Python Beginner Roadmap 🐍

📂 Start Here
∟📂 Install Python & VS Code
∟📂 Learn How to Run Python Files

📂 Python Basics
∟📂 Variables & Data Types
∟📂 Input & Output
∟📂 Operators (Arithmetic, Comparison)
∟📂 if, else, elif
∟📂 for & while loops

📂 Data Structures
∟📂 Lists
∟📂 Tuples
∟📂 Sets
∟📂 Dictionaries

📂 Functions
∟📂 Defining & Calling Functions
∟📂 Arguments & Return Values

📂 Basic File Handling
∟📂 Read & Write to Files (.txt)

📂 Practice Projects
∟📌 Calculator
∟📌 Number Guessing Game
∟📌 To-Do List (store in file)

📂 ✅ Move to Next Level (Only After Basics)
∟📂 Learn Modules & Libraries
∟📂 Small Real-World Scripts

For detailed explanation, join this channel 👇
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SQL Beginner Roadmap 🗄️

📂 Start Here
∟📂 Install SQL Server / MySQL / SQLite
∟📂 Learn How to Run SQL Queries

📂 SQL Basics
∟📂 What is SQL?
∟📂 Basic SELECT Statements
∟📂 Filtering with WHERE Clause
∟📂 Sorting with ORDER BY
∟📂 Using LIMIT / TOP

📂 Data Manipulation
∟📂 INSERT INTO
∟📂 UPDATE
∟📂 DELETE

📂 Table Management
∟📂 CREATE TABLE
∟📂 ALTER TABLE
∟📂 DROP TABLE

📂 SQL Joins
∟📂 INNER JOIN
∟📂 LEFT JOIN
∟📂 RIGHT JOIN
∟📂 FULL OUTER JOIN

📂 Advanced Queries
∟📂 GROUP BY & HAVING
∟📂 Subqueries
∟📂 Aggregate Functions (COUNT, SUM, AVG)

📂 Practice Projects
∟📌 Build a Simple Library DB
∟📌 Employee Management System
∟📌 Sales Report Analysis

📂 ✅ Move to Next Level (Only After Basics)
∟📂 Learn Indexing & Performance Tuning
∟📂 Stored Procedures & Triggers
∟📂 Database Design & Normalization

Credits: https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v

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✅ Data Analyst Interview Questions for Freshers 📊

1) What is the role of a data analyst?
Answer: A data analyst collects, processes, and performs statistical analyses on data to provide actionable insights that support business decision-making.

2) What are the key skills required for a data analyst?
Answer: Strong skills in SQL, Excel, data visualization tools (like Tableau or Power BI), statistical analysis, and problem-solving abilities are essential.

3) What is data cleaning?
Answer: Data cleaning involves identifying and correcting inaccuracies, inconsistencies, or missing values in datasets to improve data quality.

4) What is the difference between structured and unstructured data?
Answer: Structured data is organized in rows and columns (e.g., spreadsheets), while unstructured data includes formats like text, images, and videos that lack a predefined structure.

5) What is a KPI?
Answer: KPI stands for Key Performance Indicator, which is a measurable value that demonstrates how effectively a company is achieving its business goals.

6) What tools do you use for data analysis?
Answer: Common tools include Excel, SQL, Python (with libraries like Pandas), R, Tableau, and Power BI.

7) Why is data visualization important?
Answer: Data visualization helps translate complex data into understandable charts and graphs, making it easier for stakeholders to grasp insights and trends.

8) What is a pivot table?
Answer: A pivot table is a feature in Excel that allows you to summarize, analyze, and explore data by reorganizing and grouping it dynamically.

9) What is correlation?
Answer: Correlation measures the statistical relationship between two variables, indicating whether they move together and how strongly.

10) What is a data warehouse?
Answer: A data warehouse is a centralized repository that consolidates data from multiple sources, optimized for querying and analysis.

11) Explain the difference between INNER JOIN and OUTER JOIN in SQL.
Answer: INNER JOIN returns only the matching rows between two tables, while OUTER JOIN returns all matching rows plus unmatched rows from one or both tables, depending on whether it’s LEFT, RIGHT, or FULL OUTER JOIN.

12) What is hypothesis testing?
Answer: Hypothesis testing is a statistical method used to determine if there is enough evidence in a sample to infer that a certain condition holds true for the entire population.

13) What is the difference between mean, median, and mode?
Answer:
⦁ Mean: The average of all numbers.
⦁ Median: The middle value when data is sorted.
⦁ Mode: The most frequently occurring value in a dataset.

14) What is data normalization?
Answer: Normalization is the process of organizing data to reduce redundancy and improve integrity, often by dividing data into related tables.

15) How do you handle missing data?
Answer: Missing data can be handled by removing rows, imputing values (mean, median, mode), or using algorithms that support missing data.

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Today, let's understand SQL JOINS in detail: 📝

SQL JOINs are used to combine rows from two or more tables based on related columns.

🟢 1. INNER JOIN
Returns only the matching rows from both tables.

Example:
SELECT Employees.name, Departments.dept_name
FROM Employees
INNER JOIN Departments
ON Employees.dept_id = Departments.id;

📌 Use Case: Employees with assigned departments only.

🔵 2. LEFT JOIN (LEFT OUTER JOIN)
Returns all rows from the left table, and matching rows from the right table. If no match, returns NULL.

Example:
SELECT Employees.name, Departments.dept_name
FROM Employees
LEFT JOIN Departments
ON Employees.dept_id = Departments.id;

📌 Use Case: All employees, even those without a department.

🟠 3. RIGHT JOIN (RIGHT OUTER JOIN)
Returns all rows from the right table, and matching rows from the left table. If no match, returns NULL.

Example:
SELECT Employees.name, Departments.dept_name
FROM Employees
RIGHT JOIN Departments
ON Employees.dept_id = Departments.id;

📌 Use Case: All departments, even those without employees.

🔴 4. FULL OUTER JOIN
Returns all rows from both tables. Non-matching rows show NULL.

Example:
SELECT Employees.name, Departments.dept_name
FROM Employees
FULL OUTER JOIN Departments
ON Employees.dept_id = Departments.id;

📌 Use Case: See all employees and departments, matched or not.

📝 Tips:
⦁ Always specify the join condition (ON)
⦁ Use table aliases to simplify long queries
⦁ NULLs can appear if there's no match in a join

📌 SQL Roadmap: https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v/1506

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📊 Data Analytics Career Paths & What to Learn 🧠📈

🧮 1. Data Analyst
▶️ Tools: Excel, SQL, Power BI, Tableau
▶️ Skills: Data cleaning, data visualization, business metrics
▶️ Languages: Python (Pandas, Matplotlib)
▶️ Projects: Sales dashboards, customer insights, KPI reports

📉 2. Business Analyst
▶️ Tools: Excel, SQL, PowerPoint, Tableau
▶️ Skills: Requirements gathering, stakeholder communication, data storytelling
▶️ Domain: Finance, Retail, Healthcare
▶️ Projects: Market analysis, revenue breakdowns, business forecasts

🧠 3. Data Scientist
▶️ Tools: Python, R, Jupyter, Scikit-learn
▶️ Skills: Statistics, ML models, feature engineering
▶️ Projects: Churn prediction, sentiment analysis, classification models

🧰 4. Data Engineer
▶️ Tools: SQL, Python, Spark, Airflow
▶️ Skills: Data pipelines, ETL, data warehousing
▶️ Platforms: AWS, GCP, Azure
▶️ Projects: Real-time data ingestion, data lake setup

📦 5. Product Analyst
▶️ Tools: Mixpanel, SQL, Excel, Tableau
▶️ Skills: User behavior analysis, A/B testing, retention metrics
▶️ Projects: Feature adoption, funnel analysis, product usage trends

📌 6. Marketing Analyst
▶️ Tools: Google Analytics, Excel, SQL, Looker
▶️ Skills: Campaign tracking, ROI analysis, segmentation
▶️ Projects: Ad performance, customer journey, CLTV analysis

🧪 7. Analytics QA (Data Quality Tester)
▶️ Tools: SQL, Python (Pytest), Excel
▶️ Skills: Data validation, report testing, anomaly detection
▶️ Projects: Dataset audits, test case automation for dashboards

💡 Tip: Pick a role → Learn tools → Practice with real datasets → Build a portfolio → Share insights

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🧠 How much SQL is enough to crack a Data Analyst Interview?

📌 Basic Queries
⦁ SELECT, FROM, WHERE, ORDER BY, LIMIT
⦁ Filtering, sorting, and simple conditions

🔍 Joins & Relations
⦁ INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL JOIN
⦁ Using keys to combine data from multiple tables

📊 Aggregate Functions
⦁ COUNT(), SUM(), AVG(), MIN(), MAX()
⦁ GROUP BY and HAVING for grouped analysis

🧮 Subqueries & CTEs
⦁ SELECT within SELECT
⦁ WITH statements for better readability

📌 Set Operations
⦁ UNION, INTERSECT, EXCEPT
⦁ Merging and comparing result sets

📅 Date & Time Functions
⦁ NOW(), CURDATE(), DATEDIFF(), DATE_ADD()
⦁ Formatting & filtering date columns

🧩 Data Cleaning
⦁ TRIM(), UPPER(), LOWER(), REPLACE()
⦁ Handling NULLs & duplicates

📈 Real World Tasks
⦁ Sales by region
⦁ Weekly/monthly trend tracking
⦁ Customer churn queries
⦁ Product category comparisons

✅ Must-Have Strengths:
⦁ Writing clear, efficient queries
⦁ Understanding data schemas
⦁ Explaining logic behind joins/filters
⦁ Drawing business insights from raw data

SQL Resources: https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v

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📊 Top 5 Data Analysis Techniques You Should Know 🧠📈

1️⃣ Descriptive Analysis
▶️ Summarizes data to understand what happened
▶️ Tools: Mean, median, mode, standard deviation, charts
▶️ Example: Monthly sales report showing total revenue

2️⃣ Diagnostic Analysis
▶️ Explores why something happened
▶️ Tools: Correlation, root cause analysis, drill-downs
▶️ Example: Investigating why customer churn spiked last quarter

3️⃣ Predictive Analysis
▶️ Uses historical data to forecast future trends
▶️ Tools: Regression, time series analysis, machine learning
▶️ Example: Predicting next month's product demand

4️⃣ Prescriptive Analysis
▶️ Recommends actions based on predictions
▶️ Tools: Optimization models, decision trees
▶️ Example: Suggesting optimal inventory levels to reduce costs

5️⃣ Exploratory Data Analysis (EDA)
▶️ Initial investigation to find patterns and anomalies
▶️ Tools: Data visualization, summary statistics, outlier detection
▶️ Example: Visualizing user behavior on a website to identify trends

💬 Tap ❤️ for more!

Top 50 Data Analyst Interview Questions (2025) 🎯📊

1. What does a data analyst do?
2. Difference between data analyst, data scientist, and data engineer.
3. What are the key skills every data analyst must have?
4. Explain the data analysis process.
5. What is data wrangling or data cleaning?
6. How do you handle missing values?
7. What is the difference between structured and unstructured data?
8. How do you remove duplicates in a dataset?
9. What are the most common data types in Python or SQL?
10. What is the difference between INNER JOIN and LEFT JOIN?
11. Explain the concept of normalization in databases.
12. What are measures of central tendency?
13. What is standard deviation and why is it important?
14. Difference between variance and covariance.
15. What are outliers and how do you treat them?
16. What is hypothesis testing?
17. Explain p-value in simple terms.
18. What is correlation vs. causation?
19. How do you explain insights from a dashboard to non-technical stakeholders?
20. What tools do you use for data visualization?
21. Difference between Tableau and Power BI.
22. What is a pivot table?
23. How do you build a dashboard from scratch?
49. What do you do if data contradicts business intuition?
50. What are your favorite analytics tools and why?

🎓 Data Analyst Jobs:
https://whatsapp.com/channel/0029Vaxjq5a4dTnKNrdeiZ0J

💬 Tap ❤️ for the detailed answers!

SQL Interviews LOVE to test you on Window Functions. Here’s the list of 7 most popular window functions

👇 𝟕 𝐌𝐨𝐬𝐭 𝐓𝐞𝐬𝐭𝐞𝐝 𝐖𝐢𝐧𝐝𝐨𝐰 𝐅𝐮𝐧𝐜𝐭𝐢𝐨𝐧𝐬

* RANK() - gives a rank to each row in a partition based on a specified column or value

* DENSE_RANK() - gives a rank to each row, but DOESN'T skip rank values

* ROW_NUMBER() - gives a unique integer to each row in a partition based on the order of the rows

* LEAD() - retrieves a value from a subsequent row in a partition based on a specified column or expression

* LAG() - retrieves a value from a previous row in a partition based on a specified column or expression

* NTH_VALUE() - retrieves the nth value in a partition

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✅ SQL Window Functions – Part 1: 🧠

What Are Window Functions?
They perform calculations across rows related to the current row without reducing the result set. Common for rankings, comparisons, and totals.

1. RANK()
Assigns a rank based on order. Ties get the same rank, but next rank is skipped.

Syntax:
RANK() OVER (
PARTITION BY column
ORDER BY column
)
Example Table: Sales
| Employee | Region | Sales |
|----------|--------|-------|
| A | East | 500 |
| B | East | 600 |
| C | East | 600 |
| D | East | 400 |

Query:
SELECT Employee, Sales,
RANK() OVER (PARTITION BY Region ORDER BY Sales DESC) AS Rank
FROM Sales;

Result:
| Employee | Sales | Rank |
|----------|-------|------|
| B | 600 | 1 |
| C | 600 | 1 |
| A | 500 | 3 |
| D | 400 | 4 |

2. DENSE_RANK()
Same logic as RANK but does not skip ranks.

Query:
SELECT Employee, Sales,
DENSE_RANK() OVER (PARTITION BY Region ORDER BY Sales DESC) AS DenseRank
FROM Sales;

Result:
| Employee | Sales | DenseRank |
|----------|-------|-----------|
| B | 600 | 1 |
| C | 600 | 1 |
| A | 500 | 2 |
| D | 400 | 3 |

RANK vs DENSE_RANK
- RANK skips ranks after ties. Tie at 1 means next is 3
- DENSE_RANK does not skip. Tie at 1 means next is 2

💡 Use RANK when position gaps matter
💡 Use DENSE_RANK for continuous ranking

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🌐 Data Analytics Tools & Their Use Cases 📊📈

🔹 Excel ➜ Spreadsheet analysis, pivot tables, and basic data visualization
🔹 SQL ➜ Querying databases for data extraction and relational analysis
🔹 Tableau ➜ Interactive dashboards and storytelling with visual analytics
🔹 Power BI ➜ Business intelligence reporting and real-time data insights
🔹 Google Analytics ➜ Web traffic analysis and user behavior tracking
🔹 Python (with Pandas) ➜ Data manipulation, cleaning, and exploratory analysis
🔹 R ➜ Statistical computing and advanced graphical visualizations
🔹 Apache Spark ➜ Big data processing for distributed analytics workloads
🔹 Looker ➜ Semantic modeling and embedded analytics for teams
🔹 Alteryx ➜ Data blending, predictive modeling, and workflow automation
🔹 Knime ➜ Visual data pipelines for no-code analytics and ML
🔹 Splunk ➜ Log analysis and real-time operational intelligence

💬 Tap ❤️ if this helped!

📊 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝗲𝗿: How do you create a running total in SQL?

👋 𝗠𝗲 Use the WINDOW FUNCTION with OVER() clause:

  Date,
  Amount,
  SUM(Amount) OVER (ORDER BY Date) AS RunningTotal
FROM Sales;

🧠 Logic Breakdown: 
- SUM(Amount) → Aggregates the values 
- OVER(ORDER BY Date) → Maintains order for accumulation 
- No GROUP BY needed 

✅ Use Case: Track cumulative revenue, expenses, or orders by date

💡 SQL Tip:
Add PARTITION BY in OVER() if you want running totals by category or region.

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📊 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄𝗲𝗿: How do you get the 2nd highest salary in SQL?

👋 𝗠𝗲: Use ORDER BY with LIMIT or OFFSET, or a subquery.

MySQL / PostgreSQL (with LIMIT & OFFSET):

SELECT salary  
FROM employees  
ORDER BY salary DESC  
LIMIT 1 OFFSET 1;

Using Subquery (Works on most databases):

SELECT MAX(salary)  
FROM employees  
WHERE salary < (SELECT MAX(salary) FROM employees);

🧠 Logic Breakdown:
- First method sorts and skips the top result
- Second method finds the highest salary below the max

💡 Tip: Use DENSE_RANK() if multiple employees share the same salary rank

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✅ SQL Checklist for Data Analysts 🧠💻

📚 1. Understand SQL Basics
☑ What is SQL and how databases work
☑ Relational vs non-relational databases
☑ Table structure: rows, columns, keys

🧩 2. Core SQL Queries
☑ SELECT, FROM, WHERE
☑ ORDER BY, LIMIT
☑ DISTINCT, BETWEEN, IN, LIKE

🔗 3. Master Joins
☑ INNER JOIN
☑ LEFT JOIN / RIGHT JOIN
☑ FULL OUTER JOIN
☑ Practice combining data from multiple tables

📊 4. Aggregation & Grouping
☑ COUNT, SUM, AVG, MIN, MAX
☑ GROUP BY & HAVING
☑ Aggregate filtering

📈 5. Subqueries & CTEs
☑ Use subqueries inside SELECT/WHERE
☑ WITH clause for common table expressions
☑ Nested queries and optimization basics

🧮 6. Window Functions
☑ RANK(), ROW_NUMBER(), DENSE_RANK()
☑ PARTITION BY & ORDER BY
☑ LEAD(), LAG(), SUM() OVER

🧹 7. Data Cleaning with SQL
☑ Remove duplicates (DISTINCT, ROW_NUMBER)
☑ Handle NULLs
☑ Use CASE WHEN for conditional logic

🛠️ 8. Practice & Real Tasks
☑ Write queries from real datasets
☑ Analyze sales, customers, transactions
☑ Build reports with JOINs and aggregations

📁 9. Tools to Use
☑ PostgreSQL / MySQL / SQL Server
☑ db-fiddle, Mode Analytics, DataCamp, StrataScratch
☑ VS Code + SQL extensions

🚀 10. Interview Prep
☑ Practice 50+ SQL questions
☑ Solve problems on LeetCode, HackerRank
☑ Explain query logic clearly in mock interviews

💬 Tap ❤️ if this was helpful!

✅ Core SQL Queries You Should Know 📊💡

1️⃣ SELECT, FROM, WHERE
This is how you tell SQL what data you want, where to get it from, and how to filter it.
👉 SELECT = what columns
👉 FROM = which table
👉 WHERE = which rows
Example:
SELECT name, age FROM employees WHERE age > 30;
This shows names and ages of employees older than 30.

2️⃣ ORDER BY, LIMIT
Use when you want sorted results or only a few records.
👉 ORDER BY sorts data
👉 LIMIT reduces how many rows you get
Example:
SELECT name, salary FROM employees ORDER BY salary DESC LIMIT 3;
Shows top 3 highest paid employees.

3️⃣ DISTINCT
Removes duplicate values from a column.
Example:
SELECT DISTINCT department FROM employees;
Lists all unique departments from the employees table.

4️⃣ BETWEEN
Used for filtering within a range (numbers, dates, etc).
Example:
SELECT name FROM employees WHERE age BETWEEN 25 AND 35;
Shows names of employees aged 25 to 35.

5️⃣ IN
Use IN to match against multiple values in one go.
Example:
SELECT name FROM employees WHERE department IN ('HR', 'Sales');
Shows names of people working in HR or Sales.

6️⃣ LIKE
Used to match text patterns.
👉 % = wildcard (any text)
Example:
SELECT name FROM employees WHERE name LIKE 'A%';
Finds names starting with A.

💬 Double Tap ❤️ if this helped you!

✅ SQL Joins with Interview Q&A 🔗💻

Joins combine data from multiple tables via common columns—essential for relational databases and analytics in 2025.

1️⃣ INNER JOIN
Only matching records from both tables.

SELECT e.name, d.department_name  
FROM employees e  
INNER JOIN departments d ON e.dept_id = d.id;

Use: Employee names with their departments.

2️⃣ LEFT JOIN (LEFT OUTER JOIN)
All left table rows + matching right; NULLs for no match.

SELECT e.name, d.department_name  
FROM employees e  
LEFT JOIN departments d ON e.dept_id = d.id;

Use: All employees, even without departments.

3️⃣ RIGHT JOIN (RIGHT OUTER JOIN)
All right table rows + matching left.

SELECT e.name, d.department_name  
FROM employees e  
RIGHT JOIN departments d ON e.dept_id = d.id;

Use: All departments, even empty ones.

4️⃣ FULL OUTER JOIN
All rows from both; NULLs where no match (PostgreSQL/MySQL supports).

SELECT e.name, d.department_name  
FROM employees e  
FULL OUTER JOIN departments d ON e.dept_id = d.id;

Use: Spot unmatched records.

5️⃣ SELF JOIN
Table joins itself.

SELECT a.name AS Employee, b.name AS Manager  
FROM employees a  
JOIN employees b ON a.manager_id = b.id;

Use: Employee-manager hierarchy.

Real-World Interview Questions + Answers

Q1: What is the difference between INNER and OUTER JOIN?
A: INNER returns only matches; OUTER includes unmatched from one/both tables.

Q2: When would you use LEFT JOIN instead of INNER JOIN?
A: To keep all left table rows, even without right matches.

Q3: How can you find employees who don’t belong to any department?
A: LEFT JOIN + IS NULL filter.

SELECT e.name  
FROM employees e  
LEFT JOIN departments d ON e.dept_id = d.id  
WHERE d.department_name IS NULL;

Q4: How would you find mismatched data between two tables?
A: FULL OUTER JOIN + IS NULL on either side.

Q5: Can you join more than two tables?
A: Yes, chain JOINs: FROM A JOIN B ON... JOIN C ON...

💬 Tap ❤️ for more!

✅ How to Learn Data Analytics Step-by-Step 📊🚀

1️⃣ Understand the Basics
⦁ Learn what data analytics is & key roles (analyst, scientist, engineer)
⦁ Know the types: descriptive, diagnostic, predictive, prescriptive
⦁ Explore the data analytics lifecycle

2️⃣ Learn Excel / Google Sheets
⦁ Master formulas, pivot tables, VLOOKUP/XLOOKUP
⦁ Clean data, create charts & dashboards
⦁ Automate with basic macros

3️⃣ Learn SQL
⦁ Understand SELECT, WHERE, GROUP BY, JOINs
⦁ Practice window functions (RANK, LAG, LEAD)
⦁ Use platforms like PostgreSQL or MySQL

4️⃣ Learn Python (for Analytics)
⦁ Use Pandas for data manipulation
⦁ Use NumPy, Matplotlib, Seaborn for analysis & viz
⦁ Load, clean, and explore datasets

5️⃣ Master Data Visualization Tools
⦁ Learn Power BI or Tableau
⦁ Build dashboards, use filters, slicers, DAX/calculated fields
⦁ Tell data stories visually

6️⃣ Work on Real Projects
⦁ Sales analysis
⦁ Customer churn prediction
⦁ Marketing campaign analysis
⦁ EDA on public datasets

7️⃣ Learn Basic Stats & Business Math
⦁ Mean, median, standard deviation, distributions
⦁ Correlation, regression, hypothesis testing
⦁ A/B testing, ROI, KPIs

8️⃣ Version Control & Portfolio
⦁ Use Git/GitHub to share your projects
⦁ Document with Jupyter Notebooks or Markdown
⦁ Create a portfolio site or Notion page

9️⃣ Learn Dashboarding & Reporting
⦁ Automate reports with Python, SQL jobs
⦁ Build scheduled dashboards with Power BI / Looker Studio

🔟 Apply for Jobs / Freelance Gigs
⦁ Analyst roles, internships, freelance projects
⦁ Tailor your resume to highlight tools & projects

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✅ Data Analytics Basics You Must Know 📈🧠

1️⃣ What is Data Analytics?
➡️ The process of extracting insights from data to support decision-making.

2️⃣ 4 Types of Data Analytics
⦁ Descriptive: What happened?
⦁ Diagnostic: Why did it happen?
⦁ Predictive: What could happen?
⦁ Prescriptive: What should we do?

3️⃣ Common Data Types
⦁ Structured: Tables, rows, columns
⦁ Unstructured: Text, images, audio
⦁ Semi-structured: JSON, XML

4️⃣ Key Tools You’ll Use
⦁ Excel/Google Sheets
⦁ SQL (PostgreSQL, MySQL)
⦁ Python (Pandas, Matplotlib)
⦁ Tableau / Power BI

5️⃣ Common Tasks
⦁ Cleaning messy data
⦁ Creating visualizations
⦁ Running SQL queries
⦁ Finding trends & patterns
⦁ Communicating insights clearly

6️⃣ Top Skills Needed
⦁ Critical thinking
⦁ Business understanding
⦁ Data storytelling
⦁ Attention to detail

💬 Tap ❤️ for more!

✅ SQL Aggregations with Interview Q&A 📊🧮

Aggregation functions help summarize large datasets. Combine them with GROUP BY to analyze grouped data.

1️⃣ COUNT()
Returns the number of records.

SELECT COUNT(*) FROM employees;

2️⃣ SUM()
Adds up values in a column.

SELECT dept_id, SUM(salary)  
FROM employees  
GROUP BY dept_id;

3️⃣ AVG()
Returns the average of values.

SELECT AVG(salary) FROM employees;

4️⃣ MAX() / MIN()
Returns the highest/lowest value.

SELECT MAX(salary), MIN(salary) FROM employees;

5️⃣ GROUP BY
Groups rows that have the same values in specified columns.

SELECT dept_id, COUNT(*)  
FROM employees  
GROUP BY dept_id;

6️⃣ HAVING
Filters groups after aggregation (unlike WHERE which filters rows).

SELECT dept_id, AVG(salary)  
FROM employees  
GROUP BY dept_id  
HAVING AVG(salary) > 50000;

————————

Real-World Interview Questions + Answers

Q1: What’s the difference between WHERE and HAVING?
A: WHERE filters rows before grouping. HAVING filters after aggregation.

Q2: Can you use aggregate functions without GROUP BY?
A: Yes. Without GROUP BY, the function applies to the entire table.

Q3: How do you find departments with more than 5 employees?

SELECT dept_id, COUNT(*)  
FROM employees  
GROUP BY dept_id  
HAVING COUNT(*) > 5;

Q4: Can you group by multiple columns?
A: Yes.

GROUP BY dept_id, job_title

Q5: How do you calculate total and average salary per department?

SELECT dept_id, SUM(salary), AVG(salary)  
FROM employees  
GROUP BY dept_id;

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✅ SQL Subqueries with Interview Q&A 🔍🧠

Subqueries and CTEs help you write cleaner, modular, and more powerful SQL queries. They're often asked in interviews!

1️⃣ Subqueries (Nested Queries)
A query inside another query.

Example:

SELECT name  
FROM employees  
WHERE salary > (SELECT AVG(salary) FROM employees);

📌 Use case: Find employees earning above average.

Types:
⦁ In SELECT
⦁ In WHERE
⦁ In FROM (Inline Views)

2️⃣ Correlated Subqueries
Inner query depends on outer query.

Example:

SELECT name  
FROM employees e  
WHERE salary > (SELECT AVG(salary) FROM employees WHERE dept_id = e.dept_id);

📌 Use case: Find employees earning above average in their department.

3️⃣ Common Table Expressions (CTE)
Temporary result set using WITH. Improves readability.

Example:

WITH high_paid AS (
  SELECT name, salary FROM employees WHERE salary > 100000
)
SELECT * FROM high_paid;

📌 Use case: Simplify complex queries, recursive queries.

4️⃣ Recursive CTE
Used for hierarchical data (e.g. org charts, folders).

Example:

WITH RECURSIVE emp_tree AS (
  SELECT id, name, manager_id FROM employees WHERE manager_id IS NULL
  UNION ALL
  SELECT e.id, e.name, e.manager_id  
  FROM employees e
  JOIN emp_tree et ON e.manager_id = et.id
)
SELECT * FROM emp_tree;

🧠 Interview Questions

Q1: When should you use a subquery vs JOIN?
A: Use subquery when working with aggregates or filtering logic. JOINs are better for combining related data.

Q2: What's the difference between subquery and CTE?
A: Subquery is inline; CTE improves readability and can be reused in the query.

Q3: What is a correlated subquery?
A: It depends on data from the outer query. Runs row by row.

Q4: When do you use recursive CTEs?
A: For hierarchical/parent-child relationships like org charts, file systems.

Q5: Can subqueries be used in the FROM clause?
A: Yes, they're called derived tables or inline views.

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✅ SQL Window Functions 🪟📊

Window functions perform calculations across rows related to the current row without collapsing them like GROUP BY does.

1️⃣ ROW_NUMBER()
Gives a unique number to each row in a partition.

SELECT name, dept_id,
  ROW_NUMBER() OVER (
    PARTITION BY dept_id
    ORDER BY salary DESC
  ) AS rank
FROM employees;

📌 Use case: Rank employees by salary within each department.

2️⃣ RANK() vs DENSE_RANK()
⦁ RANK() → Skips numbers on ties (1, 2, 2, 4)
⦁ DENSE_RANK() → No gaps (1, 2, 2, 3)

SELECT name, salary,
  RANK() OVER (ORDER BY salary DESC) AS rnk,
  DENSE_RANK() OVER (ORDER BY salary DESC) AS dense_rnk
FROM employees;

3️⃣ LAG() and LEAD()
Access previous/next row values.

SELECT name, salary,
  LAG(salary)  OVER (ORDER BY id) AS prev_salary,
  LEAD(salary) OVER (ORDER BY id) AS next_salary
FROM employees;

📌 Use case: Compare current row to previous/next (e.g., salary or stock change).

4️⃣ NTILE(n)
Divides rows into n buckets.

SELECT name,
  NTILE(4) OVER (ORDER BY salary DESC) AS quartile
FROM employees;

📌 Use case: Quartiles/percentile-style grouping.

5️⃣ SUM(), AVG(), COUNT() with OVER()
Running totals, partition-wise aggregates, moving stats.

SELECT name, dept_id, salary,
  SUM(salary) OVER (PARTITION BY dept_id) AS dept_total
FROM employees;

🧠 Interview Q&A

Q1: Difference between GROUP BY and OVER()?
⦁ GROUP BY → Collapses rows into groups; one row per group.
⦁ OVER() → Keeps all rows; adds an extra column with the aggregate.

Q2: When would you use LAG()?
To compare current row values with previous ones (e.g., day‑to‑day revenue change, previous month’s balance).

Q3: What happens if no PARTITION BY is used?
The function runs over the entire result set as a single partition.

Q4: Can you sort inside OVER()?
Yes, ORDER BY inside OVER() defines the calculation order (needed for ranking, LAG/LEAD, running totals).

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