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🧠 SQL Interview Question (Moderate & Analytical)
📌

customers(customer_id, customer_name)
orders(order_id, customer_id, order_date, order_amount)

❓ Ques :

👉 Find customers who contributed more than 20% of the total company revenue.

🧩 How Interviewers Expect You to Think

• Calculate overall revenue
• Aggregate revenue per customer
• Compare individual revenue with total revenue
• Structure query clearly using CTEs

💡 SQL Solution

WITH customer_revenue AS (
SELECT
customer_id,
SUM(order_amount) AS cust_revenue
FROM orders
GROUP BY customer_id
),
total_revenue AS (
SELECT SUM(order_amount) AS total_revenue
FROM orders
)

SELECT
c.customer_id,
c.customer_name
FROM customers c
JOIN customer_revenue cr
ON c.customer_id = cr.customer_id
CROSS JOIN total_revenue tr
WHERE cr.cust_revenue > 0.2 * tr.total_revenue;

🔥 Why this question is powerful:

• Tests aggregation + joins together
• Checks revenue contribution calculation logic
• Evaluates structured query writing (clean CTE usage)

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✅ Power BI Basics You Should Know 📊⚡

Power BI is a business analytics tool by Microsoft that helps you visualize data, create dashboards, and make data-driven decisions.

1️⃣ What is Power BI?
A powerful data visualization and business intelligence platform to analyze and share insights across your organization.

2️⃣ Key Components:
- Power BI Desktop – Create reports
- Power BI Service – Share reports online
- Power BI Mobile – Access reports on the go
- Power Query – Data cleaning & transformation tool
- DAX (Data Analysis Expressions) – Formula language for calculations

3️⃣ Common Data Sources:
- Excel
- SQL Server
- SharePoint
- Azure
- Web APIs
- Google Sheets

4️⃣ Core Features:
- Interactive dashboards
- Real-time data updates
- Data modeling
- Custom visuals
- Drill-down & filtering

5️⃣ Power BI Workflow:
1. Connect to data
2. Clean and transform using Power Query
3. Create data model
4. Add visuals & dashboards
5. Publish and share

6️⃣ Popular Visuals:
- Bar/column charts
- Pie charts
- Tables & matrices
- Maps
- Cards & KPIs
- Slicers (filters)

7️⃣ Use Cases:
- Sales and marketing reports
- Financial dashboards
- HR analytics
- Project tracking
- Customer insights

8️⃣ Benefits of Using Power BI:
- Easy to use
- Scales with your business
- Integrates with Microsoft ecosystem
- Great for both analysts and business users

9️⃣ Challenges:
- Learning DAX for advanced logic
- Performance with very large datasets
- Licensing limitations for Pro/Free users

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- Data Analyst
- Power BI Developer
- Dashboard Designer

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Tableau turns data into stories. Companies love it for dashboards. Here's the simple path.

🔹 FOUNDATIONS

1️⃣ What is Tableau
- Drag-and-drop visualization tool
- Connects to Excel, SQL, Google Sheets
- Interactive dashboards
- No coding needed
- Publish to Tableau Public/Server

🔥 Used by 80% of Fortune 500 companies

2️⃣ Getting Started (Free)
- Download Tableau Public (free forever)
- Connect your first dataset
- Build a basic bar chart
- Save & share online

3️⃣ Core Visualizations
- Bar charts, line charts
- Pie charts (use wisely)
- Maps (geographic data)
- Scatter plots
- Heatmaps

🔥 Start here daily — 30 mins practice

🔥 TABLEAU ESSENTIAL SKILLS

4️⃣ Calculated Fields (Must-Know ⭐)
- Basic math: SUM, AVG
- IF statements
- Date functions (DATEDIFF)
- String functions (CONTAINS)
- LOD expressions (intro)

5️⃣ Filters & Parameters
- Quick filters
- Context filters
- Filter actions
- Parameter controls
- Top N filters

🔥 Makes dashboards interactive

6️⃣ Dashboards (Game-Changer ⭐)
- Layout containers
- Dashboard actions (filter, highlight)
- Device layouts (desktop, mobile)
- Legends & tooltips
- Storytelling points

7️⃣ Data Prep (Tableau Prep)
- Clean data before viz
- Union & join datasets
- Pivot data
- Handle nulls/duplicates

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8️⃣ Advanced Charts
- Bullet graphs
- Waterfall charts
- Pareto charts
- Box plots
- Funnel viz

🚀 PRO TABLEAU SKILLS

9️⃣ Level of Detail (LOD) Expressions
- FIXED, INCLUDE, EXCLUDE
- Percent of total
- Ranking across groups
- Cohort analysis

🔥 Interview must-have

🔟 Maps & Geocoding
- Custom territories
- Path maps
- Filled maps
- Spatial files

1️⃣1️⃣ Sets & Groups
- Combined sets
- Set actions
- Hierarchy drill-down
- Bins & clusters

1️⃣2️⃣ Performance Optimization
- Extracts vs Live
- Custom SQL
- Aggregations
- Publish best practices

⚙️ INDUSTRY READY

1️⃣3️⃣ Stories & Presentations
- Story points
- Annotations
- Export to PDF/PowerPoint
- Device designer

1️⃣4️⃣ Tableau Public Portfolio
- Build 5 dashboards
- Finance, sales, HR examples
- Share links in resume
- Get feedback

🔥 Employers check this first

⭐ TOOLS TO MASTER WITH TABLEAU
- Excel/SQL (data source)
- Tableau Public (free)
- Tableau Desktop (trial)
- Google Data Studio (compare)


⭐ Simple Learning Order
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✅ 🔤 A–Z of Power BI DAX Functions 📊⚡💻

A – ALL()
Removes filters from a table or column.

B – BLANK()
Returns an empty value.

C – CALCULATE()
Modifies filter context to evaluate expressions.

D – DISTINCT()
Returns unique values from a column.

E – EARLIER()
Refers to earlier row context in calculated columns.

F – FILTER()
Returns filtered table based on conditions.

G – GROUPBY()
Groups rows and performs aggregations.

H – HASONEVALUE()
Checks if a column has exactly one value.

I – IF()
Conditional function (like if-else).

J – JSON Functions
Used for handling JSON data (limited use in DAX).

K – KEEPFILTERS()
Preserves existing filters while applying new ones.

L – LOOKUPVALUE()
Fetches value from another table.

M – MAX()
Returns maximum value.

N – NOW()
Returns current date and time.

O – OR()
Logical OR condition.

P – POWER()
Raises number to a power.

Q – QUOTIENT()
Returns integer portion of division.

R – RELATED()
Fetches related table value using relationships.

S – SUM()
Adds values in a column.

T – TOTALYTD()
Calculates year-to-date totals.

U – USERELATIONSHIP()
Activates inactive relationship in calculations.

V – VALUES()
Returns distinct values in a column.

W – WEEKNUM()
Returns week number from a date.

X – X Functions (SUMX, AVERAGEX, COUNTX)
Iterative functions that evaluate expressions row-by-row.

Y – YEAR()
Extracts year from date.

Z – Z-Test Concept (Statistical Calculation)
Statistical comparison implemented using DAX formulas.

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✅ Data Visualization Mistakes Beginners Should Avoid

1. Choosing the Wrong Chart
- Pie charts for trends fail
- Line charts for categories confuse
- Use bar for comparison
- Use line for time series

2. Too Much Data in One Chart
- Visual clutter
- Hard to read
- Split into multiple charts

3. Ignoring Axis Scales
- Truncated axes mislead
- Uneven scales distort insight
- Start from zero for bars

4. Poor Color Choices
- Too many colors
- Low contrast
- Red green fails for color blindness
- Use 3 to 5 colors max

5. Missing Labels and Titles
- Viewer guesses meaning
- Low trust
- Always add title, axis labels, units

6. Using 3D Charts
- Distorts perception
- Hides values
- Use flat 2D visuals

7. Sorting Data Incorrectly
- Random order hides pattern
- Sort bars by value
- Keep time data chronological

8. No Context
- Numbers without meaning
- No baseline or target
- Add reference lines or benchmarks

9. Overloading Dashboards
- Too many KPIs
- Decision paralysis
- One dashboard. One question

10. No Validation
- Visual looks right but lies
- Data filters missed
- Always cross-check with raw numbers

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Essential Python and SQL topics for data analysts 😄👇

Python Topics:

Python Resources - @pythonanalyst

1. Data Structures
   - Lists, Tuples, and Dictionaries
   - NumPy Arrays for numerical data

2. Data Manipulation
   - Pandas DataFrames for structured data
   - Data Cleaning and Preprocessing techniques
   - Data Transformation and Reshaping

3. Data Visualization
   - Matplotlib for basic plotting
   - Seaborn for statistical visualizations
   - Plotly for interactive charts

4. Statistical Analysis
   - Descriptive Statistics
   - Hypothesis Testing
   - Regression Analysis

5. Machine Learning
   - Scikit-Learn for machine learning models
   - Model Building, Training, and Evaluation
   - Feature Engineering and Selection

6. Time Series Analysis
   - Handling Time Series Data
   - Time Series Forecasting
   - Anomaly Detection

7. Python Fundamentals
   - Control Flow (if statements, loops)
   - Functions and Modular Code
   - Exception Handling
   - File

SQL Topics:

SQL Resources - @sqlanalyst

1. SQL Basics
- SQL Syntax
- SELECT Queries
- Filters

2. Data Retrieval
- Aggregation Functions (SUM, AVG, COUNT)
- GROUP BY

3. Data Filtering
- WHERE Clause
- ORDER BY

4. Data Joins
- JOIN Operations
- Subqueries

5. Advanced SQL
- Window Functions
- Indexing
- Performance Optimization

6. Database Management
- Connecting to Databases
- SQLAlchemy

7. Database Design
- Data Types
- Normalization

Remember, it's highly likely that you won't know all these concepts from the start. Data analysis is a journey where the more you learn, the more you grow. Embrace the learning process, and your skills will continually evolve and expand. Keep up the great work!

Share with credits: https://t.me/sqlspecialist

Hope it helps :)