casey Scott-Songin. Check it out!
The value of data is more widely accepted than ever before with more organisations adopting data initiatives. Quantitative and Qualitative data methods are often pitted against each other. Quant vs Qual! Both qualitative and quantitative methods have unique strengths. We want to discuss how they can work together to help organisations harness of the power of united data insights.
YOU MAY ALSO LIKE:
- Transforming a Museum to be data-driven using R (SkillsCast recorded in March 2017)
- Leonardo De Marchi's Deep Learning Fundamentals (in London on 22nd - 23rd October 2019)
- Lightbend Akka for Scala - Professional (in London on 11th - 12th November 2019)
- Infiniteconf 2019 - A one-day community celebration of Big Data, Machine Learning and AI (in London on 4th July 2019)
- F# eXchange 2020 (in London on 2nd - 3rd April 2020)
- Keynote by Dick Wall on Why API Design Matters, and Why Yours Sucks! (and mine sucks too!) (in London on 24th June 2019)
- Keynote by Emily Robinson on Creating a Strong Data Science Portfolio (in London on 24th June 2019)
- Speed Up Your Apache Cassandra™ Applications: A Practical Guide to Reactive Programming - Cedrick Lunven, Developer Advocate, DataStax (SkillsCast recorded in May 2019)
- Inextricably Linked: Reproducibility and Productivity in Data Science and AI - Mark Coleman, VP Marketing, dotscience & Marketing Chairperson, Cloud Native Computing Foundation (SkillsCast recorded in May 2019)
Thanks to our sponsors
How Qual and Quant Can Work Together
Casey Scott-Songin
Casey Scott-Songin is Senior Manager: Data & Insight at the National Gallery, leading a joint team of quantitative and qualitative researchers to inspire data-driven decision making within the organisation. She is a social/cultural anthropologist with seven years experience in UX Research at organisations such as The British Museum and Which?.
Alice Daish
Alice Daish is a Data Scientist with experience in using data for decision making and organisational data transformation. Previously working at The British Museum focusing on making the museum data-driven. Co-Founder of R- Ladies Global and Leadership Team. Microsoft MVP. Registered Scientist previously trained in ecology and quantitative biology.
Don't miss this month's London Business Analytics Group where we're joined by Alice Daish and Casey Scott-Songin. Check it out!
The value of data is more widely accepted than ever before with more organisations adopting data initiatives. Quantitative and Qualitative data methods are often pitted against each other. Quant vs Qual! Both qualitative and quantitative methods have unique strengths. We want to discuss how they can work together to help organisations harness of the power of united data insights.
YOU MAY ALSO LIKE:
- Transforming a Museum to be data-driven using R (SkillsCast recorded in March 2017)
- Leonardo De Marchi's Deep Learning Fundamentals (in London on 22nd - 23rd October 2019)
- Lightbend Akka for Scala - Professional (in London on 11th - 12th November 2019)
- Infiniteconf 2019 - A one-day community celebration of Big Data, Machine Learning and AI (in London on 4th July 2019)
- F# eXchange 2020 (in London on 2nd - 3rd April 2020)
- Keynote by Dick Wall on Why API Design Matters, and Why Yours Sucks! (and mine sucks too!) (in London on 24th June 2019)
- Keynote by Emily Robinson on Creating a Strong Data Science Portfolio (in London on 24th June 2019)
- Speed Up Your Apache Cassandra™ Applications: A Practical Guide to Reactive Programming - Cedrick Lunven, Developer Advocate, DataStax (SkillsCast recorded in May 2019)
- Inextricably Linked: Reproducibility and Productivity in Data Science and AI - Mark Coleman, VP Marketing, dotscience & Marketing Chairperson, Cloud Native Computing Foundation (SkillsCast recorded in May 2019)
Thanks to our sponsors
How Qual and Quant Can Work Together
Casey Scott-Songin
Casey Scott-Songin is Senior Manager: Data & Insight at the National Gallery, leading a joint team of quantitative and qualitative researchers to inspire data-driven decision making within the organisation. She is a social/cultural anthropologist with seven years experience in UX Research at organisations such as The British Museum and Which?.
Alice Daish
Alice Daish is a Data Scientist with experience in using data for decision making and organisational data transformation. Previously working at The British Museum focusing on making the museum data-driven. Co-Founder of R- Ladies Global and Leadership Team. Microsoft MVP. Registered Scientist previously trained in ecology and quantitative biology.