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Absolutely ecstatic to be nominated as a finalist for this year's Women Leading Tech Awards in the Data Science category! I'm so grateful to have…
Absolutely ecstatic to be nominated as a finalist for this year's Women Leading Tech Awards in the Data Science category! I'm so grateful to have…
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Albert Edwards
You're hired as a Data Scientist to solve specific business problems. So think about what types of problems you want to solve, and then build projects related to those problems (aka Tailored Projects). Otherwise you'll end up applying for any Data related role just so you can 'get something' and before you know it, you've applied for hundreds of jobs with no response. Tailor your projects to the jobs you actually want. To help you do this, I'm sharing my ultimate guide to Tailored Projects on Wednesday the 10th July, 09:30 am What would you like to see in the guide?
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Ray Givler
🤔 If you have a dual-axis chart of the same variable Tableau, you may end up with two labels for the reference line? That's because you have two reference lines: one for each instance of your variable (this tends to happen on a duplication of a single variable). 🎯 To fix it, remove the variable for the reference line from one of your two Marks definitions in the Marks card. Real-world problem with the solution. 🏗→🧠 Build to Learn! 💭🚶♀️🚶♂️ Follow for more. #tableau #data #analytics #VizoftheRay
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Ray Givler
🤔 How many times have you reinvented the wheel in Tableau? I thought the radial jittering in my IronViz qualifier was pretty cool. But it looks like my IronViz mentor, Samuel Parsons, had done the same thing 5 years prior. At least I settled on the same chart name as he did, so that's what it is - a radial jitter. These are good for infographics showing a distribution that concentrates around a central point. 📝 In the near future, I hope to make a simple dashboard showing three variations of this chart type, including evenly distributed dots as well as dots on the periphery. In this #makeovermonday case, I thought the idea of concentrated dots meshed well with firearms - I got the idea from seeing the bee swarms by Shangruff Raina and Sherzodbek I. Anyhow, I adjusted the worksheet size so that the output is slightly elliptical to fit well in the target image. ⚖️ I thought I'd go down the route of comparison to domestic purchases because I knew those numbers were high. They've been higher than 2022, but I used that year because it was the last year of the other range and I wanted something semi-current. 🏗→🧠 Build to Learn! 💭🚶♀️🚶♂️ Follow for more. #makeovermonday #tableau #data #analytics #VizoftheRay
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Amos Robinson
Looking for the best time to purchase your next home or refinance? I did some analysis on the 30 year fixed rate mortgage today. The reason why I took a look is because mortgage applications have spiked recently. In the chart below, I have converted the data to a cycle analysis. This latest cycle started in December of 2020. It peaked in February 2023. The trajectory of mortgage rates have been on a declining growth rate since then. In the month of August, mortgage rates have entered a contraction phase of this latest cycle. When I run the data through my predictive model, it looks like we should see rates bottom out in June of 2025. So, that means that the second quarter or third quarter of next year might be the best time to take out a new mortgage or, potentially, refinance your existing mortgage if that makes sense. Amos B Robinson | www.RobinsonAnalytics.com
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Ryan Simmons
Lately, I have been forced to reflect deeply on the importance of data quality. In the realm of quantitative analytics, precision isn't just a goal; it's a necessity. When processes falter upstream, the ripple effects are felt throughout the entire data pipeline, often slowing down the entire enterprise. This disruption impacts everyone relying on that data, from analysts to executives, suddenly halting updates and decision-making processes. In light of this, the implementation of robust data governance and quality assurance measures is essential. Not only do these frameworks prevent inaccuracies, but they also ensure the integrity and reliability of data, reinforcing the trust that stakeholders place in the insights derived from it. Continuous monitoring and validation of data at every stage of the pipeline can avert potential errors that could derail projects and lead to costly setbacks. Furthermore, educating all team members involved in data handling about the importance of data quality and the common pitfalls to avoid can significantly uplift the overall data culture within an organization. By prioritizing data quality, businesses can enhance their analytical capabilities, foster innovation, and maintain a competitive edge in today's data-driven world. #DataQuality #Automation #QuantitativeAnalytics #DataGovernance #Innovation #AnalyticsExcellence #DataDrivenDecision
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🖖🏼 Richad Nieves-Becker
LLMs are obliterating the Old Ways of how data scientists work. And no one's talking about it. It's NOT just code copilots. It's: - more flexible OCR - faster named entity recognition - fewer training samples to do classification - faster data labeling (with human-in-the-loop) - and even faster baselines for regression (Regression? Yes, regression! Check out "From Words to Numbers: Your Large Language Model Is Secretly A Capable Regressor When Given In-Context Example" on arvix for direct predictions on synthetic data. And "Boosting Tabular Data Predictions with Large Language Models" on Medium for an embeddings-as-features approach on a Kaggle problem's test set). LLMs are doubling data scientists' productivity by reducing time-to-value across a range of problems. With this comes new challenges: - PII leakage - prediction robustness - inference costs and speed - data leakage (e.g., from public datasets) There's an emerging playbook to handle these while delivering with speed: The Switcheroo. 1. start with LLMs on a "classic ML" problem to prove value fast without much/any feature engineering (avoid models hosted on the public cloud) 2. if predictions are performant and robust enough on an internal holdout set, deploy 3. as that generates data, train a smaller model to do the same thing 4. replace the LLM to cut ongoing costs (People have been doing this with vendors since the dawn of time. Now, we're treat the LLM as a pricey vendor that lets us get up-to-speed faster.) --- It's time to move beyond the copilot paradigm. And deliver on the promise of the sexiest job of the 21st century. It is the #datascience leader's job to think through the second-order implications of new technology developments. Your business leaders want these implications with respect to: - the Strategy (why) - the Products (what), and - the core Operating Model (how) But it's equally important to think through how it changes YOUR operating model. Meaning, how your team works, and how they can work better. The world is changing, and you must change with it. This stuff's exciting and is one of the reasons I chose to lead teams rather than staying an IC (without losing my technical skills). It is a hard transition. Soon, I'll be publishing my perspective on The Multidimensional Leader. It's a framework I wish I had 5 years ago when I started leading teams. I use it daily to balance the competing priorities of my leadership role. You'll want to do the same. Follow me so you don't miss out. Good luck, #thinksenior PS - a repost is appreciated if you think someone else will find this useful!
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