Data Analysis: Advanced SQL, 1000x Faster Python & Visualize

SQL (Structured Query Language) is the industry-standard language for managing and analyzing data stored in relational databases. Whether you are a data analyst, business intelligence professional, marketer, product manager, student, or someone pivoting into tech, learning SQL can unlock the potential to extract meaningful insights from data. This course is designed to take you from the very basics of SQL to advanced techniques used in real-world data analysis.

No prior programming or database experience is required. You'll learn by doing-writing SQL queries against real datasets and solving analytical problems that reflect practical scenarios. By the end of the course, you will not only understand how SQL works, but more importantly, you will be able to use it to answer complex business questions, clean and transform data, and create data-driven reports.

What You'll LearnThis course is structured to help you gradually build your skills, layer by layer, with a focus on analysis:Getting Started with SQLWhat is SQL and why it matters in data analysisUnderstanding databases, tables, rows, and columnsSetting up your SQL environment (we will use a browser-based tool for easy access)Writing your first SQL queryCore SQL SkillsSelecting data using the SELECT statementFiltering results with WHERE conditionsUsing comparison and logical operatorsSorting and limiting data using ORDER BY and LIMITWorking with NULL values and handling missing dataData Aggregation and GroupingCounting, summing, averaging, and finding min/max valuesGrouping data with GROUP BYFiltering aggregated results with HAVINGCalculating percentages and ratios from grouped dataJoining TablesUnderstanding relationships between tablesInner joins, left joins, right joins, and full outer joinsCombining data from multiple tables to form complete viewsJoin best practices and avoiding common pitfallsData Cleaning and TransformationUsing CASE statements for conditional logicWorking with text using functions like UPPER, LOWER, SUBSTRING, REPLACEWorking with dates and times: extracting year, month, weekdayRemoving duplicates with DISTINCTData type conversions and formattingAdvanced SQL for AnalysisSubqueries: writing queries within queriesCommon Table Expressions (CTEs) for readable and reusable SQLWindow functions (ROW_NUMBER, RANK, LEAD, LAG) for advanced analysisAnalytical functions to calculate running totals, rolling averages, and moreUsing COALESCE and NULLIF for data managementReal-World Analytical ScenariosCohort analysis to study user behavior over timeFunnel analysis for product and marketing optimizationRetention analysis and churn measurementSales and revenue trends using time series SQLCustomer segmentation based on behavior and attributesPolars: The Next-Gen DataFrame LibraryIntroduction to Polars: Series, DataFrames, expressions, and lazy executionLearn how Polars differs from Pandas-and why it's fasterPerform data cleaning, filtering, aggregation, and joinsExplore both eager and lazy APIsApply Polars in real-world analytical workflowsSQL + Polars: Hybrid WorkflowsWhen to use SQL, when to use Polars-and how to use them togetherImport data from PostgreSQL into PolarsBuild complete data pipelines for analysis and reportingAnswer complex business questions using both toolsCreate reproducible analysis in Jupyter NotebooksWhy This Course. There are many SQL courses out there. This one is different because it focuses specifically on SQL for analysis, not just learning the syntax.

You won't be building or administering databases; instead, you'll focus on asking and answering questions with data. Whether you want to become a data analyst, prepare for interviews, or simply level up your analytical thinking, this course is built for you. It's clear, practical, and based on real problems-not academic exercises.