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Quest Training

SQL for Data Analysis Training Course

Teaches analysts to query relational databases with SQL, filtering, joining and grouping records to summarize data and uncover trends that inform decisions.

DWH · Data Warehousing & Data EngineeringAll Levels#2733ClassroomEnglish , Arabic
Duration
5 Days
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Free substitution at any time · 30+ days' notice: full refund minus 10%. Cancellation policy

Sectors:Data, Analytics & Digital TransformationCategories:Data Warehousing & Data Engineering

Course Overview

SQL for Data Analysis is an essential professional skill for extracting, transforming, validating, and interpreting data stored in relational databases. Organizations across government, banking, finance, oil and gas, healthcare, manufacturing, and other industries rely on structured data to monitor performance, identify operational trends, support strategic decisions, and improve business outcomes. This SQL for Data Analysis Training Course develops the practical skills required to turn raw database records into meaningful analytical insights. The course provides a structured understanding of SQL fundamentals and their application to business and operational analysis. Participants learn to retrieve relevant information using SELECT statements, filter records with conditional expressions, sort and aggregate data, and combine information from multiple tables. The programme emphasizes writing accurate, readable, and efficient queries that answer real-world business questions rather than simply memorizing SQL syntax. A major component of the course focuses on analytical techniques for exploring datasets and identifying patterns. Participants develop practical skills in joins, subqueries, Common Table Expressions (CTEs), conditional logic, date and text functions, and window functions. These techniques support more advanced analyses, including ranking performance, calculating running totals, comparing periods, identifying exceptions, and examining trends across customers, departments, transactions, and operational activities. The programme also addresses data quality, query validation, performance considerations, and the preparation of reliable datasets for reporting and business intelligence. Through guided exercises and realistic business scenarios, participants learn to check for missing values, duplicate records, inconsistent data, and misleading aggregations. By the end of the course, participants will be able to develop SQL-based analytical solutions that support management reporting, performance monitoring, operational investigation, and evidence-based decision-making.

Learning Objectives

  • By the end of the course, participants will be able to:
  • Explain relational database concepts and the role of SQL in data analysis.
  • Retrieve relevant records using SELECT statements, filtering conditions, and sorting techniques.
  • Apply aggregate functions and GROUP BY clauses to summarize business and operational data.
  • Combine information from multiple tables using appropriate join types.
  • Develop subqueries and Common Table Expressions to structure complex analytical tasks.
  • Apply conditional expressions and date functions to classify records and analyze time-based data.
  • Use window functions to calculate rankings, running totals, moving averages, and comparative metrics.
  • Identify missing values, duplicate records, inconsistent entries, and potential data quality issues.
  • Validate query results and improve query readability, accuracy, and execution efficiency.
  • Develop reusable SQL queries that support reporting, dashboards, and management decision-making.

Who Should Attend

This course is designed for data analysts, business analysts, reporting specialists, management information system professionals, business intelligence practitioners, database users, and professionals responsible for extracting and interpreting structured organizational data. It is also suitable for finance analysts, operations analysts, performance management specialists, and departmental professionals who rely on database information to prepare reports and support decisions. The programme is relevant to employees in government entities, ministries, banks, financial institutions, oil and gas companies, industrial organizations, and large corporations. It is particularly valuable for professionals working with transaction records, financial data, customer information, inventory, service delivery metrics, operational performance, and departmental reporting. Basic computer literacy and familiarity with spreadsheets are helpful, but previous SQL programming experience is not required.

Learning Outcomes

  • Upon completing the course, participants will be able to:
  • Interpret basic relational database structures, including tables, columns, keys, and relationships.
  • Write SQL queries to retrieve, filter, sort, and organize relevant information.
  • Summarize large datasets using aggregate functions and grouped calculations.
  • Select and apply appropriate joins to combine related records accurately.
  • Structure complex queries using subqueries and CTEs.
  • Analyze time-based data using date functions and comparative calculations.
  • Apply window functions to perform ranking, sequencing, and cumulative analysis.
  • Detect potential data quality problems and validate analytical results.
  • Recognize common causes of inaccurate results, including duplicate rows and incorrect join conditions.
  • Prepare SQL-based datasets suitable for dashboards, recurring reports, and business analysis.

Course Outline

Day 1

SQL Fundamentals and Data Retrieval

  • Introduction to relational databases and the role of SQL in analytical workflows.
  • Understanding database tables, columns, data types, primary keys, and foreign keys.
  • Writing SELECT statements to retrieve specific fields and records.
  • Applying WHERE conditions to filter data using comparison and logical operators.
  • Using IN, BETWEEN, LIKE, and NULL-related conditions.
  • Sorting query results with ORDER BY and limiting returned records where supported.
  • Understanding DISTINCT and its role in removing duplicate values from query output.
  • Working with aliases to improve query readability.
  • Practical Exercise: Write SQL queries to retrieve and filter employee, customer, transaction, or inventory records according to defined business requirements.
Day 2

Data Aggregation, Joins, and Business Reporting

  • Applying COUNT, SUM, AVG, MIN, and MAX to summarize datasets.
  • Grouping records with GROUP BY to analyze performance by department, product, location, or reporting period.
  • Filtering aggregated results using HAVING.
  • Understanding INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL OUTER JOIN where supported by the database system.
  • Joining multiple tables using primary and foreign key relationships.
  • Identifying and resolving duplicate rows caused by incorrect join logic.
  • Applying CASE expressions to classify records and calculate conditional metrics.
  • Combining aggregation and joins to develop management reporting queries.
  • Practical Exercise: Build a departmental performance report combining data from multiple tables and calculate key operational or financial indicators.
Day 3

Advanced Query Techniques and Analytical Functions

  • Developing subqueries for filtering, comparison, and multi-step analysis.
  • Using Common Table Expressions (CTEs) to organize complex SQL statements.
  • Applying date and time functions to analyze daily, monthly, quarterly, and annual performance.
  • Using string functions to clean, standardize, and extract text values.
  • Handling NULL values and applying appropriate default-value logic.
  • Introducing window functions and understanding their differences from grouped aggregation.
  • Applying ROW_NUMBER, RANK, DENSE_RANK, and aggregate window functions.
  • Calculating running totals, rankings, and comparisons across categories or periods.
  • Practical Exercise: Analyze transaction or operational datasets to identify top performers, calculate cumulative results, and compare performance across reporting periods.
Day 4

Data Quality, Query Validation, and Performance

  • Identifying missing values, duplicate records, inconsistent formats, and invalid entries.
  • Using SQL queries to investigate data quality issues systematically.
  • Validating record counts, totals, and aggregation results against expected values.
  • Understanding how join cardinality and filtering conditions affect analytical accuracy.
  • Organizing queries with clear formatting, meaningful aliases, and logical structure.
  • Understanding indexes and their potential effect on query execution.
  • Introducing execution plans and identifying common causes of inefficient queries.
  • Applying practical optimization techniques, including selective filtering and avoiding unnecessary data retrieval.
  • Understanding the importance of data access permissions and responsible handling of sensitive information.
  • Practical Exercise: Audit a sample dataset, identify quality issues and query errors, and revise the SQL statements to produce reliable analytical results.
Day 5

Integrated SQL Analytics and Business Intelligence Applications

  • Translating business questions into structured SQL analysis requirements.
  • Designing reusable queries for recurring operational and management reports.
  • Combining joins, CTEs, aggregations, conditional logic, and window functions in integrated analyses.
  • Calculating performance indicators, period-over-period changes, and exception metrics.
  • Analyzing customer, financial, inventory, and operational datasets using realistic business scenarios.
  • Preparing clean analytical datasets for Power BI, Tableau, Excel, and other reporting tools.
  • Documenting query assumptions, definitions, filters, and calculation methods.
  • Reviewing query accuracy, maintainability, and suitability for repeated reporting.
  • Final Workshop: Develop an end-to-end SQL analysis using a representative business dataset, from data retrieval and validation through analytical calculations and presentation of findings, and prepare a reusable query set for management reporting.

Upcoming Dates

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Amsterdam · 11 – 15 Oct 2027 · Classroom

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2 – 6 Nov 2026€4,400—
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