Data Adventures
Data analytics and data science made practical
13 articles and counting
The Data Science Pipeline: Where Assumptions Hide
Every data scientist has trained a model that works perfectly in the notebook, then fails in production. The problem rarely lies in the algorithm itself—it’s hidden in upstream decisions about data collection, preprocessing, and validation. This guide reveals where those assumptions compound and how to catch them.
Selecting the Right Evaluation Metrics for ML Models
Stop defaulting to accuracy. This post explains how to pick model evaluation metrics—precision, recall, F1, AUC, MAE/RMSE, MCC—so your evaluation matches class imbalance and real-world costs.
Closing the Gap Between Data and Business Action Now
Many analytics teams produce solid findings that never translate to business action. This post gives practical handoff, prioritization, and measurement steps to turn insight into outcome.
When More Data Hurts: Prioritizing Data Quality in ML
Collecting more data is often the instinct, but more data with poor labels can make models worse. This post explains how to audit data, detect label noise, and decide when to prioritize quality before scaling.
Navigating Complexity in Machine Learning Models
Discover how to choose between simple and complex machine learning models. This guide explores the trade-offs and scenarios for optimal model selection.
Using Regression Analysis in Google Sheets
Discover how regression analysis in Google Sheets can transform your data insights into actionable predictions, enhancing your decision-making processes.
Understanding the Limits of Automation in Machine Learning
Automation in machine learning can lead to significant failures if not monitored properly. Discover how to implement effective human oversight to mitigate risks.
Understanding Interpretability in Deep Learning Models
Delve into the critical role of interpretability in deep learning models, exploring its impact on regulations and the balance between performance and transparency.
Time Series Decomposition in Analytics
Explore how time series decomposition can transform your data analytics by revealing hidden patterns and improving forecasting accuracy for better decision-making.
Understanding Silent Failures in Machine Learning Models
Discover the nuances of silent failures in machine learning models and how they can lead to significant business consequences. Learn about calibration techniques to improve reliability.