If you work in applied data science long enough, you’ll eventually hear some version of this question:
“Should we switch from logistic regression to a tree-based model?”
Sometimes the answer is yes.
Very often, the answer is not really.
Logistic Regression Is Still King: Here’s Why
In a world filled with gradient boosting, deep learning, and AutoML tools, logistic regression can feel almost embarrassing to mention.
It’s old.
It’s simple.
It’s taught in every intro course.
Why Many “Data Scientists” Don’t Actually Do Data Science
Over the past decade, data scientist has become one of the most attractive titles in tech.
It promises impact, influence, and technical depth.
It suggests working on hard problems, building models, and shaping decisions with data.
SQL for Experiment Analysis: Beyond Simple Aggregates
Most experiment analyses start—and end—the same way.
You group by experiment variant.
You calculate averages.
You compare numbers.
You call it a day.
Using SQL for Feature Engineering — A Practical Guide for Analysts and Aspiring Data Scientists
When people talk about feature engineering, SQL is often treated as a second-class citizen.
You’ll hear things like:
- “Feature engineering should be done in Python.”
- “SQL is just for data extraction.”
- “Real modeling happens after the data leaves the warehouse.”
What Is a Data Science Model? — A Beginner-Friendly Guide for Analysts
If you’ve worked in BI or analytics long enough, you’ve probably heard people talk about models as if they were something mysterious.
“Once we build a model, we can predict this.”
“The model says this user will churn.”
“We need a better model for this problem.”
SQL for Experimentation: Understanding CUPED, A/A Tests, and Variance Reduction
If you’ve ever run an A/B test, you’ve probably seen this happen:
- The metrics bounce around every day
- It takes forever to reach significance
- Your A group is magically “different” from your B group
- Stakeholders keep asking, “Is this test done yet?”
API to DataFrame: A Beginner-Friendly Guide for Analysts
As a data analyst, you’re probably very comfortable working with SQL tables, CSV files, and Excel spreadsheets. But sooner or later, you’ll run into a situation like this:
- The data you need isn’t in the database
- The source system exposes data via an API
- Your stakeholder asks, “Can we pull this automatically instead of downloading it manually?”
What Is dbt and How Does dbt Work? A Practical Guide for Analysts
If you work with data — in analytics, BI, or data engineering — you’ve probably heard the term dbt (pronounced “dee-bee-tee”). It has become one of the most popular tools in the modern data stack because it empowers analysts to build production-grade data pipelines using just SQL.
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How to Use SQL for A/B Testing: A Comprehensive Guide for Analysts
A/B testing (or split testing) is one of the most powerful tools in an analyst’s toolbox: it allows you to compare two (or more) versions of a web page, feature, or user experience — and determine which version truly performs better.
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