MaRS & ThinkData Apply Machine Learning to Ontario Businesses
The MaRS team is using ThinkData’s technology to link records across multiple sources to understand Canadian start-up and scale-up activity.
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(Re): Softbear Developer at ThinkData Works
ThinkData Works provides the best data – through our platform and to our new employees. Take a look at our offer letter to see just a few of the perks.
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The Data Behind Love #ValentinesDay
A data analysis on how business are tapping into social media data in order to fuel their holiday marketing initiatives to increase sales.
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ThinkData on Entity Resolution at TMLS 2019
See Data Scientists Hoyoung Jang and Cheng Lin speak on scaleable entity resolution at TMLS 2019.
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The Difference Between Data Cleansing and Data Enrichment
There are plenty of steps between raw data and data that's ready for modeling & analysis – here's difference between data cleansing and data enrichment.
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#Womenintech is more than just a hashtag
Creating equal opportunities for women in tech must start early and continue throughout their careers to ensure diversity and equality in the workforce.
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Refined Data is the Key to Train Machine Learning
Refined, standardized data is the key to train machine learning in order to achieve meaningful results.
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Namara Ingest vs. a Standard ETL
Understand why we built our Ingest pipeline the way we did, and why generic ETL solutions fall short when it comes to handling data variety.
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Beyond the Startup Work Perks: Life at ThinkData
What's it like working at a startup like ThinkData? We ride that line between delivering enterprise solutions and keeping the spirit of fun and creativity.
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Adapt or Die: why your business strategy is failing your data strategy
Data projects are failing, but the problem isn't your data team – find out why business goals and data outcomes aren't lining up and what to do about it.
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Bridging the Digital Divide
Open data cannot live up to its potential until available data becomes accessible information – explore the historical gap and the progress being made.
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What do campaign contributions tell us about the federal election?
Use public sources of data to better understand the electoral process and apply data science to examine the socio-economic profiles of contributors.
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