Vivek Kanna Jayaprakash

🚩 You Can’t Grow in Data if You Don’t Learn This First Someone asked me this after my last post on business framing, and it’s such a good question. 1. Pause and Probe: When someone says “Can you pull customer data for Q2?” 
📍 Don’t open Jupyter Notebook immediately. Pause and ask:
→ What are they trying to decide?
→ What would make this analysis useful?
→ Are they asking the right question? 2. Learn to ask “Why?” and “Then what?” Someone says:
“We need a dashboard for marketing.” 📍 You say:
→ Why do you need it?
→ What decisions will it help you make?
→ What would you do differently after seeing it? You’re not being difficult. You’re helping. 3. Practice turning vague questions into specific ones: Business:
“Why are fewer people renewing their policy?” 📍 You learn to reframe: 
→ Is renewal rate dropping more for first-time customers vs returning ones? 
→ Did the drop begin after a pricing or communication change? This is what framing looks like. 4. Do this with any dataset you find: Go to Kaggle or anywhere. Pick a real-world dataset not dummy data. 
Before doing any analysis, do this: 📍 Ask yourself: 
→ If I was hired by this company, what problem would they want me to solve? → What would I ask them before writing code? 
→ What would be bad advice to give from this data? 📍This is how you practice the thinking. 5. Work backwards from the decision: 📍 Don’t start from the data. Start from the goal. → What’s the decision they want to make? 
→ What are they worried about? → What would “value” look like in this case? 📍That’s how you stop being a task-taker and start becoming a strategic thinker. → Business framing is not a tool. 
→ It won’t make your portfolio look fantastic. 
📍 But it will make you the analyst/scientists that gets invited into every meeting because you ask the right questions. 📍That’s how you move from executing tasks to influencing strategy. ♻️ Repost so others can learn #DataLiving #FavourIbude

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If you're into time series forecasting, data science, or business analytics, this free, hands-on textbook will be a game changer for you — especially if you're a Python user like me. 🔗 Forecasting: Principles and Practice (Python edition) - https://otexts.com/fpppy/ 🔗 GitHub: Nixtla's fpp3-python - https://lnkd.in/g7fQ4xVG Over the last few weeks, I’ve been diving deep into this book — and I have to say, it’s one of the most practical, beginner-friendly, yet advanced resources out there for learning forecasting with Python. 𝐖𝐡𝐞𝐭𝐡𝐞𝐫 𝐲𝐨𝐮'𝐫𝐞: 📊 A business analyst forecasting sales or inventory 🎓 A student learning time series for the first time 👨‍💻 A data scientist or ML engineer building forecasting models 💼 Or even an MLOps practitioner showcasing model deployment... This book strikes the perfect balance between intuition, real-world examples, and code implementation. 𝐇𝐞𝐫𝐞’𝐬 𝐰𝐡𝐚𝐭 𝐦𝐚𝐤𝐞𝐬 𝐢𝐭 𝐬𝐭𝐚𝐧𝐝 𝐨𝐮𝐭: ✅ Free and online, continuously updated ✅ Based on the gold-standard "Forecasting: Principles and Practice (3rd ed)" ✅ Uses Nixtla's Python libraries (nixtlaverse) – a powerful alternative to traditional statsmodels ✅ Covers everything from classical time series to deep learning-based models ✅ Dozens of industry examples from real consulting projects ✅ Emphasis on graphical analysis, not just metrics And the best part? No heavy math prerequisites. Just your Python skills, a bit of stats, and the will to learn! 🌟 For me, this book bridges the gap between academic theory and real-world forecasting. Big shoutout to the teams at Nixtla and the original authors 𝐑𝐨𝐛 𝐇𝐲𝐧𝐝𝐦𝐚𝐧 and 𝐆𝐞𝐨𝐫𝐠𝐞 𝐀𝐭𝐡𝐚𝐧𝐚𝐬𝐨𝐩𝐨𝐮𝐥𝐨𝐬 for making this so accessible to the Python community! 🙌 Have you tried this book yet? Would love to hear your thoughts or use cases. Let’s connect if you’re building something exciting with time series or LLMs in production! #DataScience #Forecasting #TimeSeries #Python #Nixtla #MLOps #OpenSource #MachineLearning #Analytics #FPP #FPP3 #FPPpy #Streamlit #Nixtlaverse #LLM #MLtools

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🚀 The Only Generative AI Cheat Sheet You’ll Ever Need Generative AI is transforming industries — from text and images to code, video, and audio. Whether you’re a Data Scientist, AI Engineer, or just starting out, mastering Gen AI is now a must-have skill. When I began exploring Gen AI, I was overwhelmed by the number of models, tools, and evaluation metrics. That’s why I created this Gen AI Cheat Sheet — a simplified guide to help you navigate this space with clarity. Here’s what you’ll find inside: ✔️ Types of Generative AI: Text, Image, Video, Code, Audio ✔️ Leading Models & Architectures: Transformers, Diffusion Models, GANs, Multimodal LLMs ✔️ Data Pre processing & Prompt Engineering best practices ✔️ Evaluation Metrics for text, images, and multimodal outputs ✔️ Fine-tuning strategies, hyperparameter tuning, and feature engineering ✔️ Top Generative AI Tools across chat, images, video, code, and audio 💡 Pro Tip: Don’t just consume — experiment with LLMs, diffusion models, and multimodal frameworks. Hands-on practice is where the real learning happens. 🚨 Remember: “Generative AI is not just the future — it’s the present skill shaping careers in AI and data.” Found this helpful? ♻️ Repost to help others in your network

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Learning NLP? 𝐃𝐨𝐧’𝐭 𝐜𝐨𝐧𝐟𝐮𝐬𝐞 𝐭𝐡𝐞𝐬𝐞 𝐜𝐨𝐦𝐦𝐨𝐧 𝐭𝐞𝐫𝐦𝐬. Many NLP beginners (and even pros) mix up concepts like stemming vs. lemmatization, or tokenization vs. chunking — and that leads to poor preprocessing and model performance. I built this quick visual to help you clearly understand 10+ commonly confused NLP techniques — with definitions, examples, and key differences. ✅ Great for anyone working on: • Text classification • Chatbots & LLMs • Preprocessing for RAG & fine-tuning 👉 Swipe to check the full comparison sheet. 💬 Have a favorite NLP trick or confusion to add? Drop it in the comments! 👍Like | 🔖 Save | 🔁 Share with NLP learners ➕ Follow Naresh Edagotti for more content that makes complex AI topics feel simple.

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Interview Readiness Playlist for Aspiring Data Scientists 📌ML Design: Combating Counterfeit Fraud youtu.be/YQZBgvLB_EQ 📌ML Design: Detecting Buyer Returns Fraud youtu.be/as4i1tUo0EA 📌Derive & Implement Logistic Regression from Scratch youtu.be/cT_U40djaww  📌PCA Explained Simply with Codes youtu.be/yxz3UpPMycg 📌 Ongoing Interviewing Trends: youtu.be/XrZXojl5Zd8 📌 Essential Mathematics for ML: youtu.be/oMY2uKjx_Zc 📌Categorical Variable Encoding Strategies youtu.be/MKuAQv6ybc8  🎯Complete guide for Linear Regression lnkd.in/gdRsMHbn 🎯Complete playlist for Logistic Regression lnkd.in/gtPfmQUv 📌 Classification Metrics: youtu.be/E2HRSJKU-_4 🎯Data Drift Detection & Model Monitoring youtu.be/tQjRQWfYQ10 📌 Conversational AI: Tech behind Chat-GPT: youtu.be/JKoJ5YIr2O4 📌 Pushing LLM Boundaries Sparse & Flash Attention, Quantisation, Pruning, Distillation, LORA : youtu.be/mF7OM_XU2S4 📌 PySpark Essentials youtu.be/aruptWppgSs #datatrek #datascience #machinelearning #statistics #deeplearning #ai

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🚨 How Many #Features Should You Keep? A Guide for #Data_Science Freshers 🚨 In interviews, a common question is: “If you have 100+ features, how do you decide whether to keep 10, 15, or 20?” Most answers sound like: “I’ll just take 5 or 10 with #PCA.” But that’s not enough. It’s not about guessing a number, it’s about thinking like a #DataScientist. Here’s how you should approach it: Step 1: Understand the Purpose Why are you reducing dimensions? Is it for #Speed, #Visualization, or #Interpretability? Step 2: Know the Techniques #PCA – Fast, variance-based, but misses non-linear patterns #tSNE – Good for 2D/3D visualization, but computationally heavy #UMAP – Faster than t-SNE, balances global + local structures #Autoencoders – Handle complex structures, need data & compute #FeatureSelection – Keeps actual features, good for business insights Step 3: Experiment & Validate Don’t just say “10 features.” Instead: With #PCA – check variance explained by 10, 15, or 20 components With #UMAP / #tSNE – see if clusters are meaningful With #FeatureSelection – verify if model accuracy improves with fewer features Example: If you have 120 features in customer purchase data: #PCA may suggest 25 features explain 95% variance #UMAP may reveal customer segments #FeatureSelection may reduce it to 15 top business-driven features The key point: It’s not about the number of features. It’s about the reasoning and evidence behind your choice. To all freshers: never give random guesses. Show that you can reason, compare methods, and explain trade-offs. “Don’t aim to sound safe. Aim to sound thoughtful.” #DataScience #Freshers #MachineLearning #CareerAdvice #DimensionalityReduction #tSNE #UMAP #Autoencoders

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Came across this course - Machine Learning and Deep Learning Projects in Python. It's currently free with a coupon (link below). It covers: ✅ Real-world ML & DL projects ✅ Python implementation ✅ NLP, image processing, forecasting & more ✅ 5.5 hours of content + 40+ cheat sheets ✅ Lifetime access + certificate If you're looking to level up your data science or Python skills, this could be a great resource. Sharing in case it helps someone, unsure how long it’ll stay free! https://lnkd.in/gATrChGk

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AI Agents for Data Engineering? (Quick Guide📝) From the last few days, I have been exploring AI Agents. They are powerful and can do lot of automation for repetitive task. Everyone’s talking about AI agents in coding, customer support, marketing… But here’s what’s about to happen in data engineering 👇🏻 ✅ Automated Data Quality Checks Imagine an AI agent that constantly monitors your pipelines, detects anomalies, and opens a Jira ticket. Before your analyst even notices the numbers are off. ✅ Self-Healing Pipelines our Airflow job fails at 2AM. The AI agent: - Reads the logs - Finds the root cause - Applies the known fix - Reruns the job No human on-call needed. ✅ Schema Change Adaptation Instead of breaking when a new column arrives… The AI agent updates the schema mapping, validates it, and informs downstream teams. All automatically. ✅ Query Optimization in Real-Time You write a slow SQL query. The AI agent rewrites it with better joins, partition filters, and indexes. You get results 10x faster without touching the execution plan. ✅ Smart Documentation An AI agent reads your dbt models, DAGs, and logs, and auto-generates documentation that’s always up to date. 🧠 My take: We’re not replacing data engineers. We’re removing the repetitive pain so engineers can focus on design, architecture, and business impact. Question: Would you trust an AI agent to fix your production pipeline without your approval? Why or why not? No matter what happens Data Engineers are going to be backbone of AI Model and this is the best time to get into Data Engineering (check below) ⬇️ #dataengineer #dataengineering

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