SQL for Data Science – QUBIT INSTITUTE

About Course

A hands-on, analyst-focused introduction to SQL that teaches you how to extract, clean, join, aggregate, and analyze data directly in databases. You’ll learn the SQL patterns used daily in analytics—window functions, subqueries, and time-series queries—and finish with a capstone that builds an analysis-ready dataset and key metrics. Two delivery options: a 4‑week intensive for working professionals and an 8‑week paced track for students.

Relational database basics: tables, keys, relationships, and how SQL fits into analytics.
Core querying: SELECT, WHERE, ORDER BY, LIMIT, DISTINCT, handling NULLs.
Aggregations & grouping: COUNT/SUM/AVG/MIN/MAX, GROUP BY, HAVING, rollups.
Joins for analysis: INNER/LEFT/RIGHT/FULL joins, anti/semi joins, join pitfalls.
Subqueries & CTEs: scalar, correlated, WITH queries, recursive CTEs (hierarchies).
Window functions: PARTITION/ORDER BY, ranks, moving averages, period-over-period.
Time-series analytics: date arithmetic, bucketing, cohort and retention queries.
Text & date functions: cleaning strings, pattern matching, regex; date parsing and formatting.
Data cleaning in SQL: deduplication, type casting, outlier flags, validation checks.
Data modeling for analytics: star schemas, normalization vs. denormalization, views.
Performance & optimization: indexes, query plans (EXPLAIN), sargable predicates.
Security & governance: roles, privileges, row-level security, PII-safe patterns.
SQL + Python workflows: pandas/read_sql, parameterized queries, exporting results.

Course Content

Introduction

Learning Approach

Module 1 — Relational databases and SQL essentials
Understand how data is stored and retrieved; write your first queries.

Module 2 — Filtering, conditional logic, and computed columns
Extract exactly what you need and derive new fields.

Module 3 — Aggregations and grouping for KPIs
Summarize data into meaningful metrics.

Module 4 — Joins that don’t bite
Combine tables accurately and avoid duplication.

Module 5 — Subqueries and CTEs (WITH)
Structure complex logic clearly and reuse it.

Module 6 — Window functions for analytics
Powerful row-by-row analytics without extra joins.

Module 7 — Dates, times, and time-series analysis
Work fluently with time.

Module 8 — Strings, patterns, and light text processing
Clean and extract insights from text fields.

Module 9 — Data cleaning and quality checks in SQL
Make messy data trustworthy.

Module 10 — Data modeling for analytics
Design analysis-friendly structures.

Module 11 — Performance tuning and query plans
Make queries fast and scalable.

Module 12 — Security, access, and governance
Keep data safe while enabling insight.

Module 13 — SQL in the analytics workflow (with Python)
Move between database and analysis tools smoothly.

Module 14 — Capstone: From raw tables to insights
Build an analysis-ready dataset and answer real questions.

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About Course

What Will You Learn?

Course Content

Introduction

Learning Approach

Module 1 — Relational databases and SQL essentials Understand how data is stored and retrieved; write your first queries.

Tables, rows, columns; primary/foreign keys; relationships

SQL syntax: SELECT, FROM, WHERE, ORDER BY, LIMIT, DISTINCT

Data types (numeric, text, boolean, date/time) and NULL semantics

Safe querying habits and result verification

Module 2 — Filtering, conditional logic, and computed columns Extract exactly what you need and derive new fields.

Comparison and logical operators; IN, BETWEEN, LIKE/ILIKE

CASE expressions for categorization and bucketing

COALESCE and NULLIF for robust calculations

Arithmetic, string concatenation, and type casting

Module 3 — Aggregations and grouping for KPIs Summarize data into meaningful metrics.

COUNT/SUM/AVG/MIN/MAX; distinct counts

GROUP BY patterns; HAVING vs WHERE

Rollups/cubes (where available) and multi-level summaries

Common KPI patterns (conversion, churn, AOV)

Module 4 — Joins that don’t bite Combine tables accurately and avoid duplication.

INNER/LEFT/RIGHT/FULL joins and join conditions

Many-to-many joins and duplication control

Anti-joins (NOT EXISTS) and semi-joins (EXISTS)

Join debugging: row counts, null checks, key diagnostics

Module 5 — Subqueries and CTEs (WITH) Structure complex logic clearly and reuse it.

Scalar, table, and correlated subqueries

WITH clauses for readability; layering transformations

Recursive CTEs for hierarchies (org charts, category trees)

Refactoring long queries into modular steps

Module 6 — Window functions for analytics Powerful row-by-row analytics without extra joins.

OVER(), PARTITION BY, ORDER BY; ROW_NUMBER, RANK, DENSE_RANK

LAG/LEAD, moving averages, running totals

Frame clauses (ROWS/RANGE) and period-over-period comparisons

Top-N per group and cohort segmentations

Module 7 — Dates, times, and time-series analysis Work fluently with time.

Date/time types, extraction (year/month/week/day), timezone basics

Date arithmetic, bucketing (daily/weekly/monthly)

Gap-filling and generating date series

Cohort retention, repeat purchase patterns, seasonality

Module 8 — Strings, patterns, and light text processing Clean and extract insights from text fields.

TRIM/UPPER/LOWER/INITCAP, REPLACE/REGEXP_REPLACE

Pattern matching: LIKE/ILIKE, SIMILAR TO, REGEXP

Tokenization basics and splitting fields

Practical examples: category parsing, code normalization

Module 9 — Data cleaning and quality checks in SQL Make messy data trustworthy.

Deduplication patterns (DISTINCT vs. window-delete)

Outlier detection flags; sanity checks and constraints

Data validation queries and completeness audits

Type normalization and standardization

Module 10 — Data modeling for analytics Design analysis-friendly structures.

Normalization vs. denormalization

Star/snowflake schemas: facts, dimensions, surrogate keys

Views and materialized views for governed access

Slowly Changing Dimensions (Type 1/2) concepts (overview)

Module 11 — Performance tuning and query plans Make queries fast and scalable.

Index types and best practices; covering indexes

Reading EXPLAIN/EXPLAIN ANALYZE; identifying bottlenecks

Sargable predicates and avoiding function-wrapped columns

Partitioning basics and selective filtering strategies

Module 12 — Security, access, and governance Keep data safe while enabling insight.

Roles, users, and privileges (GRANT/REVOKE)

Row-level security and column masking patterns

Handling PII: minimization, hashing, tokenization

Auditing and reproducible analysis

Module 13 — SQL in the analytics workflow (with Python) Move between database and analysis tools smoothly.

Connecting from Python (SQLAlchemy/psycopg2), pandas read_sql

Parameterized queries to prevent injection

Exporting to CSV and BI tools; creating reproducible data pulls

Scheduling/reporting considerations

Module 14 — Capstone: From raw tables to insights Build an analysis-ready dataset and answer real questions.

Define business questions and KPIs

Model a small star schema or create a robust view layer

Write production-quality queries (joins, windows, time-series)

Deliverables: ERD + SQL scripts + KPI report (notebook/slide)

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