Remove Data Collection Remove Experimentation Remove Statistics Remove Testing
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Why Nonprofits Shouldn’t Use Statistics

Depict Data Studio

— Thank you to Ann Emery, Depict Data Studio, and her Simple Spreadsheets class for inviting us to talk to them about the use of statistics in nonprofit program evaluation! But then we realized that much of the time, statistics just don’t have much of a role in nonprofit work. Why Nonprofits Shouldn’t Use Statistics.

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Bringing an AI Product to Market

O'Reilly on Data

Product Managers are responsible for the successful development, testing, release, and adoption of a product, and for leading the team that implements those milestones. Without clarity in metrics, it’s impossible to do meaningful experimentation. Ongoing monitoring of critical metrics is yet another form of experimentation.

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Methods of Study Design – Experiments

Data Science 101

Researchers/ scientists perform experiments to validate their hypothesis/ statements or to test a new product. Bias ( syatematic unfairness in data collection ) can be a potential problem in experiments and we need to take it into account while designing experiments. We randomly recruit subjects for that.

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What you need to know about product management for AI

O'Reilly on Data

All you need to know for now is that machine learning uses statistical techniques to give computer systems the ability to “learn” by being trained on existing data. After training, the system can make predictions (or deliver other results) based on data it hasn’t seen before. Machine learning adds uncertainty.

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Unintentional data

The Unofficial Google Data Science Blog

1]" Statistics, as a discipline, was largely developed in a small data world. Data was expensive to gather, and therefore decisions to collect data were generally well-considered. As computing and storage have made data collection cheaper and easier, we now gather data without this underlying motivation.

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Backtesting index rebalancing arbitrage with Amazon EMR and Apache Iceberg

AWS Big Data

Look – ahead bias – This is a common challenge in backtesting, which occurs when future information is inadvertently included in historical data used to test a trading strategy, leading to overly optimistic results. To comply with licensing considerations, we cannot provide a sample of the ETF constituents data.

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Product Management for AI

Domino Data Lab

Skomoroch proposes that managing ML projects are challenging for organizations because shipping ML projects requires an experimental culture that fundamentally changes how many companies approach building and shipping software. Yet, this challenge is not insurmountable. for what is and isn’t possible) to address these challenges. Transcript.