Mike Zhang is the VP of Ecommerce, Digital Marketing, and Innovation at Land’s End. It’s not uncommon to misunderstand or overanalyze an ecommerce term, and Zhang provided attendees with a solid intro course on data science, including some useful definitions.
- Data analytics – Analyzing data using math and statistics. A lot of people think this is synonymous to data science.
Data science: analytics that adds computer science. It’s a broader field of training, running, testing, and productizing data models.
A bonus story on productizing – Zhang shared this example: Netflix created a competition for data analysts to create an algorithm that makes better predictions about what customers will enjoy. The winner increased conversion by 10.1%. Netflix waited 3 years and paid a million dollars to the winner, but they didn’t use it because they couldn’t productize it in the system. In other words, they couldn’t make it work in real life. In data science, you have to be able to use computer science to make it work.
Big data: Big data is high volume, velocity, variety, and veracity (4Vs) information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making.
Machine learning (ML) A type of artificial intelligence that provides computers with the ability to learn without being explicitly programmed. It learns on its own and, it doesn’t just look back; it looks forward to gives you a bigger picture. Examples include:
- Google search results
- Netflix “you may also like”
- Email spam filters
- Voice and facial recognition
AI: Advanced Advanced and complex systems that can perform tasks beyond simple prediction models.
Natural language processing (NL) the ability of a computer program to understand human speech as it is spoken. NL is a component of AI.
I’ve got his list of great steps to get started, but they’re too long to list here. You can email me at firstname.lastname@example.org and I’ll send you the list.
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