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🚀 SQL & Python Quick Cheatsheet for Beginners

🗄️ SQL Programming

1. What is SQL?

SQL stands for Structured Query Language. It is used to communicate with databases and work with stored data.

You can use SQL to:

✅ Retrieve data

✅ Filter data

✅ Analyze data

✅ Insert data

✅ Update data

✅ Delete data

2. SELECT

Used to retrieve data from a table.

SELECT name, salary
FROM employees;

SELECT → columns you want

FROM → table you want data from

To get all columns:

SELECT *
FROM employees;

3. WHERE

Used to filter rows.

SELECT *
FROM employees
WHERE salary > 50000;

Common operators:

= Equal

Greater than

< Less than

= Greater than or equal

<= Less than or equal

<> Not equal

4. AND, OR, NOT

Used to combine conditions.

SELECT *
FROM employees
WHERE salary > 50000
AND department = 'IT';

AND → both conditions must be true.

SELECT *
FROM employees
WHERE department = 'IT'
OR department = 'HR';

OR → at least one condition must be true.

5. ORDER BY

Used to sort your results.

SELECT *
FROM employees
ORDER BY salary DESC;

ASC → Lowest to highest

DESC → Highest to lowest

6. DISTINCT

Used to remove duplicate values.

SELECT DISTINCT department
FROM employees;

7. LIMIT

Used to restrict the number of rows returned.

SELECT *
FROM employees
LIMIT 10;

Note: Some databases use TOP or FETCH.

8. Aggregate Functions

Used to perform calculations on multiple rows.

COUNT() -- Count

SUM() -- Total

AVG() -- Average

MIN() -- Minimum

MAX() -- Maximum

Example:

SELECT AVG(salary)
FROM employees;

9. GROUP BY

Used to create groups and calculate results for each group.

SELECT
    department,
    AVG(salary) AS average_salary
FROM employees
GROUP BY department;

10. HAVING

Used to filter grouped results.

SELECT
    department,
    AVG(salary) AS average_salary
FROM employees
GROUP BY department
HAVING AVG(salary) > 70000;

WHERE → filters rows

HAVING → filters groups

🐍 Python — Beginner Fundamentals

1. What is Python?

Python is a general-purpose programming language used for:

✅ Data Analytics

✅ Automation

✅ AI & Machine Learning

✅ Data Engineering

✅ Web Development

2. Variables

Variables store values.

name = "Alex"
age = 25
salary = 50000

3. Data Types

Important beginner data types:

name = "Alex" # str
age = 25 # int
salary = 50000.5 # float
active = True # bool

type(age)

4. Strings

Strings represent text.

name = "Python"

name.upper() # PYTHON
name.lower() # python
name.strip() # removes spaces

5. Numbers

Python supports integers and floating-point numbers.

age = 25
price = 99.50

10 + 5 # Addition
10 - 5 # Subtraction
10 * 5 # Multiplication
10 / 5 # Division
10 % 3 # Remainder
10 ** 2 # Power

6. Boolean

Boolean values represent True or False.

is_logged_in = True

7. Lists

Lists store multiple values in an ordered collection.

numbers = [10, 20, 30, 40]

numbers[0] # Output: 10

Python indexing starts from 0.

8. Dictionaries

Dictionaries store data as key-value pairs.

employee = {
    "name": "Alex",
    "age": 25,
    "salary": 50000
}

employee["name"] # Output: Alex

9. Tuples

Tuples store ordered values that cannot normally be changed.

coordinates = (10, 20)

10. Sets

Sets store unique values.

numbers = {1, 2, 2, 3}
# Result: {1, 2, 3}

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Learn SQL from basic to advanced level in 30 days

Week 1: SQL Basics

Day 1: Introduction to SQL and Relational Databases

Overview of SQL Syntax

Setting up a Database (MySQL, PostgreSQL, or SQL Server)


Day 2: Data Types (Numeric, String, Date, etc.)

Writing Basic SQL Queries:

SELECT, FROM

Day 3: WHERE Clause for Filtering Data

Using Logical Operators:

AND, OR, NOT

Day 4: Sorting Data: ORDER BY

Limiting Results: LIMIT and OFFSET

Understanding DISTINCT

Day 5: Aggregate Functions:

COUNT, SUM, AVG, MIN, MAX


Day 6: Grouping Data: GROUP BY and HAVING

Combining Filters with Aggregations


Day 7: Review Week 1 Topics with Hands-On Practice

Solve SQL Exercises on platforms like HackerRank, LeetCode, or W3Schools


Week 2: Intermediate SQL

Day 8: SQL JOINS:

INNER JOIN, LEFT JOIN

Day 9: SQL JOINS Continued: RIGHT JOIN, FULL OUTER JOIN, SELF JOIN

Day 10: Working with NULL Values

Using Conditional Logic with CASE Statements

Day 11: Subqueries: Simple Subqueries (Single-row and Multi-row)

Correlated Subqueries

Day 12: String Functions:

CONCAT, SUBSTRING, LENGTH, REPLACE

Day 13: Date and Time Functions: NOW, CURDATE, DATEDIFF, DATEADD

Day 14: Combining Results: UNION, UNION ALL, INTERSECT, EXCEPT

Review Week 2 Topics and Practice

Week 3: Advanced SQL

Day 15: Common Table Expressions (CTEs)

WITH Clauses and Recursive Queries

Day 16: Window Functions:

ROW_NUMBER, RANK, DENSE_RANK, NTILE

Day 17: More Window Functions:

LEAD, LAG, FIRST_VALUE, LAST_VALUE


Day 18: Creating and Managing Views

Temporary Tables and Table Variables

Day 19: Transactions and ACID Properties

Working with Indexes for Query Optimization

Day 20: Error Handling in SQL

Writing Dynamic SQL Queries


Day 21: Review Week 3 Topics with Complex Query Practice

Solve Intermediate to Advanced SQL Challenges



Week 4: Database Management and Advanced Applications

Day 22: Database Design and Normalization:

1NF, 2NF, 3NF


Day 23: Constraints in SQL:
PRIMARY KEY, FOREIGN KEY, UNIQUE, CHECK, DEFAULT


Day 24: Creating and Managing Indexes

Understanding Query Execution Plans

Day 25: Backup and Restore Strategies in SQL

Role-Based Permissions

Day 26: Pivoting and Unpivoting Data

Working with JSON and XML in SQL

Day 27: Writing Stored Procedures and Functions

Automating Processes with Triggers

Day 28: Integrating SQL with Other Tools (e.g., Python, Power BI, Tableau)

SQL in Big Data: Introduction to NoSQL

Day 29: Query Performance Tuning:

Tips and Tricks to Optimize SQL Queries


Day 30: Final Review of All Topics

Attempt SQL Projects or Case Studies (e.g., analyzing sales data, building a reporting dashboard)

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🚀 Data Analyst Roadmap — Part 29

POWER BI LEVEL 8 — ADVANCED DAX: FILTER(), VALUES(), SELECTEDVALUE() & DYNAMIC CALCULATIONS

Now let's move into DAX functions that help you build more dynamic Power BI reports.

These functions are especially useful when your calculation needs to react to slicers, selections, or the current report context.

🔹 1. FILTER()

You already know that FILTER() can create a filtered table.

Example:

High Value Sales =
CALCULATE(
    [Total Sales],
    FILTER(
        Sales,
        Sales[SalesAmount] > 10000
    )
)

This keeps only transactions where SalesAmount is greater than 10,000.

The important thing to understand:

• "FILTER()" works with a table and evaluates a condition for each row.

• Use it when your filtering requirement is more complex than a simple condition.

🔹 2. VALUES()

"VALUES()" returns the unique values from a column based on the current filter context.

Example:

Customer Count =
COUNTROWS(
    VALUES(Sales[CustomerID])
)

This counts the unique customers visible in the current context.

For example:

• Without filters → 1,000 customers

• Region = West → 250 customers

• Region = South → 300 customers

The result changes according to the report filters.

🔹 3. VALUES() vs DISTINCT()

Both can return unique values, but they aren't identical in every situation.

A useful beginner-level rule:

• "DISTINCT()" → returns unique values from a column.

• "VALUES()" → returns unique values while also being sensitive to the current DAX context and can include a blank value when appropriate.

In advanced DAX, "VALUES()" is extremely useful for understanding what values are currently available in the filter context.

🔹 4. SELECTEDVALUE()

This is one of the most useful functions for interactive reports.

Suppose you have a Region slicer.

You can write:

Selected Region =
SELECTEDVALUE(
    Sales[Region],
    "Multiple Regions"
)

If the user selects:

• West → Result: West

• West + South → Result: Multiple Regions

If nothing is selected, the result can also return the alternate value depending on the filter context.

🔹 5. SELECTEDVALUE() with Dynamic Titles

You can use SELECTEDVALUE() to make report titles dynamic.

Example:

Sales Title =
"Sales Performance - "
&
SELECTEDVALUE(
    Sales[Region],
    "All Regions"
)

If the user selects West:

• Sales Performance - West

If multiple regions are selected:

• Sales Performance - All Regions

This makes dashboards much more interactive.

🔹 6. HASONEVALUE()

"HASONEVALUE()" checks whether exactly one unique value exists in the current filter context.

Example:

Single Region Selected =
IF(
    HASONEVALUE(Sales[Region]),
    "One Region",
    "Multiple Regions"
)

If exactly one region is selected:

• One Region

Otherwise:

• Multiple Regions

🔹 7. SELECTEDVALUE() vs HASONEVALUE()

They are related but serve different purposes.

"HASONEVALUE()" asks:

• "Is exactly one value selected?"

"SELECTEDVALUE()" asks:

• "What is that selected value?"

For example:

SELECTEDVALUE(Sales[Region]) returns the actual region.

HASONEVALUE(Sales[Region]) returns TRUE or FALSE.

🔹 8. Dynamic KPI Calculation

Suppose you want a KPI to change based on a slicer containing:

• Sales

• Profit

• Orders

A measure can use the selected value to determine what should be displayed.

Conceptually:

Selected KPI =
SWITCH(
    SELECTEDVALUE(KPI[KPI Name]),
    "Sales", [Total Sales],
    "Profit", [Total Profit],
    "Orders", [Total Orders]
)

Now one visual can display different KPIs based on the user's selection.

This is called a:

• 👉 Dynamic Measure

🔹 9. SWITCH()

"SWITCH()" is extremely useful for dynamic DAX.

Instead of writing many nested IF statements:

Performance =
SWITCH(
    TRUE(),
    [Profit Margin] >= 0.30, "Excellent",
    [Profit Margin] >= 0.15, "Good",
    [Profit Margin] >= 0, "Needs Improvement",
    "Loss"
)

It evaluates conditions and returns the corresponding result.

This is useful for:

✔ KPI categories

✔ Business rules

✔ Dynamic labels

✔ Conditional calculations

✔ Performance classification

🔹 10. Building a Dynamic Customer Message

You can combine these functions to create business-friendly messages.

Example:

Customer Message =
"Selected Customers: "
&
COUNTROWS(VALUES(Sales[CustomerID]))

If the current filter context contains 125 unique customers:

• Selected Customers: 125

This can be displayed inside a Card or used in a report title.

🔹 11. Why These Functions Matter

Real dashboards rarely show the same calculation under every situation.

Users interact with:

• Slicers

• Filters

• Drill-downs

• Cross-highlighting

• Page filters

Your DAX measures should respond appropriately.

Functions such as:

• "FILTER()"

• "VALUES()"

• "SELECTEDVALUE()"

• "HASONEVALUE()"

• "SWITCH()"

help you build that dynamic behavior.

🎯 Interview Questions

1️⃣ What does FILTER() do?

• It returns a filtered table based on a specified condition.

2️⃣ What does SELECTEDVALUE() return?

• The single value in the current context, or an alternate result when there isn't exactly one value.

3️⃣ What is HASONEVALUE() used for?

• To check whether exactly one unique value exists in the current filter context.

4️⃣ How can SELECTEDVALUE() be used in a dashboard?

• It can create dynamic titles, labels, messages, and calculations based on slicer selections.

5️⃣ Why is SWITCH() useful in DAX?

• It allows multiple conditions or selections to determine which result should be returned.

🧪 PRACTICE

Create a Region slicer.

Then create:

✔ Selected Region

✔ Customer Count

✔ Dynamic Sales Title

✔ Dynamic KPI using SWITCH()

✔ One Region / Multiple Regions indicator

Select different regions and observe how every measure responds.

💡 Key lesson:

Advanced DAX is largely about making calculations respond intelligently to the user's current context.

Once you understand:

• FILTER()

• VALUES()

• SELECTEDVALUE()

• HASONEVALUE()

• SWITCH()

you can start building genuinely interactive Power BI reports.

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