Data Analytics

📊 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. 💬 Tap ❤️ for more!

Sometimes reality outpaces expectations in the most unexpected ways. While global AI development seems increasingly fragmented, Sber just released Europe's largest open-source AI collection—full weights, code, and commercial rights included. ✅ No API paywalls. ✅ No usage restrictions. ✅ Just four complete model families ready to run in your private infrastructure, fine-tuned on your data, serving your specific needs. What makes this release remarkable isn't merely the technical prowess, but the quiet confidence behind sharing it openly when others are building walls. Find out more in the article from the developers. GigaChat Ultra Preview: 702B-parameter MoE model (36B active per token) with 128K context window. Trained from scratch, it outperforms DeepSeek V3.1 on specialized benchmarks while maintaining faster inference than previous flagships. Enterprise-ready with offline fine-tuning for secure environments. GitHub | HuggingFace | GitVerse GigaChat Lightning offers the opposite balance: compact yet powerful MoE architecture running on your laptop. It competes with Qwen3-4B in quality, matches the speed of Qwen3-1.7B, yet is significantly smarter and larger in parameter count. Lightning holds its own against the best open-source models in its class, outperforms comparable models on different tasks, and delivers ultra-fast inference—making it ideal for scenarios where Ultra would be overkill and speed is critical. Plus, it features stable expert routing and a welcome bonus: 256K context support. GitHub | Hugging Face | GitVerse Kandinsky 5.0 brings a significant step forward in open generative models. The flagship Video Pro matches Veo 3 in visual quality and outperforms Wan 2.2-A14B, while Video Lite and Image Lite offer fast, lightweight alternatives for real-time use cases. The suite is powered by K-VAE 1.0, a high-efficiency open-source visual encoder that enables strong compression and serves as a solid base for training generative models. This stack balances performance, scalability, and practicality—whether you're building video pipelines or experimenting with multimodal generation. GitHub | GitVerse | Hugging Face | Technical report Audio gets its upgrade too: GigaAM-v3 delivers speech recognition model with 50% lower WER than Whisper-large-v3, trained on 700k hours of audio with punctuation/normalization for spontaneous speech. GitHub | HuggingFace | GitVerse Every model can be deployed on-premises, fine-tuned on your data, and used commercially. It's not just about catching up – it's about building sovereign AI infrastructure that belongs to everyone who needs it.

🌐 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 🔹 Zoho Analytics ➜ Cloud-based reporting and multi-source data integration 🔹 SAS Viya ➜ AI-driven analytics for secure enterprise modeling 🔹 DataRobot ➜ Automated ML for predictive analytics and forecasting 💬 Tap ❤️ if this helped!

✅ 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 Double Tap ♥️ For More

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 React ❤️ for the detailed explanation

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!

Greetings from PVR Cloud Tech!! 🌈 🚀 Along with our highly successful Azure Data Engineering program, we are now launching a brand-new Data Engineering with Snowflake, DBT, and Airflow training track! Course: Snowflake + DBT + Airflow 📌 Start Date: 24th Nov 2025 ⏰ Time:  8 PM – 9 PM IST | Monday 🔹 Course Content: https://drive.google.com/file/d/1luKHrhYZ6zKuXZpVPGzMydrU_6R2yQnL/view 📱 Join WhatsApp Group: https://chat.whatsapp.com/EZghn5PVmryDgJZ1TjIMRk?mode=wwt 📥 Register Now: https://forms.gle/Vaofd52rkJcUpKPV7 📺 WhatsApp Channel: https://www.whatsapp.com/channel/0029Vb60rGU8V0thkpbFFW2n Team   PVR Cloud Tech:)  +91-9346060794

📊 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!

Tune in to the 10th AI Journey 2025 international conference: scientists, visionaries, and global AI practitioners will come together on one stage. Here, you will hear the voices of those who don't just believe in the future—they are creating it! Speakers include visionaries Kai-Fu Lee and Chen Qufan, as well as dozens of global AI gurus! Do you agree with their predictions about AI? On the first day of the conference, November 19, we will talk about how AI is already being used in various areas of life, helping to unlock human potential for the future and changing creative industries, and what impact it has on humans and on a sustainable future. On November 20, we will focus on the role of AI in business and economic development and present technologies that will help businesses and developers be more effective by unlocking human potential. On November 21, we will talk about how engineers and scientists are making scientific and technological breakthroughs and creating the future today! The day's program includes presentations by scientists from around the world: - Ajit Abraham (Sai University, India) will present on “Generative AI in Healthcare” - Nebojša Bačanin Džakula (Singidunum University, Serbia) will talk about the latest advances in bio-inspired metaheuristics - AIexandre Ferreira Ramos (University of São Paulo, Brazil) will present his work on using thermodynamic models to study the regulatory logic of transcriptional control at the DNA level - Anderson Rocha (University of Campinas, Brazil) will give a presentation entitled “AI in the New Era: From Basics to Trends, Opportunities, and Global Cooperation”. And in the special AIJ Junior track, we will talk about how AI helps us learn, create and ride the wave with AI. The day will conclude with an award ceremony for the winners of the AI Challenge for aspiring data scientists and the AIJ Contest for experienced AI specialists. The results of an open selection of AIJ Science research papers will be announced. Ride the wave with AI into the future! Tune in to the AI Journey webcast on November 19-21.

🧠 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 💬 Tap ❤️ for more!

📊 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 💬 Tap ❤️ for more!

The program for the 10th AI Journey 2025 international conference has been unveiled: scientists, visionaries, and global AI practitioners will come together on one stage. Here, you will hear the voices of those who don't just believe in the future—they are creating it! Speakers include visionaries Kai-Fu Lee and Chen Qufan, as well as dozens of global AI gurus from around the world! On the first day of the conference, November 19, we will talk about how AI is already being used in various areas of life, helping to unlock human potential for the future and changing creative industries, and what impact it has on humans and on a sustainable future. On November 20, we will focus on the role of AI in business and economic development and present technologies that will help businesses and developers be more effective by unlocking human potential. On November 21, we will talk about how engineers and scientists are making scientific and technological breakthroughs and creating the future today! Ride the wave with AI into the future! Tune in to the AI Journey webcast on November 19-21.

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 💬 Double Tap ❤️ For More!

✅ 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. 💬 React ❤️ for more!

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 React "❤️" For More!

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 👇 https://whatsapp.com/channel/0029Vau5fZECsU9HJFLacm2a React "❤️" For More :)

🧑‍💼 Interviewer: What’s the difference between DELETE and TRUNCATE? 👨‍💻 Me: Both commands are used to remove data from a table, but they work differently: 🔹 DELETE  – Removes rows one by one, based on a WHERE condition (optional).  – Logs each row deletion, so it’s slower.  – Can be rolled back if used within a transaction.  – Triggers can fire on deletion. 🔹 TRUNCATE  – Removes all rows instantly—no WHERE clause allowed.  – Faster, uses minimal logging.  – Cannot delete specific rows—it's all or nothing.  – Usually can’t be rolled back in some databases. 🧪 Example:

-- DELETE only inactive users
DELETE FROM users WHERE status = 'inactive';

-- TRUNCATE entire users table
TRUNCATE TABLE users;

💡 Tip: Use DELETE when you need conditions. Use TRUNCATE for a quick full cleanup. 💬 Tap ❤️ if this helped you!

✅ SQL Interview Challenge – Filter Top N Records per Group 🧠💾 🧑‍💼 Interviewer: How would you fetch the top 2 highest-paid employees per department? 👨‍💻 Me: Use ROW_NUMBER() with a PARTITION BY clause—it's a window function that numbers rows uniquely within groups, resetting per partition for precise top-N filtering. 🔹 SQL Query:

SELECT *
FROM (
  SELECT name, department, salary,
         ROW_NUMBER() OVER (PARTITION BY department ORDER BY salary DESC) AS rn
  FROM employees
) AS ranked
WHERE rn <= 2;

✔ Why it works: – PARTITION BY department resets row numbers (starting at 1) for each dept group, treating them as mini-tables. – ORDER BY salary DESC ranks highest first within each partition. – WHERE rn <= 2 grabs the top 2 per group—subquery avoids duplicates in complex joins! 💡 Pro Tip: Swap to RANK() if ties get equal ranks (e.g., two at #1 means next is #3, but you might get 3 rows); DENSE_RANK() avoids gaps. For big datasets, this scales well in SQL Server or Postgres. 💬 Tap ❤️ for more!

✅ Top 5 SQL Aggregate Functions with Examples 📊💡 1️⃣ COUNT() Counts rows or non-null values—use COUNT(*) for total rows, COUNT(column) to skip nulls. Example:

SELECT COUNT(*) AS total_employees FROM Employees;

Tip: In a 1k-row table, it returns 1k; great for validating data completeness. 2️⃣ SUM() Adds up numeric values—ignores nulls automatically. Example:

SELECT SUM(salary) AS total_salary FROM Employees;

Tip: For March orders totaling $60, it sums to 60; pair with WHERE for filtered totals like monthly payroll. 3️⃣ AVG() Calculates average of numeric values—also skips nulls, divides sum by non-null count. Example:

SELECT AVG(salary) AS average_salary FROM Employees;

Tip: Two orders at $20/$40 avg to 30; use for trends, like mean salary ~$75k in tech firms. 4️⃣ MAX() Finds the highest value in a column—works on numbers, dates, strings. Example:

SELECT MAX(salary) AS highest_salary FROM Employees;

Tip: Max order of $40 in a set; useful for peaks, like top sales $150k. 5️⃣ MIN() Finds the lowest value in a column—similar to MAX but for mins. Example:

SELECT MIN(salary) AS lowest_salary FROM Employees;

Tip: Min order of $10; spot outliers, like entry-level pay ~$50k. Bonus Combo Query:

SELECT COUNT(*) AS total,
       SUM(salary) AS total_pay,
       AVG(salary) AS avg_pay,
       MAX(salary) AS max_pay,
       MIN(salary) AS min_pay
FROM Employees;

💬 Tap ❤️ for more!

✅ How much 𝗣𝘆𝘁𝗵𝗼𝗻 is enough to crack a 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄? 🐍💻 📌 𝗕𝗮𝘀𝗶𝗰 𝗣𝘆𝘁𝗵𝗼𝗻 𝗦𝗸𝗶𝗹𝗹𝘀 - Data types: Lists, Dicts, Tuples, Sets - Loops & conditionals (for, while, if-else) - Functions & lambda expressions - File handling (open, read, write) 📊 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 𝘄𝗶𝘁𝗵 𝗣𝗮𝗻𝗱𝗮𝘀 - read_csv, head(), info() - Filtering, sorting, and grouping data - Handling missing values - Merging & joining DataFrames 📈 𝗗𝗮𝘁𝗮 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 - Matplotlib: plot(), bar(), hist() - Seaborn: heatmap(), pairplot(), boxplot() - Plot styling, titles, and legends 🧮 𝗡𝘂𝗺𝗣𝘆 & 𝗠𝗮𝘁𝗵 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻 - Arrays and broadcasting - Vectorized operations - Basic statistics: mean, median, std 🧩 𝗗𝗮𝘁𝗮 𝗖𝗹𝗲𝗮𝗻𝗶𝗻𝗴 & 𝗣𝗿𝗲𝗽 - Remove duplicates, rename columns - Apply functions row-wise or column-wise - Convert data types, parse dates ⚙️ 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗣𝘆𝘁𝗵𝗼𝗻 𝗧𝗶𝗽𝘀 - List comprehensions - Exception handling (try-except) - Working with APIs (requests, json) - Automating tasks with scripts 💼 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝗦𝗰𝗲𝗻𝗮𝗿𝗶𝗼𝘀 - Sales forecasting - Web scraping for data - Survey result analysis - Excel automation with openpyxl or xlsxwriter ✅ Must-Have Strengths: - Data wrangling & preprocessing - EDA (Exploratory Data Analysis) - Writing clean, reusable code - Extracting insights & telling stories with data Python Programming Resources: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L 💬 Tap ❤️ for more!