MrComputerScience.com
Blog For Marketing And Tech Nerds
Data science is where programming, statistics, and machine learning come together to turn raw data into useful insight. In this category, I explore data analysis, predictive modeling, feature engineering, data visualization, messy datasets, and the practical workflows behind modern data-driven decision making.
A lot of data science looks glamorous from the outside, but the real work usually involves cleaning data, testing assumptions, and building models that actually hold up. If you enjoy finding patterns and extracting signal from noise, you will probably enjoy this category.
Browse the articles below to explore data science in a practical and technical way.
A recommendation model trained on data from six months ago reflects user preferences, item catalogs, and interaction patterns from six months ago. New users have no history in the model. New items …
Continue Reading about Why Do Recommendation Systems Get Worse If You Don’t Retrain Them? →
Most data scientists spend 60 to 80 percent of their time cleaning and preparing data, not training models. This is not inefficiency. Raw data collected from real systems is full of missing values, …
Continue Reading about Why Does Data Preprocessing Take Longer Than Modeling? →
Data engineering builds and maintains the pipelines that move and store data. Data science uses that data to produce insights and models. Machine learning engineering takes those models and puts them …
Continue Reading about What Is the Difference Between Data Science, ML, and Data Engineering? →
