🚀 Top 10 Careers in Data Analytics (2026)📊💼

1️⃣ Data Analyst
▶️ Skills: Excel, SQL, Power BI, Data Cleaning, Data Visualization
💰 Avg Salary: ₹6–15 LPA (India) / 90K+ USD (Global)

2️⃣ Business Intelligence (BI) Analyst
▶️ Skills: Power BI, Tableau, SQL, Data Modeling, Dashboard Design
💰 Avg Salary: ₹8–18 LPA / 100K+

3️⃣ Product Analyst
▶️ Skills: SQL, Python, A/B Testing, Product Metrics, Experimentation
💰 Avg Salary: ₹12–25 LPA / 120K+

4️⃣ Analytics Engineer
▶️ Skills: SQL, dbt, Data Modeling, Data Warehousing, ETL
💰 Avg Salary: ₹12–22 LPA / 120K+

5️⃣ Marketing Analyst
▶️ Skills: Google Analytics, SQL, Excel, Customer Segmentation, Attribution Analysis
💰 Avg Salary: ₹7–16 LPA / 95K+

6️⃣ Financial Data Analyst
▶️ Skills: Excel, SQL, Forecasting, Financial Modeling, Power BI
💰 Avg Salary: ₹8–18 LPA / 105K+

7️⃣ Data Visualization Specialist
▶️ Skills: Tableau, Power BI, Storytelling with Data, Dashboard Design
💰 Avg Salary: ₹7–17 LPA / 100K+

8️⃣ Operations Analyst
▶️ Skills: SQL, Excel, Process Analysis, Business Metrics, Reporting
💰 Avg Salary: ₹6–15 LPA / 95K+

9️⃣ Risk & Fraud Analyst
▶️ Skills: SQL, Python, Fraud Detection Models, Statistical Analysis
💰 Avg Salary: ₹10–20 LPA / 110K+

🔟 Analytics Consultant
▶️ Skills: SQL, BI Tools, Business Strategy, Stakeholder Communication
💰 Avg Salary: ₹12–28 LPA / 125K+

📊 Data Analytics is one of the most practical and fastest ways to enter the tech industry in 2026.

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📊 Essential SQL Concepts Every Data Analyst Must Know

🚀 SQL is the most important skill for Data Analysts. Almost every analytics job requires working with databases to extract, filter, analyze, and summarize data.

Understanding the following SQL concepts will help you write efficient queries and solve real business problems with data.

1️⃣ SELECT Statement (Data Retrieval)

What it is: Retrieves data from a table.

SELECT name, salary
FROM employees;

Use cases: Retrieving specific columns, viewing datasets, extracting required information.

2️⃣ WHERE Clause (Filtering Data)

What it is: Filters rows based on specific conditions.

SELECT *
FROM orders
WHERE order_amount > 500;

Common conditions: =, >, <, >=, <=, BETWEEN, IN, LIKE

3️⃣ ORDER BY (Sorting Data)

What it is: Sorts query results in ascending or descending order.

SELECT name, salary
FROM employees
ORDER BY salary DESC;

Sorting options: ASC (default), DESC

4️⃣ GROUP BY (Aggregation)

What it is: Groups rows with same values into summary rows.

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

Use cases: Sales per region, customers per country, orders per product category.

5️⃣ Aggregate Functions

What they do: Perform calculations on multiple rows.

SELECT AVG(salary)
FROM employees;

Common functions: COUNT(), SUM(), AVG(), MIN(), MAX()

6️⃣ HAVING Clause

What it is: Filters grouped data after aggregation.

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

Key difference: WHERE filters rows before grouping, HAVING filters groups after aggregation.

7️⃣ SQL JOINS (Combining Tables)

What they do:

Combine tables.
-- INNER JOIN
SELECT orders.order_id, customers.customer_name
FROM orders
INNER JOIN customers
ON orders.customer_id = customers.customer_id;

-- LEFT JOIN
SELECT customers.customer_name, orders.order_id
FROM customers
LEFT JOIN orders
ON customers.customer_id = orders.customer_id;

Common types: INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL JOIN

8️⃣ Subqueries

What it is: Query inside another query.

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

Use cases: Comparing values, filtering based on aggregated results.

9️⃣ Common Table Expressions (CTE)

What it is: Temporary result set used inside a query.

WITH high_salary AS (
SELECT name, salary
FROM employees
WHERE salary > 70000
)
SELECT *
FROM high_salary;

Benefits: Cleaner queries, easier debugging, better readability.

🔟 Window Functions

What they do: Perform calculations across rows related to current row.

SELECT name, salary, RANK() OVER (ORDER BY salary DESC) AS salary_rank
FROM employees;

Common functions: ROW_NUMBER(), RANK(), DENSE_RANK(), LAG(), LEAD()

Why SQL is Critical for Data Analysts
• Extract data from databases
• Analyze large datasets efficiently
• Generate reports and dashboards
• Support business decision-making

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

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📂 Top Projects for Data Analytics Portfolio 🚀💻

📊 1. Sales Dashboard (Excel / Power BI / Tableau)
▶️ Analyze monthly/quarterly sales by region, category
▶️ Show KPIs: Revenue, YoY Growth, Profit Margin

🛍 2. E-commerce Customer Segmentation (Python + Clustering)
▶️ Use RFM (Recency, Frequency, Monetary) model
▶️ Visualize clusters with Seaborn / Plotly

📉 3. Churn Prediction Model (Python + ML)
▶️ Dataset: Telecom or SaaS customer data
▶️ Techniques: Logistic Regression, Decision Tree

📦 4. Supply Chain Delay Analysis (SQL + Tableau)
▶️ Identify causes of late deliveries using historical order data
▶️ Visualize supplier-wise performance

📈 5. A/B Testing for Product Feature (SQL + Python)
▶️ Simulate or use real test data (e.g. button click-through rates)
▶️ Metrics: Conversion Rate, Significance Test

📍 6. COVID-19 Trend Tracker (Python + Dash)
▶️ Scrape or pull live data from APIs
▶️ Show cases, recovery, testing rates by country

📅 7. HR Analytics – Attrition Analysis (Excel / Python)
▶️ Predict or explore employee exits
▶️ Use decision trees or visual storytelling

💡 Tip: Upload projects to GitHub + create a simple portfolio site or blog to stand out.

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Quick SQL functions cheat sheet for beginners ✍

Aggregate Functions

COUNT(*): Counts rows.

SUM(column): Total sum.

AVG(column): Average value.

MAX(column): Maximum value.

MIN(column): Minimum value.

String Functions

CONCAT(a, b, …): Concatenates strings.

SUBSTRING(s, start, length): Extracts part of a string.

UPPER(s) / LOWER(s): Converts string case.

TRIM(s): Removes leading/trailing spaces.

Date Time Functions

CURRENT_DATE / CURRENT_TIME / CURRENT_TIMESTAMP: Current date/time.

EXTRACT(unit FROM date): Retrieves a date part (e.g., year, month).

DATE_ADD(date, INTERVAL n unit): Adds an interval to a date.

Numeric Functions

ROUND(num, decimals): Rounds to a specified decimal.

CEIL(num) / FLOOR(num): Rounds up/down.

ABS(num): Absolute value.

MOD(a, b): Returns the remainder.

Control Flow Functions

CASE: Conditional logic.

COALESCE(val1, val2, …): Returns the first non-null value.

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Don't Confuse to learn Python.

Learn This Concept to be proficient in Python.

𝗕𝗮𝘀𝗶𝗰𝘀 𝗼𝗳 𝗣𝘆𝘁𝗵𝗼𝗻:
- Python Syntax
- Data Types
- Variables
- Operators
- Control Structures:
if-elif-else
Loops
Break and Continue
try-except block
- Functions
- Modules and Packages

𝗢𝗯𝗷𝗲𝗰𝘁-𝗢𝗿𝗶𝗲𝗻𝘁𝗲𝗱 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 𝗶𝗻 𝗣𝘆𝘁𝗵𝗼𝗻:
- Classes and Objects
- Inheritance
- Polymorphism
- Encapsulation
- Abstraction

𝗣𝘆𝘁𝗵𝗼𝗻 𝗟𝗶𝗯𝗿𝗮𝗿𝗶𝗲𝘀:
- Pandas
- Numpy

𝗣𝗮𝗻𝗱𝗮𝘀:
- What is Pandas?
- Installing Pandas
- Importing Pandas
- Pandas Data Structures (Series, DataFrame, Index)

𝗪𝗼𝗿𝗸𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗗𝗮𝘁𝗮𝗙𝗿𝗮𝗺𝗲𝘀:
- Creating DataFrames
- Accessing Data in DataFrames
- Filtering and Selecting Data
- Adding and Removing Columns
- Merging and Joining DataFrames
- Grouping and Aggregating Data
- Pivot Tables

𝗗𝗮𝘁𝗮 𝗖𝗹𝗲𝗮𝗻𝗶𝗻𝗴 𝗮𝗻𝗱 𝗣𝗿𝗲𝗽𝗮𝗿𝗮𝘁𝗶𝗼𝗻:
- Handling Missing Values
- Handling Duplicates
- Data Formatting
- Data Transformation
- Data Normalization

𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗧𝗼𝗽𝗶𝗰𝘀:
- Handling Large Datasets with Dask
- Handling Categorical Data with Pandas
- Handling Text Data with Pandas
- Using Pandas with Scikit-learn
- Performance Optimization with Pandas

𝗗𝗮𝘁𝗮 𝗦𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝘀 𝗶𝗻 𝗣𝘆𝘁𝗵𝗼𝗻:
- Lists
- Tuples
- Dictionaries
- Sets

𝗙𝗶𝗹𝗲 𝗛𝗮𝗻𝗱𝗹𝗶𝗻𝗴 𝗶𝗻 𝗣𝘆𝘁𝗵𝗼𝗻:
- Reading and Writing Text Files
- Reading and Writing Binary Files
- Working with CSV Files
- Working with JSON Files

𝗡𝘂𝗺𝗽𝘆:
- What is NumPy?
- Installing NumPy
- Importing NumPy
- NumPy Arrays

𝗡𝘂𝗺𝗣𝘆 𝗔𝗿𝗿𝗮𝘆 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀:
- Creating Arrays
- Accessing Array Elements
- Slicing and Indexing
- Reshaping Arrays
- Combining Arrays
- Splitting Arrays
- Arithmetic Operations
- Broadcasting

𝗪𝗼𝗿𝗸𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗗𝗮𝘁𝗮 𝗶𝗻 𝗡𝘂𝗺𝗣𝘆:
- Reading and Writing Data with NumPy
- Filtering and Sorting Data
- Data Manipulation with NumPy
- Interpolation
- Fourier Transforms
- Window Functions

𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝘄𝗶𝘁𝗵 𝗡𝘂𝗺𝗣𝘆:
- Vectorization
- Memory Management
- Multithreading and Multiprocessing
- Parallel Computing

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Quick Excel Functions Cheat Sheet for Beginners 📊✍️

Excel offers powerful functions for data analysis, calculations, and automation—perfect for beginners handling spreadsheets.

▎Aggregation Functions

• SUM(range): Totals all values in a range, e.g., SUM(A1:A10).
• AVERAGE(range): Computes the mean of numbers, ignoring blanks.
• COUNT(range): Counts cells with numbers.
• COUNTA(range): Counts non-empty cells.
• MAX(range): Finds the highest value.
• MIN(range): Finds the lowest value.

▎Lookup Functions

• VLOOKUP(value, table, col_index, [range_lookup]): Searches vertically for a value and returns from specified column.
• HLOOKUP(value, table, row_index, [range_lookup]): Searches horizontally.
• INDEX(range, row_num, [column_num]): Returns value at specific position.
• MATCH(lookup_value, range, [match_type]): Finds position of a value.

▎Logical Functions

• IF(condition, true_value, false_value): Executes based on condition, e.g., IF(A1>10, "High", "Low").
• AND(condition1, condition2): True if all conditions met.
• OR(condition1, condition2): True if any condition met.
• NOT(logical): Reverses TRUE/FALSE.

▎Text Functions

• CONCATENATE(text1, text2): Joins text strings (or use operator).
• LEFT(text, num_chars): Extracts from start.
• RIGHT(text, num_chars): Extracts from end.
• LEN(text): Counts characters.
• TRIM(text): Removes extra spaces.

▎Date Time Functions

• TODAY(): Current date.
• NOW(): Current date and time.
• YEAR(date): Extracts year.
• MONTH(date): Extracts month.
• DATEDIF(start_date, end_date, unit): Calculates interval (Y/M/D).

▎Math Stats Functions

• ROUND(number, num_digits): Rounds to digits.
• SUMIF(range, criteria, sum_range): Sums based on condition.
• COUNTIF(range, criteria): Counts based on condition.
• ABS(number): Absolute value.

Excel Resources: https://whatsapp.com/channel/0029VaifY548qIzv0u1AHz3i

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⚙️ Data Analytics Roadmap

📂 Excel/Google Sheets (VLOOKUP, Pivot Tables, Charts)
∟📂 SQL (SELECT, JOINs, GROUP BY, Window Functions)
∟📂 Python/R Basics (Pandas, Data Cleaning)
∟📂 Statistics (Descriptive, Inferential, Correlation)
∟📂 Data Visualization (Tableau, Power BI, Matplotlib)
∟📂 ETL Processes (Extract, Transform, Load)
∟📂 Dashboard Design (KPIs, Storytelling)
∟📂 Business Intelligence Tools (Looker, Metabase)
∟📂 Data Quality & Governance
∟📂 A/B Testing & Experimentation
∟📂 Advanced Analytics (Cohort Analysis, Funnel Analysis)
∟📂 Big Data Basics (Spark, Airflow)
∟📂 Communication (Reports, Presentations)
∟📂 Projects (Sales Dashboard, Customer Segmentation)
∟✅ Apply for Data Analyst / BI Analyst Roles

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Quick Python Cheat Sheet for Beginners 🐍✍️

Python is widely used for data analysis, automation, and AI—perfect for beginners starting their coding journey.

Aggregation Functions 📊

• sum(list) → Adds all values
👉 sum([1,2,3]) = 6
• len(list) → Counts total elements
👉 len([1,2,3]) = 3
• max(list) → Highest value
👉 max([4,7,2]) = 7
• min(list) → Lowest value
👉 min([4,7,2]) = 2
• sum(list)/len(list) → Average
👉 sum([10,20])/2 = 15

Lookup / Searching 🔍

• in → Check existence
👉 5 in [1,2,5] = True
• list.index(value) → Position of value
👉 [10,20,30].index(20) = 1
• Dictionary lookup
👉 data = {"name": "John", "age": 25} data["name"] # John

Logical Operations 🧠

• if condition: → Decision making
👉 if x > 10: print("High") else: print("Low")
• and → All conditions true
• or → Any condition true
• not → Reverse condition

Text (String) Functions 🔤

• len(text) → Length
👉 len("hello") = 5
• text.lower() → Lowercase
• text.upper() → Uppercase
• text.strip() → Remove spaces
👉 " hi ".strip() = "hi"
• text.replace(old, new)
👉 "hi".replace("h","H") = "Hi"
• String concatenation
👉 "Hello " + "World"

Date Time Functions 📅

• from datetime import datetime
• datetime.now() → Current date time
• Extract values:
now = datetime.now() now.year now.month now.day

Math Functions ➗

• import math
• math.sqrt(x) → Square root
• math.ceil(x) → Round up
• math.floor(x) → Round down
• abs(x) → Absolute value

Conditional Aggregation (Like Excel SUMIF) ⚡

• Using list comprehension

nums = [10, 20, 30, 40] sum(x for x in nums if x > 20) # 70

• Count condition

len([x for x in nums if x > 20]) # 2

Pro Tip for Data Analysts 💡

👉 For real-world work, use libraries: pandas & numpy

Example:
import pandas as pd df["salary"].mean()

Python Resources: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L

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✅ SQL Real-world Interview Questions with Answers 🖥️

📊 TABLE: employees

id | name | department | salary
1 | Rahul | IT | 50000
2 | Priya | IT | 70000
3 | Amit | HR | 60000
4 | Neha | HR | 70000
5 | Karan | IT | 80000
6 | Simran | HR | 60000

🎯 1️⃣ Find the 2nd highest salary
🧠 Logic: Get highest salary Then find max salary below that

✅ Query:
SELECT MAX(salary) FROM employees WHERE salary < ( SELECT MAX(salary) FROM employees );

🎯 2️⃣ Find employees earning more than average salary
🧠 Logic: Calculate overall average salary Compare each employee

✅ Query:
SELECT name, salary FROM employees WHERE salary > ( SELECT AVG(salary) FROM employees );

🎯 3️⃣ Find highest salary in each department
🧠 Logic: Group by department Use MAX

✅ Query:
SELECT department, MAX(salary) AS highest_salary FROM employees GROUP BY department;

🎯 4️⃣ Find top 2 highest salaries in each department
🧠 Logic: Use ROW_NUMBER Partition by department Filter top 2

✅ Query:
SELECT * FROM (
SELECT name, department, salary,
ROW_NUMBER() OVER( PARTITION BY department ORDER BY salary DESC ) r
FROM employees
) t WHERE r <= 2;

🎯 5️⃣ Find employees earning more than their department average
🧠 Logic: Use correlated subquery Compare with department avg

✅ Query:
SELECT e.name, e.department, e.salary
FROM employees e
WHERE e.salary > (
SELECT AVG(salary) FROM employees WHERE department = e.department
);

⭐ What Interviewer Checks Here
These 5 questions test:
✔ Subqueries
✔ GROUP BY
✔ Window functions
✔ Correlated queries
✔ Real business logic

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

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🎯 📊 DATA ANALYST MOCK INTERVIEW (WITH ANSWERS)

🧠 1️⃣ Tell me about yourself
✅ Sample Answer:
“I have around 3 years of experience working with data. My core skills include SQL, Excel, and Power BI. I regularly work with data cleaning, transformation, and building dashboards to generate business insights. Recently, I’ve also been strengthening my Python skills for data analysis. I enjoy solving business problems using data and presenting insights in a simple and actionable way.”

📊 2️⃣ What is the difference between WHERE and HAVING?
✅ Answer:
WHERE filters rows before aggregation
HAVING filters after aggregation
Example:
SELECT department, COUNT(*)
FROM employees
GROUP BY department
HAVING COUNT(*) > 5;

🔗 3️⃣ Explain different types of JOINs
✅ Answer:
INNER JOIN → only matching records
LEFT JOIN → all left + matching right
RIGHT JOIN → all right + matching left
FULL JOIN → all records from both
👉 In analytics, LEFT JOIN is most used.

🧠 4️⃣ How do you find duplicate records in SQL?
✅ Answer:
SELECT column, COUNT(*)
FROM table
GROUP BY column
HAVING COUNT(*) > 1;

👉 Used for data cleaning.

📈 5️⃣ What are window functions?
✅ Answer:
“Window functions perform calculations across rows without reducing the number of rows. They are used for ranking, running totals, and comparisons.”
Example:
SELECT salary, RANK() OVER(ORDER BY salary DESC)
FROM employees;

📊 6️⃣ How do you handle missing data?
✅ Answer:
Remove rows (if small impact)
Replace with mean/median
Use default values
Use interpolation (advanced)
👉 Depends on business context.

📉 7️⃣ What is the difference between COUNT(_) and COUNT(column)?
✅ Answer:
COUNT(*) → counts all rows
COUNT(column) → ignores NULL values

📊 8️⃣ What is a KPI? Give example
✅ Answer:
“KPI (Key Performance Indicator) is a measurable value used to track performance.”
Examples: Revenue growth, Conversion rate, Customer retention

🧠 9️⃣ How would you find the 2nd highest salary?
✅ Answer:
SELECT MAX(salary)
FROM employees
WHERE salary < ( SELECT MAX(salary) FROM employees );

📊 🔟 Explain your dashboard project
✅ Strong Answer:
“I created a sales dashboard in Power BI where I analyzed revenue trends, top-performing products, and regional performance. I used DAX for calculations and added filters for better interactivity. This helped stakeholders identify key areas for growth.”

🔥 1️⃣1️⃣ What is normalization?
✅ Answer:
“Normalization is the process of organizing data to reduce redundancy and improve data integrity.”

📊 1️⃣2️⃣ Difference between INNER JOIN and LEFT JOIN?
✅ Answer:
INNER JOIN → only matching data
LEFT JOIN → keeps all left table data
👉 LEFT JOIN is preferred in analytics.

🧠 1️⃣3️⃣ What is a CTE?
✅ Answer:
“A CTE (Common Table Expression) is a temporary result set defined using WITH clause to improve readability.”

📈 1️⃣4️⃣ How do you explain insights to non-technical people?
✅ Answer:
“I focus on storytelling. Instead of technical terms, I explain insights in simple business language with visuals and examples.”

📊 1️⃣5️⃣ What tools have you used?
✅ Answer:
SQL, Excel, Power BI, Python (basic/advanced depending on you)

💼 1️⃣6️⃣ Behavioral Question: Tell me about a challenge
✅ Answer:
“While working on a dataset, I found inconsistencies in data. I cleaned and standardized it using SQL and Excel, ensuring accurate analysis. This improved the dashboard reliability.”

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🔰 Data Analyst Roadmap 2026

├── 🗃 Introduction to Data Analysis
│ ├── Role overview & career paths
│ ├── Key skills: SQL, Excel, storytelling
│ └── Tools ecosystem (Colab, Tableau Public)

├── 📊 Excel Mastery (Formulas, Pivots)
│ ├── VLOOKUP, INDEX-MATCH, XLOOKUP
│ ├── PivotTables, slicers, Power Query
│ ├── Charts & conditional formatting
│ └── ETL basics in spreadsheets

├── 🔍 SQL for Analytics (Joins, Aggregates)
│ ├── Advanced SELECT with WHERE, GROUP BY
│ ├── JOINS (INNER, LEFT, window functions)
│ └── Performance: indexes, EXPLAIN plans

├── 📈 Visualization Principles (Charts, Dashboards)
│ ├── Chart types (bar, line, heatmaps)
│ ├── Design rules (avoid chart junk)
│ └── Color theory & accessibility

├── 🐍 Python Basics (Pandas, NumPy)
│ ├── DataFrames: load, clean, merge
│ ├── Grouping, pivoting, NumPy arrays
│ └── Jupyter notebooks & stats intro

├── 🔢 Statistics Fundamentals (Averages, Tests)
│ ├── Descriptive (mean, median, distributions)
│ ├── Hypothesis testing (t-tests, chi-square)
│ └── A/B testing & confidence intervals

├── 🛠 Tableau/Power BI Essentials
│ ├── Tableau: calculated fields, LOD
│ ├── Power BI: DAX, data modeling
│ └── Interactive dashboards & storytelling

├── 🤖 AI Tools for Insights (Prompts, AutoML)
│ ├── Prompt engineering for SQL/viz
│ ├── Tableau Einstein, Power BI Copilot
│ └── AutoML basics (no-code modeling)

├── ☁️ Cloud Platforms (BigQuery Basics)
│ ├── BigQuery SQL & massive datasets
│ ├── AWS QuickSight, Snowflake intro
│ └── Free tier cost optimization

├── 📊 Data Storytelling Frameworks
│ ├── Pyramid Principle for reports
│ ├── KPI dashboards & executive summaries
│ └── Narrative structure (context-insight-action)

├── 🔗 ETL Pipelines Intro (dbt, Airflow)
│ ├── Data transformation with dbt
│ ├── Orchestration (Airflow basics)
│ └── No-code: Zapier automation

├── 💼 Portfolio & Interview Prep
│ ├── 3-5 projects (sales, churn analysis)
│ ├── Kaggle datasets & GitHub portfolio
│ └── STAR method, mock interviews

└── 🧪 Real-world Challenges (Kaggle, Cases)
├── E-commerce churn prediction
├── Marketing ROI analysis
├── Supply chain optimization
└── LeetCode SQL, case studies

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