Data Analytics Fundamentals

What Does a Data Analyst Actually Do?

A data analyst turns raw numbers into an answer someone can act on — the job is as much about asking the right question as it is about the tools.

SQL for Analytics

SQL's SELECT, WHERE, GROUP BY and aggregate functions are the fastest way to answer most everyday business questions directly from a database.

Python & pandas for Data Analytics

pandas is Python's go-to library for working with tabular data — think of a DataFrame as a spreadsheet you can manipulate entirely in code.

Data Cleaning

Real-world data almost always has missing values, duplicates, and inconsistent formatting — cleaning it properly is what makes any later analysis trustworthy.

Choosing the Right Chart

The right chart makes a pattern obvious at a glance — the wrong one hides it, or actively misleads. The choice depends on what question you're answering.

Practical Project: Analyzing Sales Data

Bringing it all together on one realistic task: given raw order data, calculate the key numbers a manager would actually ask for.

Pivot Tables: Summarizing Data the Spreadsheet Way

A pivot table turns a long list of raw rows into a compact summary table — the exact same idea as SQL's GROUP BY, just built by dragging fields around in a spreadsheet instead of writing a query.

Basic Statistics Every Analyst Needs

Mean, median, mode and outliers are the small set of summary statistics that come up in almost every analysis — knowing what each one actually tells you (and when it quietly lies to you) is essential.

A/B Testing Basics

An A/B test compares two versions of something against real users at the same time, so you can tell whether a change actually caused an improvement — rather than guessing, or crediting random noise.

Building a Dashboard Mindset: Good vs. Bad

A dashboard is meant to be glanced at, not studied — the difference between a genuinely useful dashboard and a cluttered, ignored one usually comes down to a handful of clear design habits.

Correlation vs. Causation

Two numbers moving together doesn't mean one is causing the other — mixing these up is one of the most common, and most consequential, mistakes in data analysis.