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You'll master funnel analysis (designing funnels, calculating drop-off, segmenting, root-cause diagnosis) and cohort/retention analysis (retention tables, heatmaps, LTV curves, churn-risk signals), all reinforced with hands-on SQL and Python. The course closes with two full portfolio projects built on real datasets: an e-commerce funnel analysis and a SaaS revenue cohort analysis.

A hands-on path to real DAX fluency in Power BI, from data modeling fundamentals through CALCULATE, time intelligence, iterators, and advanced analytical patterns, all built on one retail model you construct yourself. Closes with two full portfolio projects: a retail sales dashboard and a SaaS analytics build.

Learn Google BigQuery from the ground up: SQL fundamentals, joins and nested data, window functions, performance and cost optimization, advanced SQL, geospatial analysis, BigQuery ML, and two full portfolio projects with worked solutions.

A hands-on path from Python fundamentals to production-grade automation, built for data analysts. Learn to clean messy files, pull data from databases and APIs, generate automated Excel/PDF/PowerPoint reports, and schedule it all to run unattended - then go further with logging, secrets management, and workflow orchestration. Built around one consistent scenario: automating a retail company's weekly sales reporting. Closes with two full portfolio projects using real data and complete worked solutions. No prior programming experience required.

A complete, practitioner-taught path from foundational concepts to advanced technique: how to mine data for a real story, structure a narrative that lands, choose and design the right chart, write with clarity, present and defend your findings live, and apply this honestly and ethically. The course closes with two full end-to-end portfolio projects, each with a real business problem, a dataset, specific questions to answer, and a complete worked solution, so you finish with concrete work you can show, not just concepts you've read about.

A complete, hands-on introduction to marketing analytics for anyone starting out in the field. Covers the full journey from foundational concepts to job-ready skills: core marketing metrics (CTR, CAC, LTV, ROAS), customer segmentation and cohort analysis, A/B testing and experimentation, multi-touch attribution, SQL and spreadsheet analysis, dashboard design, and forecasting. Every lesson uses a consistent running case study - Northloom, a direct-to-consumer outdoor apparel brand - so concepts build on each other with realistic, connected examples rather than one-off exercises. Includes original diagrams for every lesson and two full end-to-end portfolio projects with real downloadable datasets and worked solutions: an e-commerce campaign performance and segmentation analysis, and a multi-channel attribution and budget reallocation project.

A complete, hands-on path from zero Git knowledge to running a full analytics workflow on GitHub. You'll learn how to track and undo changes safely, branch and merge without fear, collaborate with teammates through pull requests and code review, and handle the parts generic Git tutorials skip: version-controlling Jupyter notebooks and R projects, managing large data files with Git LFS, and structuring a repository so any analyst can pick it up. The course ends with two full portfolio projects: building a reviewed, documented analysis repository from scratch, and publishing an automated, GitHub Pages-hosted analytics portfolio powered by a scheduled GitHub Actions workflow.

A complete, practitioner-built path into customer analytics, covering the full beginner-to-advanced journey used at real companies. You'll learn the core metrics every customer analyst relies on (CAC, CLV, retention, churn, NRR), how to segment customers with RFM and behavioral analysis, how to run cohort and funnel analysis to diagnose what's actually driving results, how to read Voice-of-Customer data like NPS and CSAT, the basics of A/B testing and experimentation, and simple predictive techniques like churn risk scoring, all using two running example businesses (a subscription app and an e-commerce retailer) so every concept builds on the last.

Generative AI and large language models have moved from research labs to everyday business tools in just a few years. This course takes you from complete beginner to job ready practitioner: how transformers and LLMs actually work, prompt engineering, retrieval-augmented generation (RAG), fine-tuning, AI agents and tool use, evaluation and safety, and how to build real applications end to end. Every concept is taught using one running business scenario so techniques build on each other instead of feeling like disconnected trivia.

Machine Learning for Data Analysts is a practical, hands-on course built for analysts who already know SQL, Excel, or BI tools and want to add predictive modeling to their skill set. Instead of abstract theory, every concept is taught through a single running business scenario, an online retailer named NorthPeak Outfitters, so techniques build on each other from lesson to lesson instead of feeling disconnected.

A/B testing is one of the highest-leverage skills a data analyst can have: it's the difference between telling a company what happened and telling them what to do next. This course takes you from the foundations of experimentation through statistics, hypothesis testing, and experiment design, into the core statistical tests analysts use daily, honest result interpretation, and advanced pitfalls like Simpson's Paradox and multiple testing. You'll close with hands-on SQL and Python workflows and two full portfolio projects, so you finish with real work to show, not just notes.

Learn to collect real-world data through web scraping and APIs, the two main ways data analysts get data that isn't already in a clean file. You'll go from HTTP and HTML basics to real skills: parsing pages, handling pagination and JavaScript-heavy sites, working with APIs, and automating full data pipelines. Everything builds around one consistent case study, so lessons connect instead of feeling scattered. The course closes with three portfolio projects, price intelligence, job market analysis, and real estate trends, each with a real dataset and a full worked solution ready to add to your portfolio.

Picks up exactly where a SQL basics course ends and covers subqueries, common table expressions, and window functions in depth, including the specific bugs that silently produce wrong answers in production, like the NOT IN and NULL trap. Built on a real five-table relational database with genuine foreign key relationships, delivered as both SQLite and CSVs. Covers scalar and correlated subqueries, recursive CTEs, ranking and offset window functions, frame clauses, and a practical decision guide for choosing the right tool. Closes with two portfolio projects producing polished, presentation-ready analytical queries.

Most of an analyst's time goes into cleaning, not analyzing, and this course treats that seriously. Five compact modules cover the full workflow: inspecting and typing a new file, handling missing data and duplicates, standardizing text and catching invalid values, merging and reshaping multiple sources, and finally building one reusable, validated cleaning pipeline. Every lesson runs on a realistically messy dataset with genuine data quality problems built in on purpose, so what you're fixing is real, not simulated. Closes with two portfolio projects: a multi-source merge and a full reshape-and-validate pipeline.

EDA sits between cleaned data and formal statistical testing, and this course treats it as its own real skill rather than a warm-up step. Five modules cover the EDA mindset, fast univariate and bivariate scanning, multivariate and time-based exploration, anomaly spotting, and turning an exploration session into a prioritized findings summary. Built on a full year of realistic daily business data, including genuine seasonality and two deliberately planted data quality anomalies that a later lesson finds and resolves. Closes with a cold first-look investigation on an unfamiliar dataset and a full performance deep-dive project.

A statistics course built for working Data Analysts, not a general statistics degree. Every lesson passes one test: would a working analyst actually use this on the job. Four modules take you from descriptive statistics and probability through hypothesis testing to regression, all taught on one continuous fictional retailer's data so techniques build on data you already recognize. Closes with two full end-to-end portfolio projects, each ending in a written business recommendation. No unnecessary mathematical depth, no career filler, every number in every lesson verified against real data before it's written down.

Most guides to AI tools teach you how to get an answer. This course teaches you how to get an answer you can put your name on. Across 11 modules you will cover the full arc of an analyst's job Every lesson uses one fictional company, so the same figures follow you from the first module to the last. Each includes practical examples, common mistakes, practice exercises, interview questions, and downloadable diagrams. For working analysts and people moving into analytics. No AI experience assumed.

An 11-module SQL course for data analysts preparing for interviews, built around one consistent company (Kestrel Outfitters) and one repeated method: the Five Questions What is the grain? What rows do I need? How should I combine them? How should I calculate the answer? How do I validate it? Starts with how to approach an unfamiliar SQL problem, then builds through core retrieval, aggregation, joins, subqueries and CTEs, window functions, NULL and conditional logic, reshaping and combining data, working with time, and data quality and validation finishing with a dedicated module on performance for analysts. Every lesson includes a custom diagram and a set of interview questions tagged by difficulty (Screener, Analyst, Advanced, Case) and type (Conceptual, Query Writing, Debugging, Optimization, Business Case), each with a fully worked solution. PostgreSQL-first, with dialect callouts for MySQL, SQL Server, BigQuery, and Snowflake throughout.

Build practical, analyst-level Excel skills from foundations to advanced analysis. Learn how to clean and structure data, write powerful formulas, use lookups and dynamic arrays, build PivotTables and interactive dashboards, transform data with Power Query, create data models with Power Pivot and DAX, automate workflows with VBA, and perform statistical and financial analysis. The course uses realistic business scenarios and hands-on projects to help you turn Excel into a powerful tool for analysis, reporting, forecasting, and decision-making.

Learn to create clear, professional data visualizations in Python using Pandas, Matplotlib, Seaborn, Plotly, Streamlit, and Dash with practical projects and real-world business scenarios.

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Tableau Mastery

This course takes you from a complete Tableau beginner to confidently building and publishing professional dashboards. Learn Tableau architecture, data connections, modeling, calculations, LOD expressions, and visualization design through hands-on projects. Build four guided dashboards across Sales, Retail, Marketing, and Finance, plus five real-world capstone projects. By the end, you'll have a strong portfolio of practical, job-ready Tableau work.

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AI for Data Analysts

This course teaches you how to use AI as a reliable force multiplier for data analytics from AI-assisted SQL, Python, EDA, and visualization to automated reporting and analyst agents. Learn a five-layer verification framework to validate AI-generated insights before they influence decisions. Explore applied ML, AI governance, ethics, and six portfolio projects from beginner to advanced. Built for analysts who know the basics and want to work faster without sacrificing accuracy or trust.

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Python for Data Analysis

This project-driven course takes you from zero Python experience to job-ready data analyst skills. Learn Python, NumPy, pandas, visualization, EDA, statistics, databases, automation, and practical machine learning through a real-world business scenario. Build five end-to-end portfolio projects across retail, churn, marketing, finance, and HR analytics. Finish with career guidance, role expectations, and current compensation insights to prepare for the job market.

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Pandas for Data Analysis

This hands-on Pandas course takes you from your first DataFrame to writing efficient, production-ready data analysis pipelines. Learn data cleaning, transformation, aggregation, merging, time series, reshaping, and performance optimization using one realistic e-commerce dataset. Build five complete portfolio projects covering sales, customer cohorts, inventory, revenue trends, and survey analysis. By the end, you'll confidently handle and analyze real-world data for data analyst and data science roles.

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Excel for Business Analytics

This complete Excel course takes you from beginner fundamentals to advanced business analytics and modeling. Learn formulas, lookups, data cleaning, Power Query, PivotTables, dashboards, dynamic arrays, financial modeling, and automation. Work with one realistic retail dataset throughout the course so every skill builds naturally. By the end, you'll be able to turn messy data into reliable analysis, interactive dashboards, and practical business models.

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MySQL for Data Analysts

This course takes you from MySQL fundamentals to job-ready SQL and database skills through 14 structured modules. Learn database design, querying, joins, aggregation, constraints, indexing, CTEs, views, stored procedures, triggers, and transactions with practical examples and exercises. Build four complete portfolio projects covering e-commerce, library management, blogging, and sales analytics. Finish with interview preparation, career guidance, project ideas, and current compensation insights.

This course takes you from Power BI fundamentals to job-ready data analytics skills through seven structured modules. Learn data connections, Power Query, relational modeling, DAX, time intelligence, and interactive dashboard design with hands-on examples. Build six portfolio projects across sales, retail, marketing, finance, and enterprise analytics. Finish with interview preparation, career guidance, project ideas, and current compensation insights.