Data Analyst with SQL
Beginner
4.2
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41 Learners
A comprehensive beginner-level learning track designed for aspiring data analysts and junior data professionals. This track introduces the core concep…
148
Chapters
15
Learning hours
202
Assignments
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Hva du vil lære
Understand the basics of data, the data lifecycle, and the role of a data analyst
Apply filtering, sorting, and aggregation techniques to analyze datasets
Build analytical reports and dashboards using SQL and BI tools
Grasp the structure and organization of relational databases
Combine data from multiple tables using SQL joins
Write and execute basic SQL queries to retrieve and manipulate data
Clean and transform data using essential SQL functions
Learning track content
Retrieving Individual Columns
Retrieving Multiple Columns
Retrieving All Columns
Retrieving Distinct Rows
Limiting Results
Challenge: Find the Population of the Countries
Challenge: Find All Countries
Challenge: Find All Countries With Their IDs
Challenge: Find Country Capitals
Challenge: Find the Regions in Which All Countries Are Located
Sorting Data
Challenge: Population Sort
Sorting by Multiple Columns
Challenge: Dual-Sort Query
Specifying Sort Direction
Challenge: Sort the Countries by Region and Capital
Challenge: Sort Capitals in Descending Order
Challenge: Sort Countries in Ascending Order
Challenge: Find Countries, Their IDs, and Their Populations
Challenge: Find Countries, IDs, Populations, Regions, and Sort Them
Challenge: Find All Continents and Sort Them in Ascending Order
Using the WHERE Clause
Challenge: North America Population Sort
The WHERE Clause Operators
Challenge: Population Threshold Query
Checking for a Range of Values
Checking for NO Value
Using the AND Operator
Challenge: Europe Region Sort
Using the OR operator
Challenge: America Regions Filter
Using the NOT Operator
Challenge: Non-Europe Capitals Sort
The AVG() Function
Challenge: Surface Area Selection
The COUNT() Function
Challenge: Continental Count
The MAX() Function
The MIN() Function
Challenge: Large Surface Area Average
The SUM() Function
Introduction to Subqueries
Subqueries in SELECT and WHERE Clauses
Challenge: Top Customers by Order Count
Challenge: Products Never Ordered
Challenge: Customers from Specific Cities with Orders
Challenge: Most Popular Product Category
Non-correlated vs Correlated Subqueries
Subqueries in FROM Clauses
Challenge: Recently Hired Employees
Challenge: Departments with Above-average Salaries
Challenge: Employees Without Recent Salary Increase
Challenge: Highest Paid Employee per Department
Subqueries for Aggregation and Filtering
Subqueries for Existence and Non-existence
Challenge: Most Active Members
Challenge: Books Borrowed in the Last Month
Challenge: Members with No Borrowings
Challenge: Most Popular Genre
Understanding INNER JOIN
Challenge: Find Customers with Orders
Exploring LEFT JOIN
Challenge: List All Customers and Their Orders
RIGHT and FULL OUTER JOIN in Action
Challenge: Orders Without Customers
Joining More Than Two Tables
Challenge: Employee Project Assignments
Self-Joins for Hierarchical Data
Challenge: Manager-Employee Relationships
Advanced Join Conditions
Challenge: Complex Assignment Matching
Challenge: Employees Without Projects
Dealing with NULLs in Joins
Join Performance and Optimization
Challenge: Optimize a Join Query
Real-World Join Scenarios
Challenge: Student Performance Dashboard
Introduction to String Functions
Challenge: Standardize Customer Names
Challenge: Extract Email Domains
Advanced String Manipulation
Challenge: Mask Sensitive Information
Challenge: Clean Up City Names
Working with Dates and Times
Challenge: Calculate Order Age
Challenge: Find Late Deliveries
Advanced Date and Time Calculations
Challenge: Monthly Order Summary
Challenge: Predict Delivery Dates
Introduction to Numeric Functions
Challenge: Calculate Discounted Prices
Challenge: Find Low Stock Products
Advanced Numeric Operations
Challenge: Analyze Price Distribution
Challenge: Calculate Inventory Value
Introduction to Logical Functions
Challenge: Categorize Transactions
Challenge: Handle Missing Payment Methods
Advanced Logical Expressions
Challenge: Flag Suspicious Transactions
Challenge: Summarize Transaction Status
Understanding Null Functions
Challenge: Assign Default Bonuses
Challenge: Find Employees Without Managers
Advanced Null Handling
Challenge: Calculate Average Bonus
Challenge: Prevent Division by Zero
Introduction to JSON Functions
Challenge: Extract User Interests
Challenge: Find Users by City
Advanced JSON Manipulation
Challenge: Update User Age
Multi-level Aggregations for Business Reports
Window Functions: ROW_NUMBER, RANK, DENSE_RANK
Running Totals and Moving Averages
Period-over-Period Comparisons (MoM, YoY)
Cohort Analysis
Funnel Analysis with CASE WHEN + SUM
Customer Segmentation (RFM-style Logic)
Top-N per Group Reports
Pivot-Style Reports with CASE WHEN
KPI Calculations: Conversion, Retention, Churn, ARPU
Multi-step Analytical Pipelines with CTEs
Data Quality Checks as Analytical Queries
Advanced Window Functions: NTILE, PERCENT_RANK, CUME_DIST
Time Series Analysis: Gaps and Islands
Churn Prediction with SQL
Retention Curve Analysis
Dynamic Date Bucketing for Reports
Combining Multiple Analytical Techniques
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