Data Science & Machine Learning (@datasciencefun) — Telegram channel

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Data Science & Machine Learning
23 Sept, 20:21edited
❌ Mistake 2: Forgetting quotes around textIncorrect:WHERE city = Pune;Correct:WHERE city = 'Pune';❌ Mistake 3: Using AND when you mean ORIncorrect if you want either city:WHERE city = 'Pune' AND city = 'Mumbai';A single city value cannot normally be both at the same time.Correct:WHERE city = 'Pune' OR city = 'Mumbai';Or:WHERE city IN ('Pune', 'Mumbai');❌ Mistake 4: Forgetting parenthesesFor complex conditions, use parentheses:WHERE (city = 'Pune' OR city = 'Mumbai') AND age > 30;❌ Mistake 5: Assuming BETWEEN excludes the boundariesBETWEEN is generally inclusive.
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Data Science & Machine Learning
23 Sept, 20:21
But IN makes this much cleaner:SELECT FROM customers WHERE city IN ('Pune', 'Mumbai', 'Delhi');IN checks whether a value belongs to a specified list.🔹 23. NOT INYou can also exclude multiple values.SELECT FROM customers WHERE city NOT IN ('Pune', 'Mumbai');This returns customers whose city isn't Pune or Mumbai.🔹 24. LIKELIKE is used for pattern matching.Suppose we want names beginning with A.SELECT FROM customers WHERE name LIKE 'A%';Here:% → Any sequence of charactersSo this could match:AliceAmitAnanya
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Data Science & Machine Learning
23 Sept, 20:21
SELECT FROM customers WHERE city = 'Pune' OR city = 'Mumbai';This returns customers from either Pune or Mumbai.🔹 14. AND vs ORConsider:WHERE age > 30 AND city = 'Pune'A customer must satisfy both conditions.But:WHERE age > 30 OR city = 'Pune'A customer only needs to satisfy one or both conditions.This difference is extremely important.🔹 15. NOTNOT reverses a condition.Example:SELECT FROM customers WHERE NOT city = 'Pune';This returns customers who aren't from Pune.You can also commonly write:SELECT
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Data Science & Machine Learning
23 Sept, 20:21
🚀 Data Science Roadmap 2026📍 Phase 3: SQL for Data Science📖 Topic 2: SQL Basics — WHEREAfter learning SELECT, the next essential SQL concept is WHERE.In real-world Data Science, databases can contain millions or billions of records. You usually don't want to retrieve everything.You want to answer questions such as:Which customers are from Mumbai?Which orders are above ₹10,000?Which employees joined after 2023?Which transactions were successful?Which products belong to a particular category?The WHERE clause allows you to filter rows based on conditions.🔹 1. What Is WHERE?WHERE is used to filter records based on a specified condition.Basic syntax:SELECT column1, column2 FROM table_name WHERE condition;Example:SELECT FROM customers WHERE city = 'Mumbai';
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Data Science & Machine Learning
23 Sept, 15:12
Soft skills questions will be part of your next data job interview!Here is what you should prepare for:1. 𝗖𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮𝘁𝗶𝗼𝗻: Be ready to discuss how you explain complex data insights to non-technical stakeholders.𝘌𝘹𝘢𝘮𝘱𝘭𝘦 𝘲𝘶𝘦𝘴𝘵𝘪𝘰𝘯: “How do you ensure that your data insights are understood and get used by non-technical stakeholders?”2. 𝗧𝗲𝗮𝗺 𝗖𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻: Show your ability to work well with others.𝘌𝘹𝘢𝘮𝘱𝘭𝘦 𝘲𝘶𝘦𝘴𝘵𝘪𝘰𝘯: “Can you talk about a time when you had to manage a conflict within a team? How did you resolve it?”3. 𝗣𝗿𝗼𝗯𝗹𝗲𝗺-𝗦𝗼𝗹𝘃𝗶𝗻𝗴: Highlight your critical thinking and problem-solving skills.𝘌𝘹𝘢𝘮𝘱𝘭𝘦 𝘲𝘶𝘦𝘴𝘵𝘪𝘰𝘯: “Describe a situation where you had to make a quick decision based on incomplete data. What was the outcome?”4. 𝗔𝗱𝗮𝗽𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆: Demonstrate your flexibility and openness to change.𝘌𝘹𝘢𝘮𝘱𝘭𝘦 𝘲𝘶𝘦𝘴𝘵𝘪𝘰𝘯: “How do you handle sudden changes in project priorities or scope?”5. 𝗧𝗶𝗺𝗲 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁: Prove your ability to manage multiple tasks and deadlines.𝘌𝘹𝘢𝘮𝘱𝘭𝘦 𝘲𝘶𝘦𝘴𝘵𝘪𝘰𝘯: “Tell me about a time when you were under tight deadlines. How did you manage to meet them?”6. 𝗘𝗺𝗽𝗮𝘁𝗵𝘆 𝗮𝗻𝗱 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴: Show your ability to understand stakeholder needs.𝘌𝘹𝘢𝘮𝘱𝘭𝘦 𝘲𝘶𝘦𝘴𝘵𝘪𝘰𝘯: “How do you approach understanding the needs of different stakeholders when starting a new project?”Structure your answers using the STAR method (Situation, Task, Action, Result). This helps you provide clear and concise responses that highlight your skills.By preparing for these soft skills questions, you’ll demonstrate that you’re not just technically fit, but also a well-rounded professional ready to make an impact on the business.You can find useful tips to improve your soft skills here: 👇 https://t.me/englishlearnerspro/
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Data Science & Machine Learning
22 Sept, 15:05
🚀 𝗧𝗼𝗽 𝟳 𝗙𝗥𝗘𝗘 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗟𝗲𝗮𝗿𝗻 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀! 📊Want to start a career in Data Analytics?Explore these 7 free Microsoft-backed learning resources covering Power BI, Excel, SQL and data fundamentals🔗 𝗔𝗰𝗰𝗲𝘀𝘀 𝘁𝗵𝗲 𝗙𝗥𝗘𝗘 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 👇https://pdlink.in/3Tm2D3Z💡 Ideal for students, freshers and professionals who want to build practical data skills.
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Data Science & Machine Learning
22 Sept, 07:18
🎓 𝗦𝘁𝗮𝗻𝗳𝗼𝗿𝗱 𝗨𝗻𝗶𝘃𝗲𝗿𝘀𝗶𝘁𝘆 𝗙𝗥𝗘𝗘 𝗢𝗻𝗹𝗶𝗻𝗲 𝗖𝗼𝘂𝗿𝘀𝗲𝘀! 🚀Explore free online learning opportunities from Stanford University across technology, business and more!💻 Tech & Programming 🤖 Artificial Intelligence & Data Science 💼 Business & Entrepreneurship 💡 Leadership & Innovation🔗 𝗘𝘅𝗽𝗹𝗼𝗿𝗲 𝘁𝗵𝗲 𝗙𝗥𝗘𝗘 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 👇https://pdlink.in/4hlnZGw🎯 Great for students, freshers and working professionals looking to expand their knowledge.
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Data Science & Machine Learning
21 Sept, 11:35edited
order_id | customer_id | product | price 1 | 101 | Laptop | 60000 2 | 102 | Phone | 30000 3 | 101 | Mouse | 1000To retrieve order details:SELECT order_id, customer_id, product, price FROM orders;To calculate price after adding a hypothetical 10% increase:SELECT product, price, price 1.10 AS increased_price
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Data Science & Machine Learning
21 Sept, 11:35
you tell the database:What data you wantYou generally don't tell the database exactly how to retrieve it internally.The database's query optimizer determines an efficient execution strategy.This is one reason SQL is called a declarative language.🔹 11. SQL KeywordsSQL uses keywords such as:SELECTFROMWHERE
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Data Science & Machine Learning
21 Sept, 11:35
🚀 Complete Data Science Roadmap 2026📍 Phase 3: SQL for Data Science📖 Topic 1: SQL Basics — SELECTSQL is one of the most important skills for a Data Scientist because real-world data is often stored in relational databases.Before using Python, Machine Learning, or advanced analytics, you will frequently need to:Retrieve dataFilter dataCombine tablesAggregate informationCreate datasets for analysis
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Data Science & Machine Learning
21 Sept, 05:21
𝗙𝗥𝗘𝗘 𝗔𝗜 𝗖𝗮𝗿𝗲𝗲𝗿 𝗠𝗮𝘀𝘁𝗲𝗿𝗰𝗹𝗮𝘀𝘀 🚀Join this expert-led masterclass and discover how to become industry-ready for high-growth AI roles.📅 Date: 24 September 2026 ⏰ Time: 7:00 PM–9:00 PM IST 🌐 Mode: Online 🎓 Certificate: Available to all attendeesEligibility :- Graduates Passing In 2025 or earlier🔗 𝗥𝗲𝗴𝗶𝘀𝘁𝗲𝗿 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘 👇https://pdlink.in/4xAMeGW⚡ Register now and take your first step towards a successful career in AI!
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Data Science & Machine Learning
20 Sept, 19:43edited
The posterior mean is: 9 / (9 + 5) = 9 / 14 ≈ 0.643🔹 24. Common Mistakes❌ Mistake 1: Thinking the prior is always subjective - A prior can come from historical data, previous studies, domain knowledge.❌ Mistake 2: Confusing likelihood with posterior - Likelihood = P(Data | Parameter), Posterior = P(Parameter | Data). They are not the same.❌ Mistake 3: Ignoring the base rate - The prior probability can have a major impact, especially when an event is rare.❌ Mistake 4: Confusing confidence intervals with credible intervals - They have different statistical interpretations.❌ Mistake 5: Thinking Bayesian methods ignore data - They don't. Bayesian inference combines prior information with observed evidence.🔹 25. Interview Perspective💡 What is Bayesian Statistics?Bayesian Statistics is an approach to statistical inference that combines prior information with observed data
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Data Science & Machine Learning
20 Sept, 19:43
New evidence: A machine starts producing unusual measurements. The likelihood of seeing those measurements may be much higher when a machine is faulty.Updated belief: After combining the historical information and new evidence, the probability that the machine is faulty increases. If additional sensor data arrives, the estimate can be updated again.This makes Bayesian methods particularly useful for continuous monitoring and decision systems.🔹 21. Advantages of Bayesian Statistics✅ 1. Incorporates Prior Knowledge - Previous research or historical information can be included.✅ 2. Naturally Represents Uncertainty - Posterior distributions provide a full representation of uncertainty.✅ 3. Supports Continuous Updating - New data can update previous beliefs.✅ 4. Useful with Limited Data - A carefully chosen prior can provide useful information when data is limited.✅ 5. Powerf
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Data Science & Machine Learning
20 Sept, 19:43
For example: A 95% credible interval represents a range containing 95% of the posterior probability for the parameter, given the model, prior, and observed data. This is a major conceptual difference.🔹 14. Bayesian Example: CoinSuppose we have a coin and want to estimate its probability of producing Heads. Before collecting data, we might believe the coin is probably close to fair. That's our prior. Then we observe: 8 Heads out of 10 tosses. This is the data. The likelihood tells us how compatible those observations are with different values of the coin's probability. We then combine the prior and likelihood to obtain a posterior distribution.🔹 15. Why Use a Distribution Instead of One Number?In Bayesian statistics, we're often interested in a posterior distribution rather than just a single estimate.Suppose we want to estimate: Probability of customer purchase. Instead of
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Data Science & Machine Learning
20 Sept, 19:43
Suppose a disease affects 1% of a population.So: P(Disease) = 0.01.A medical test is positive for someone who has the disease 99% of the time. But the test can also be positive for healthy people.Suppose: P(Positive | No Disease) = 5%Now someone receives a positive test. The important question is:What is the probability that this person actually has the disease?This is not simply 99%. We need to consider: The prior probability of the disease, The probability of a positive test among people with the disease, The probability of a positive test among people without the disease. Bayes' theorem combines these pieces of information.🔹 8. Solving the ExampleLet's assume:P(Disease) = 0.01
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Data Science & Machine Learning
20 Sept, 19:43
🚀 Data Science Roadmap 2026📘 Phase 2: Mathematics & Statistics for Data Science📖 Topic 16: Bayesian Statistics — Prior, Likelihood & PosteriorBayesian Statistics is an important approach to statistical inference.It provides a framework for updating our beliefs about an unknown quantity when new evidence becomes available.The central idea is:Start with prior information, observe new data, and update your belief to obtain a posterior distribution.Bayesian methods are widely used in: Machine Learning, Classification, Medical diagnosis, Spam detection, Risk analysis, Recommendation systems, A/B testing, Natural Language Processing.🔹 1. What Is Bayesian Statistics?Suppose a company wants to determine whether a customer is likely to purchase a product.
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Data Science & Machine Learning
20 Sept, 16:12
🚀 𝗧𝗼𝗽 𝗜𝗻-𝗗𝗲𝗺𝗮𝗻𝗱 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝘁𝗼 𝗠𝗮𝘀𝘁𝗲𝗿 𝗶𝗻 𝟮𝟬𝟮𝟲Explore these certification courses in today’s most in-demand technology fields:💻 Full Stack :- https://pdlink.in/3SuUeuD📊 Data Analytics :- https://pdlink.in/45vk5ph💫AI Engineering :- https://pdlink.in/4fWJVID🔥 Take the first step towards your high-paying tech career in 2026!
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Data Science & Machine Learning
19 Sept, 20:56
Step-by-Step Approach to Learn AI Agents➊ Understand What AI Agents Are → Autonomous systems that can perceive, reason, and act ↓ ➋ Master the Basics → Python, Data Structures, APIs, and JSON handling ↓ ➌ Explore LLMs as Agents → Understand how GPT, Claude, or Gemini can act as reasoning agents ↓ ➍ Tool Use & Function Calling → Learn how agents use tools, call APIs, and perform tasks dynamically ↓ ➎ Agent Frameworks → LangChain: For chaining LLM calls and memory AutoGen / Autogen Studio: For multi-agent collaboration Haystack: For document question answering ↓ ➏ Memory & Persistence → Vector databases (e.g., FAISS, Chroma, Pinecone) for long-term memory ↓ ➐ Planning & Reasoning → ReAct, CoT (Chain-of-Thought), and Tree of Thought prompting ↓ ➑ Build & Deploy AI Agents →
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Data Science & Machine Learning
19 Sept, 13:48
🎓 𝐅𝐑𝐄𝐄 𝐈𝐁𝐌 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐂𝐨𝐮𝐫𝐬𝐞𝐬 🚀Explore these beginner-friendly courses and strengthen your resume!🎯 Perfect for Students, Freshers and Working Professionals 💻 Learn Online at Your Own Pace 📜 Earn Certificates After Successful Completion🔗 𝗘𝗻𝗿𝗼𝗹𝗹 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘 👇:-https://pdlink.in/45KgqDR🔥 Don’t just collect certificates—build skills that employers value. Share this with your friends!
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Data Science & Machine Learning
18 Sept, 16:35
🔥 SQL Interview Case Studies & Real-World Business Problems🧠 Case Study 1: Top 3 Customers by Revenue 📊 Orders Table order_id customer_id amount 1 101 500 2 102 1000 3 101 700❓ Business Question Find the top 3 customers by total revenue.✅ Solution SELECT customer_id, SUM(amount) AS total_revenue FROM orders GROUP BY customer_id ORDER BY total_revenue DESC LIMIT 3;
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