Data Analysis Guides
Practical, step-by-step guides to build data analysis skills you can apply at work.
52 Guides
Using AI to Assist Your Excel and SQL Work
AI tools can speed up Excel and SQL work, but only if you verify everything. A concrete workflow to generate, test, and trust AI-assisted analysis.
AI-Assisted Python and Pandas: Clean and Analyze Faster
AI accelerates Pandas work, yet wrong code fails quietly. Use AI to draft, then check every step on real data.
AI-Assisted SQL: Generate, Explain, and Safely Debug Queries
AI can draft and explain SQL, but you must verify it. A safe workflow for generating, reading, and fixing queries with AI.
AI Data Cleaning: From Messy to Analysis-Ready
AI turns messy exports into clean tables faster, but needs guardrails. A practical cleanup workflow with checkpoints.
AI-Assisted Data Visualization: Charts and Insights
AI suggests charts and spots patterns, yet chart choice is still yours. Use it to prototype, then apply sound design.
How AI Changes the Data Analyst Role
AI can write the code, so what is left for the analyst? The skills that survive are judgment, problem definition, and verifying AI output. Build them.
What Does a Data Analyst Actually Do? A Day in the Life
A realistic look at a data analyst day: the meetings, the SQL, the dashboards, and the 80% of the job nobody talks about. Not the marketing version.
Data Cleaning in Excel
Remove duplicates, fix formatting, and structure messy data the right way.
Data Cleaning with SQL (Common Mistakes & Fixes)
Clean messy data directly in SQL — find duplicates, fix NULLs, standardize text and dates, and build a repeatable cleaning workflow. Real queries included.
Data Storytelling: How to Present Data So People Act
Dashboards that get ignored are weak data storytelling. Learn a repeatable 3-part structure to present data, choose the right visual, and drive action.
Excel Chart Selection: Line, Bar, Scatter — Which to Use
Pick the right Excel chart for the data you have: line for trends over time, bar for comparing categories, scatter for relationships. See real examples.
Excel for Data Analysis
Turn pivot tables and formulas into a practical analysis workflow that saves you hours.
Excel vs Python: Which Should You Use for Data Analysis?
Same three analysis jobs done both ways, with real numbers on where Excel wins, where Python wins, and the file sizes where the answer flips.
Excel vs SQL: When to Use Which
Excel is not a worse SQL and SQL is not a better Excel — they answer different questions. The 3-question test tells you which tool to open.
How to Become a Data Analyst
The skills, portfolio, and job-search strategy to land your first analyst role.
How to Choose Your Data Tool Stack (Excel/SQL/Python/BI)
There's no single best stack — there's the right stack for your goal. How to pick from Excel, SQL, Python, and BI tools, mapped to the job you want.
How to Create a Data Dashboard in Excel (Step by Step)
Build a working Excel dashboard in one afternoon — pivot tables, slicers, charts, and KPIs that update themselves. No Excel wizardry required.
How to Learn Data Analysis for Beginners
A practical roadmap from Excel fundamentals to SQL queries. Start building real analysis skills in 8 weeks.
How to Read Any Chart: Spotting Misleading Axes and Bad Data
Most chart mistakes are not about picking the wrong type — they are about truncated axes, cherry-picked ranges, and double axes. Learn to spot all three.
Power BI Chart Selection: Pick the Right Visual
Power BI chart selection decides whether a report lands. A quick map from question type to the right visual.
Power BI Copilot: AI Reports, DAX, and Data Prep
Power BI Copilot drafts reports, DAX, and data prep in plain language. What it does well, and where you still steer.
Power BI Data Modeling: Star Schema Basics
Power BI data modeling with a star schema makes reports fast and correct. Build one fact table and clean dimensions.
Power BI DAX CALCULATE and Filter Context
Power BI CALCULATE changes filter context to build ratios and YoY. Master it with a month-over-month example.
Power BI DAX: Measures and Calculated Columns
Power BI DAX measures aggregate at query time; calculated columns store per row. Know when to use each with a sales model.
Power BI for Beginners: Build Your First Report
New to Power BI? Connect data, shape it in Power Query, build a sales dashboard with DAX measures, and publish your first report — with a practice file.
Power BI Power Query: ETL from the BI Side
Power BI Power Query reshapes data before it loads. Unpivot, merge, and clean steps that refresh on schedule.
Power BI vs Tableau: Which Data Tool Should You Learn First
Both Power BI and Tableau are top-tier BI tools. Here is a side-by-side comparison of cost, learning curve, job demand, and which to learn first.
Prompt Engineering for Data Analysts: Reusable Templates
Prompt engineering for analysts is about reproducible asks. Templates that turn vague requests into clean analysis specs.
Python Data Visualization: Matplotlib and Seaborn
Python Matplotlib and Seaborn turn DataFrames into charts. Build clean, labeled plots without fighting defaults.
How to Learn Python for Data Analysis (for Excel Users)
A practical path from Excel to Python for data work — pandas mapped to the Excel skills you already know. No CS degree, no math prerequisites.
Python for Office Automation: Batch and Repeat Tasks
Python automates the office busywork: batch Excel, rename files, email reports. A few scripts that save hours weekly.
Python pandas for Data Cleaning (the Excel User's Guide)
If you already know Excel, pandas is the next step. Here is how to clean messy data in pandas — with a practice file and real code you can run.
Python Pandas groupby: Aggregate Like SQL
Python Pandas groupby mirrors SQL GROUP BY. Compute totals by category and reshape results into a report.
Python Pandas: Read, Filter, and Explore DataFrames
Python Pandas reads, filters, and explores tables in a few lines. Load a CSV and answer questions like a SQL query.
Python Regex for Data Cleaning: Patterns That Work
Python regex pulls structure from messy strings. Extract IDs, phone numbers, and codes with patterns you can trust.
Python Setup for Data Analysis: Environment and Tools
Set up Python for analysis without breaking your system. Virtual environments, Jupyter, and the packages worth installing.
SQL Aggregate Advanced: Conditional SUM, AVG, COUNT
SQL conditional aggregates compute totals that change with a flag. Use SUM(CASE ...) for pivots inside one query.
How to Write SQL Queries for Business Reports
The exact SQL patterns every analyst rewrites every week — revenue trends, MoM growth, TopN rankings, retention, cohort, and funnel. Copy, adapt, ship.
SQL CASE WHEN: Conditional Logic in Queries
SQL CASE WHEN labels rows inside a query. Tag orders as small or large and build status flags without a second table.
SQL Common Errors and How to Debug Them
SQL throws vague errors that scare beginners. A debugging checklist for the ten most common query failures.
SQL Date Aggregation: Group by Month, Week, Quarter
SQL date aggregation groups sales by month, week, or quarter. Use DATE_TRUNC and date parts on a transactions table.
SQL for Data Analysis: A Beginner's Practical Guide
Learn the SQL basics analysts use every day — SELECT, WHERE, JOIN, GROUP BY — and write your first real business queries in 6 weeks.
SQL GROUP BY and HAVING: Aggregate Correctly
SQL GROUP BY collapses rows into summary groups; HAVING filters those groups. Learn both with an orders table and common mistakes.
SQL Joins Explained (INNER, LEFT, RIGHT, FULL) with Examples
Learn SQL joins the way analysts actually use them — INNER, LEFT, RIGHT, and FULL JOIN on real tables with runnable examples and the mistakes to avoid.
SQL or Python First? A Realistic Learning Path for 2026
SQL and Python both matter for data work, but which should you learn first? Here is a practical, honest path based on the jobs actually hiring.
SQL Performance and Indexing Basics
SQL indexing stops full table scans on big data. Read EXPLAIN plans and add the right index without guessing.
SQL String Handling: SUBSTRING, REGEXP, CONCAT
Clean dirty text in SQL with SUBSTRING, REGEXP, and CONCAT. Split names, strip codes, and standardize categories.
SQL Subqueries vs CTEs: When to Use Which
SQL subqueries and CTEs both break problems into steps. Learn when a CTE beats a nested subquery for readability and debugging.
SQL Views and Temporary Tables: Reusable Query Logic
SQL views and temp tables package query logic for reuse. Know which to reach for in reports versus one-offs.
SQL Window Functions & CTEs (Real Examples)
Ranking rows, comparing to the previous row, running totals — window functions, made readable with CTEs. A real-world guide on one sample table.
Tableau Basics: Your First Dashboard from Scratch
Build your first Tableau dashboard from scratch. Connect data, drop your first sheets, and ship a one-screen view.
Tableau for Beginners: Build Your First Dashboard
Learn Tableau the fast way — load data, drag to chart, filter with parameters, and publish a clean dashboard. A hands-on beginner path with real examples.