Data Elixir - Data Elixir - Issue 379
ISSUE 379 · March 22, 2022In this week’s issue, Conor Dewey joins Data Elixir with an eye towards data teams and analytics. Conor is a former data scientist at Squarespace and is currently a product manager at Metabase, an open source approach to business intelligence. TrendsThe 2022 AI Index ReportThe latest edition of Stanford's AI Index Report considers data from a wide range of organizations and offers insights into the current state of AI and where things are going. Covers technical capabilities, research and development, ethics, AI policy and governance, and more. OrganizationsOrganizing and Scaling an Effective Data TeamI quite like this post on how to scale your organization’s inaugural data team. I’ve spent more time around earlier-stage companies the last few years so the emphasis on the 1-4 member stage stood out to me as particularly useful. Those early days can be a difficult trek. Founding an Analytics Engineering TeamStaying on the team-building theme, this write-up from dbt does a good job of outlining a similar journey through the lens of analytics engineering. The “State of Analytics Before AE” and “Justifying & Starting the AE Team” breakdown is a great way to look at things in my experience. What I’ve Learned About Documentation for Data TeamsWith enough team growth, it’s inevitable that documentation becomes a foundational topic of discussion for data teams. It’s a hard skill to master individually and even harder as a team. Brittany Bennett shares her experience of “taking her team dark” for a month and overhauling how they thought about data documentation. Sponsored LinkUnderstanding and Overcoming Four Types of Biases in AIThere are four types of biases found in machine learning models. These are algorithmic bias, sample bias, prejudicial bias, and measurement bias. How do each of these biases arise and how are each of them mitigated? Read this article to understand how you can produce better business outcomes by training AI models to do precisely what they are meant to do. Tutorials, Projects & OpinionsImbalance Detection for Healthier ExperimentationThis post from the experimentation team at Etsy is a good one. If you’re frequently running A/B tests with complex required instrumentation, things go wrong from time to time. The question is: how do you know when you’re getting balanced, representative results and when things are imbalanced? Is Facebook Prophet suited for doing good predictions in a real-world project?This real-world look at Prophet explores its strengths and weaknesses to help you determine if Prophet is a good fit your own forecasting projects. Covers feature engineering and modeling, interpretability, and maintenance, with tips and tricks along the way. Data Science Project Quick-StartStarting out on a new data science project can be a bit overwhelming. The surface area is often vast with a number of directions you could go. One way to make your life easier is to have a framework. In this post, Eugene Yan lays out exactly that, from understanding the problem to defining requirements to ultimately digging into the data. Deploy a Data Stack 3x Faster w/o Data EngModern Treasury did it — see how your team can do the same To find specific content from prior issues or to research topics, check out the searchable Archives on Data Elixir's Search Page >> |
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Data Elixir - Issue 378
Tuesday, March 15, 2022
Open salaries. Foundations of causal inference. Data Apps. Data labelling. Jupyter everywhere.
Data Elixir - Issue 377
Tuesday, March 8, 2022
Embrace complexity. ML for design. People analytics. MLOps is a mess. Algorithms for Decision Making. The evolving AI startup.
Data Elixir - Issue 376
Tuesday, March 1, 2022
Active learning. Notebooks in production. Interpretable models. Probabilistic ML: Advanced Topics.
Data Elixir - Issue 375
Tuesday, February 22, 2022
Data diff algorithms. Unbundling the data platform. Changing jobs? Watch for these 🚩🚩. Interactive canvas for Jupyter. Finding missing evidence.
Data Elixir - Issue 374
Tuesday, February 15, 2022
Intro to design-based causal inference. Easy EDA for Pandas. Modeling with encrypted data. Data distribution shifts.
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