Subhan Liaqat

If you’re aiming to move into a Machine Learning Engineer position—whether from software engineering or another background—these free resources will build the foundation and skills you need: 💻 1. Math & Fundamental Concepts - Linear Algebra: 3Blue1Brown’s “Essence of Linear Algebra” (YouTube) - Multivariable Calculus: Khan Academy’s Multivariable Calculus (Units on Intro & Derivatives) - Backpropagation & Calculus: Animated “Backpropagation” explainer (YouTube) - Information Theory: Information Theory: A Tutorial Introduction (free PDF) - Statistics & Probability: StatQuest YouTube channel for clear, concise tutorials 💻 2. Core Machine Learning - Stanford CS229 (Andrew Ng): Lectures 1, 2, 3, 4, 8, 9, 11, 12, 13 + free course notes (YouTube) - Caltech Machine Learning: Intuition-focused lectures to reinforce ML concepts (YouTube) 💻 3. Deep Learning Foundations - Karpathy’s “Zero to Hero”: Hands-on series—code along in Colab or Jupyter (YouTube) - Stanford CS231n: Lecture videos + free Colab assignments for CNNs, backprop, and more 💻 4. Transformers & LLMs - Intro to Transformers: University of Waterloo & University of Michigan lectures (YouTube) - ChatGPT Explainer: Wolfram’s breakdown of ChatGPT (YouTube) - Interactive Transformer Demos: Jay Alammar’s “The Illustrated Transformer” (web) - Residual Learning: Kaiming He’s 2023 lecture on ResNets (YouTube) 💻 5. Efficient ML - CUDA Basics: Free lecture series on CUDA programming (YouTube) - TinyML & Efficient DL: MIT’s 2023 “Efficient ML” lectures covering on-device and GPU-optimized techniques (YouTube) 💻 6. Next Steps - Build Small Projects: Use these lectures to implement mini-projects—train a CNN in Colab, deploy a model with Docker, or run inference on CPU/GPU. - Showcase on GitHub & Colab: Share notebooks, README write-ups, and hosted demos (Streamlit/Flask). - Network & Apply: Join MLOps/Data Science Slack or Discord groups; highlight these projects on LinkedIn when applying. With consistent effort—refreshing math, mastering ML fundamentals, and hands-on coding—you can bridge into an MLE role using only free online resources. More details: https://lnkd.in/g_ZzW2Fg

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Aspiring Data Scientists, here’s the fastest way to catch a hiring manager’s attention. Stop filling your portfolio with “just for practice” projects. Start building projects that solve real problems with messy, incomplete, and meaningful data. Because anyone can follow a tutorial. What sets you apart is showing that you can identify a problem, gather the data, and deliver value. Here are 5 project ideas that stand out: 1️⃣ Customer churn prediction for a subscription service Predict the likelihood of subscription customers canceling their service based on historical engagement and account data. Dataset: https://lnkd.in/dSmiReGz 2️⃣ Spotify Performance Overview Dashboard Build an interactive Tableau dashboard to visualize Spotify's key business metrics. Dataset: https://lnkd.in/dhYMCcQJ 3️⃣ Sales Forecasting for Walmart Build a model that can accurately predict store sales for Walmart stores. Dataset: https://lnkd.in/dfS8KQRm 4️⃣ Text-to-SQL AI Assistant Enable non-technical users to query databases using natural language by translating text into SQL queries. Dataset: https://lnkd.in/dVrhzF4X 5️⃣ Sales Uplift Analysis for Online Retail Store Determine how much a specific marketing intervention increased company sales. Dataset: https://lnkd.in/dQknhK3m Each one helps prove you can think like a data scientist, not just code like one. Which project are you working on next?

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AI JOBS ARE BOOMING!! • AI Engineer – $134,188 • Machine Learning Engineer – $123,117 • Deep Learning Engineer – $112,389 • NLP Engineer – $109,845 • Computer Vision Engineer – $115,400 5 Steps To Get Your First AI Job Without A Degree: 1. Take Online AI & Machine Learning Courses • Learn from platforms like Coursera, Udacity, and Stanford AI. • Google, IBM, and AWS AI certifications can boost credibility. 2. Learn Python & Essential AI Libraries • Python is essential—master TensorFlow, PyTorch, and OpenCV. • Work on coding challenges on LeetCode & HackerRank. 3. Build AI Projects & a Strong Portfolio • Build chatbots, recommendation systems, or self-learning models. • Share work on GitHub, Kaggle, or a personal blog. 4. Gain Experience Through Internships & Freelance Work • Apply for internships or contribute to AI open-source projects. • Freelance AI gigs on Fiverr & Upwork can build expertise. 5. Network & Keep Learning • Join AI groups on Discord, Reddit, and LinkedIn. • Attend AI summits & hackathons for career growth. 💡 Pro Tip: Specializing in a niche (NLP, Computer Vision, or Data Science) will make you stand out! What do you think on the topic? Lemme know in the comments ⬇️ ☆---------------------------------------------------------------------------☆ If you find this useful, please... 👍 like and comment ♻ share 🔔 follow me for more #GenAINews #TechUpdates #GenAI #ArtificialIntelligence #AI #chatgpt #openai #Perplexity #Claude #mistralAI #anthropic #aws #bedrock #google #gemini #nvidia #Microsoft #agent #agi #deepseek #meta #llama #xai #grok #prompt #PromptEngineering #deepseek #manus #manusai

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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 📌 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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A/B Testing is one of the most in-demand skills in data and product roles - but it's rarely taught well in grad school. Master these 7 A/B testing concepts and stand out in data science and product analytics interviews. 1️⃣ Correlation vs Causation Why A/B testing matters and why relying on historical data can mislead you. 📺 https://lnkd.in/gNBngegb 2️⃣ Calculating Sample Size Top interview question: how big should your test be? 📺 https://lnkd.in/g2xWMVWT 3️⃣ A/B Testing Lifecycle Learn the 4 key phases: preparation, implementation, running, and evaluation. 📺 https://lnkd.in/g3V7afQe 4️⃣ Success Metrics & Evaluation How to define and measure success in your test. 📺 https://lnkd.in/gvBcqApq 5️⃣ Hands-On Demo Watch a real A/B test in action and follow along. 📺 https://lnkd.in/gQiR2UNv 6️⃣ Avoid Common Pitfalls Avoid these: Peeking, unbalanced groups, novelty effects, seasonality, and more. 📺 https://lnkd.in/g4P_8qy4 7️⃣ Follow Ron Kohavi who is one of the most influential figures in A/B testing and watch this amazing podcast https://lnkd.in/gK5vVAPj Bookmark this channel on A/B testing: https://lnkd.in/gCM47CkH Comment if you found this helpful. Reach out via DM if you need help with A/B testing. Repost if you think someone in your network should read this.

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📌 The Fastest Way to Land a Data Role Without Applying for One A friend saw my post yesterday and asked me: “What do you mean when you wrote, ‘Don’t wait for an opportunity, create one’?” And I knew I had to write about this. 📍You might think breaking into data roles means quitting their job, starting from scratch, or applying endlessly to different companies. But what if I told you that the fastest way in… might be staying exactly where you are? This is one of the strategies I recommend, Instead of spending months applying and hearing nothing back, why not turn your current role into the opportunity you’re looking for? 📍 The Data Role Hiding in Your Current Job Before you stress over job applications, ask yourself: → Are you tracking performance in marketing, sales, finance, or operations? → Are you building reports, analyzing trends, or presenting insights? → Are you the go-to person for Excel, dashboards, or data cleanup? If yes, you’re already doing data work, you just don’t have the title yet. 1: Turn Your Work Into a Data Project One mistake? waiting for someone to “give” you a data project. 📍Instead, take what you’re already doing and turn it into a portfolio-worthy initiative: → If you’re in finance, automate those manual Excel reports with Python & SQL. → If you’re in marketing, use data to predict which campaigns will perform best. → If you’re in operations, optimize workflows with data-driven insights. Your goal isn’t just to work with data, it’s to solve real business problems with it. 2: Become The Data Person on Your Team 📍You don’t need a title to be recognized as a data professional. → Instead of just reporting numbers, explain what they mean. → Instead of manually pulling data, automate it with SQL or Python. → Instead of making decisions on gut feeling, bring in the numbers. 📍Soon, your team will naturally start coming to you for data-related work. And once that happens, it’s only a matter of time before the business realizes they need you in an official data role. 3: Pitch Your Own Data Role 📍Most data roles don’t exist until someone proves they should. Once you’ve shown the impact of your data work, approach leadership with this: → “I’ve been analyzing [business problem] and found that by optimizing [data insight], we could increase [revenue/savings/efficiency] by X%. I’d love to formalize this into a data-driven role to scale these insights across the company.” 📌Who Is This Strategy For? This isn’t about saying don’t apply for jobs. It’s about giving yourself more options if your job search isn’t moving fast enough. → If you like your current company but want to move into a data role. → If you’re transitioning into data, this lets you practice and sharpen your skills in a real-world setting while still earning a paycheck. → If you’re waiting for callbacks, why not make progress in your current company while you wait? ♻️Repost so others can learn

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Don’t miss these essential skills for becoming a Senior Data Scientist. Over the years, I’ve applied to numerous companies and gone through a wide range of interview processes. Here’s what I realized: while interview styles differ across companies, certain patterns remain consistent. So instead of preparing randomly, focus your efforts on the right set of skills. Use this roadmap to guide your next big career move: 1. Foundation Stage • Math & Stats: Probability, Bayes’ theorem, descriptive stats, linear algebra, calculus basics • Python: Core syntax, NumPy, Pandas, Matplotlib/Seaborn, Scikit-learn, Jupyter notebooks • SQL: Joins, CTEs, window functions, complex queries • Data Wrangling: Missing values, feature engineering, outliers, transformations, EDA 2. Intermediate Level • Machine Learning: Regression, decision trees, SVMs, kNN, clustering, PCA, evaluation metrics • Databases: Advanced SQL, NoSQL (MongoDB/Cassandra), query optimization • Data Engineering: ETL pipelines, Airflow, APIs, data quality • Version Control: Git workflow, GitHub collaboration 3. Advanced Skills • Deep Learning: Neural networks, CNNs, RNNs, TensorFlow/PyTorch, transfer learning • NLP: Text preprocessing, embeddings, transformers, BERT • Big Data: Spark, Hadoop, distributed computing • Cloud & MLOps: AWS/GCP/Azure, Docker, MLflow, CI/CD • Tools: Streamlit/Flask, experiment tracking 4. Career Development • Portfolio: 3–5 end-to-end projects, Kaggle, GitHub • Business Skills: Problem framing, stakeholder communication, domain knowledge • Interview Prep: Core concepts, SQL/DSA challenges, system design, behavioral rounds Focus on one skill at a time. Mix theory with real projects. Revisit the basics often and engage with the data science community. Track your progress with milestones that matter. Balancing work, life, and learning is hard — especially with courses piling up. Choose what truly adds value. If you’re looking for structured, impactful learning — check out Bosscoder Academy https://bit.ly/4crzg5b They’ve helped 800+ professionals make successful career switches. As always, please do your own research before you enroll for any program #DataScience #AI #MachineLearning #GenerativeAI #CareerGrowth #Sponsored

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Being an ML Engineer I appeared in multiple interviews for Data / Applied Scientist roles. These are the most common interview topics from DEEP LEARNING. - Fundamentals 1/ Perceptrons and Multilayer Perceptrons (MLP) 2/ Activation Functions (ReLU, Sigmoid, Tanh, etc.) 3/ Loss Functions (Cross-Entropy, MSE, Hinge, etc.) 4/ Backpropagation & Gradient Descent 5/ Vanishing/Exploding Gradients - Neural Network Architecture 6/ Convolutional Neural Networks (CNNs) 7/ Recurrent Neural Networks (RNNs), LSTM, GRU 8/ Transformers and Attention Mechanism 9/ Residual Networks (ResNet), Skip Connections 10/ Batch Normalization, Layer Normalization, Dropout - Training Deep Networks 11/ Weight Initialization Techniques 12/ Optimizers (SGD, Adam, RMSprop, etc.) 13/ Overfitting & Regularization (Dropout, L2, Early Stopping) 14/ Hyperparameter Tuning (learning rate, batch size, etc.) 15/ Learning Rate Scheduling - Advanced Topics 16/ Transfer Learning & Fine-Tuning 17/ Self-Supervised Learning 18/ Contrastive Learning (SimCLR, MoCo) 19/ Autoencoders & Variational Autoencoders (VAE) 20/ Generative Adversarial Networks (GANs) - Deep Learning Applications 21/ Image Classification / Object Detection / Segmentation 22/ Sequence Models (Text, Time Series) 23/ Embedding Techniques (word2vec, BERT embeddings, etc.) Prepare these topics with theoretical intuition and mathematical depth along with basic practical implementation. This will also help to solve ML case studies asked during interview. If you are preparing for interview checkout my articles on Machine Learning. Link in comment. #ai #ml #deeplearning #interview

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🔍 𝗪𝗵𝘆 𝗔𝗿𝗲 𝗦𝗼 𝗠𝗮𝗻𝘆 𝗖𝗦 𝗚𝗿𝗮𝗱𝘂𝗮𝘁𝗲𝘀 𝗦𝘁𝗶𝗹𝗹 𝗨𝗻𝗲𝗺𝗽𝗹𝗼𝘆𝗲𝗱? Despite the booming tech industry, thousands of Computer Science graduates remain jobless. Why? The answer lies in a painful truth: 𝘁𝗵𝗲𝗿𝗲’𝘀 𝗮 𝗴𝗿𝗼𝘄𝗶𝗻𝗴 𝗴𝗮𝗽 𝗯𝗲𝘁𝘄𝗲𝗲𝗻 𝗮𝗰𝗮𝗱𝗲𝗺𝗶𝗰 𝗰𝘂𝗿𝗿𝗶𝗰𝘂𝗹𝘂𝗺 𝗮𝗻𝗱 𝗿𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝗶𝗻𝗱𝘂𝘀𝘁𝗿𝘆 𝗱𝗲𝗺𝗮𝗻𝗱𝘀. As someone who has reviewed countless resumes and mentored aspiring data professionals, here's what I often observe: 🎓 𝗪𝗵𝗮𝘁 𝗖𝗼𝗹𝗹𝗲𝗴𝗲𝘀 𝗧𝗲𝗮𝗰𝗵: • Heavy focus on theory: OS, Compiler Design, DBMS, CN, etc. • Limited hands-on exposure to real-world tools or large datasets • Little emphasis on collaborative, project-based learning • Outdated or irrelevant programming languages or tech stacks 💼 𝗪𝗵𝗮𝘁 𝘁𝗵𝗲 𝗜𝗻𝗱𝘂𝘀𝘁𝗿𝘆 𝗡𝗲𝗲𝗱𝘀: • Proficiency in Python, SQL, and relevant libraries (Pandas, NumPy, etc.) • Problem-solving with real datasets using tools like Jupyter, Tableau, Power BI • Understanding of cloud platforms (AWS, GCP, Azure) • Experience with Git, APIs, Agile methodologies, and CI/CD pipelines • Communication, collaboration, and stakeholder engagement 💡 𝗧𝗵𝗲 𝗙𝗶𝘅: 𝟭 𝗖𝗼𝗹𝗹𝗲𝗴𝗲𝘀 must collaborate with industry to redesign curriculum. 𝟮 𝗦𝘁𝘂𝗱𝗲𝗻𝘁𝘀 should take ownership — build projects, join open-source, attend hackathons. 𝟯 𝗣𝗿𝗼𝗳𝗲𝘀𝘀𝗶𝗼𝗻𝗮𝗹𝘀 must reskill continuously — tech evolves, and so should we. 🎯 𝗧𝗵𝗲 𝗷𝗼𝗯 𝗺𝗮𝗿𝗸𝗲𝘁 𝗶𝘀 𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 𝗮𝗯𝗼𝘂𝘁 𝗱𝗲𝗴𝗿𝗲𝗲𝘀. 𝗜𝘁’𝘀 𝗮𝗯𝗼𝘂𝘁 𝘀𝗸𝗶𝗹𝗹𝘀. Let’s hear from you: 📊 𝗣𝗼𝗹𝗹: 𝗪𝗵𝗮𝘁 𝘀𝗸𝗶𝗹𝗹 𝗵𝗲𝗹𝗽𝗲𝗱 𝘆𝗼𝘂 𝗹𝗮𝗻𝗱 𝘆𝗼𝘂𝗿 𝗳𝗶𝗿𝘀𝘁 𝗷𝗼𝗯? • Python or Programming • SQL / Data Analysis • Internships / Real Projects • Communication / Soft Skills 🗣 Drop a comment and share your story — it might inspire someone still figuring it out. 🔁 𝗕𝗼𝗻𝘂𝘀 𝗧𝗶𝗽:-Whether you're a fresher or already working professional — upgrading your skills is non-negotiable in tech. Focus on Python, Data Science, AI, Machine Learning, DSA, and more. I’d personally recommend exploring resources from 𝗧𝗲𝗰𝗵𝗩𝗶𝗱𝘃𝗮𝗻 — practical, beginner-friendly, and industry-relevant. 🚀 🔗 𝗘𝘅𝗽𝗹𝗼𝗿𝗲 𝗵𝗲𝗿𝗲:-https://lnkd.in/gWaTM7KG If you found this helpful, drop a like or share it with someone who’s building their career! 💬 Let’s keep learning, growing, and building smarter solutions.

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𝗛𝗼𝘄 𝘁𝗼 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲 𝗥𝗲𝗮𝗹-𝗪𝗼𝗿𝗹𝗱 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 (𝗪𝗶𝘁𝗵𝗼𝘂𝘁 𝗮 𝗝𝗼𝗯)? Let’s be honest - breaking into analytics feels tough when every job wants "𝗿𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲." But here’s something I learned early on: You don’t need a full-time job to practice real analytics. You just need a little creativity (and maybe a messy dataset). Here’s what can help you: 𝗕𝘂𝗶𝗹𝗱 𝗮 𝗽𝗼𝗿𝘁𝗳𝗼𝗹𝗶𝗼 𝗽𝗿𝗼𝗷𝗲𝗰𝘁 𝘄𝗶𝘁𝗵 𝗽𝘂𝗿𝗽𝗼𝘀𝗲: Don’t just analyze Titanic data again pick a domain you actually care about. E-commerce, sports, finance, music, supply chain anything. Make it real. Make it messy. Make it yours. 𝗨𝘀𝗲 𝗽𝘂𝗯𝗹𝗶𝗰 𝗱𝗮𝘁𝗮𝘀𝗲𝘁𝘀 𝗹𝗶𝗸𝗲 𝘁𝗵𝗲𝘆’𝗿𝗲 𝗰𝗹𝗶𝗲𝗻𝘁 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀: Go to Kaggle.com, Data. gov, or Google Dataset Search. Then ask: “If I were hired to analyze this… what would the stakeholder care about?” Build a report. Tell a story. Practice explaining insights in simple terms. 𝗖𝗿𝗲𝗮𝘁𝗲 𝗺𝗼𝗰𝗸 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗽𝗿𝗼𝗯𝗹𝗲𝗺𝘀 Example: “You’re a data analyst for an online store. Sales dropped 20% last month. Where do you start?” Now… dive into the data and try to find out. Even if you made it up, the thinking process is real. You can also document everything create a portfolio on GitHub or Notion. Add a summary. Explain your process. Share your learnings on LinkedIn. This is the kind of “experience” hiring managers love to see. You don’t need permission to get better. Start where you are. Build what you can. That’s how you level up even before your first job.

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As a data analyst, you use data to make informed decisions but how informed are you? A common frustration I hear from data guys: “I trained a solid machine learning model… but I don’t know how to deploy it!” Here’s the missed opportunity: If your model stays on your Jupyter Notebook, visual studio code, Pycharm or Colab, it’s just a project—not a product. And that's where many machine learning journeys stall. But what if I told you deployment isn’t just for engineers? Well, check this out: Streamlit and Flask—the power duo for every data professional who is ready to scale their impact. Streamlit: ✅️ Turn your ML models into interactive web apps in minutes. ✅️ Zero front-end experience needed. ✅️ Perfect for demos, internal tools, and quick POCs (proof of concept) Flask: ✅️ Lightweight, flexible Python web framework ✅️ Ideal for deploying models via APIs ✅️ Great for integrating into larger applications So where do YOU come in? If you're a scaled data analyst—someone who has moved from basic reporting to advanced analytics—this is your next frontier. Let’s clarify: 💥Data Analyst💥 ✅️ Focuses on descriptive analytics ✅️ Answers “what happened” using tools like Excel, SQL, Power BI ✅️ Supports business decisions with reports and dashboards 💥Data Analytics Professional💥 ✅️ Goes beyond analysis into modeling, automation, and deployment ✅️ Builds predictive models and integrates them into business pipelines ✅️ Knows how to turn code into value Why does this matter? ✅️ Because businesses don’t just need analysts. ✅️ They need professionals who can build, deploy, and scale data solutions. 💥This is your invitation to: ✅️ Learn Streamlit for app interfaces ✅️ Learn Flask for backend deployment ✅️ Take your models from notebook to the boardroom ✅️ Don’t stop at building models. Ship them. Show them. Scale them. If you’re already good at data analytics, deployment is your value multiplier.💪💪💪 In summary, it's high time you include machine learning engineering in your must-have skill as a data analyst to give yourself a competitive edge. Let’s grow together.💪💪💪 What deployment tools are you exploring this year? hashtag #DataAnalytics #Streamlit #Flask #MachineLearning #ModelDeployment #AI #DataProfessionals #Upskilling #DatLMSPro

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Shrinkage methods like Ridge Regression, Lasso, and Elastic Net are essential techniques in modern statistics and machine learning. These methods help reduce overfitting in models by shrinking the coefficient values, making them more robust and generalizable to unseen data. ✔️ Improved model performance: Shrinkage methods reduce the risk of overfitting by penalizing large coefficients, leading to more reliable predictions. ✔️ Feature selection: Lasso, in particular, can reduce some coefficients to exactly zero, prioritizing features that most improve predictive performance. ✔️ Balance between Ridge Regression and Lasso: Elastic Net offers a balance, combining Lasso’s feature selection and Ridge Regression’s stability for correlated variables. ❌ Loss of interpretability: If shrinkage is too aggressive, it may drive important coefficients closer to zero, making it hard to interpret the true importance of predictors. ❌ Tuning challenges: Selecting the correct penalization parameter (lambda) is crucial. Too much shrinkage can lead to underfitting, while too little shrinkage can still cause overfitting. ❌ Not all methods perform well in every situation: Ridge Regression works better when all predictors are important, while Lasso is more suited when only a few predictors matter. Elastic Net tries to balance both but may need careful tuning to work effectively. The plot attached visualizes the differences between OLS (no shrinkage), Ridge Regression, Lasso, and Elastic Net. OLS shows the raw coefficients, while shrinkage methods reduce the magnitude of the coefficients to varying degrees. Lasso sets some coefficients to exactly zero, Ridge Regression keeps all coefficients non-zero but shrinks them, and Elastic Net combines aspects of both methods. 🔹 In R: Use glmnet for Ridge Regression, Lasso, and Elastic Net, providing control over the alpha parameter to adjust between Lasso and Ridge Regression. 🔹 In Python: Use sklearn.linear_model with Ridge, Lasso, and ElasticNet classes for efficient model fitting and coefficient shrinking. You can check out my online course on Statistical Methods in R, which explains this topic as well as other related topics in further detail. Take a look here for more details: https://lnkd.in/d-UAgcYf #datavisualization #dataviz #analytics

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As a grad student specializing in data science and data engineering, I’ve spent plenty of time stitching together tools – ETL frameworks here, orchestration platforms there, plus a few custom Python scripts to hold it all together. 😅 But after getting a sneak peek at the new Agentic Data Engineering launch from Ascend.io, it’s clear that things are changing. Ascend isn’t just adding AI on top–it’s woven into every layer. From pipeline creation to debugging, the platform actively helps you build, learn, and iterate faster. 🤖 Enter Otto, their “superagent,” coordinating a team of specialized agents for: Python 👨‍💻 SQL 🧮 Config generation 🛠️ Error explainers 🧠 Otto can even help you build automations and full-on data apps to orchestrate workflows and modularize repeated tasks. I wish more tools taught that kind of reusability from day one. For anyone early in their journey, this kind of AI-assisted development feels like a cheat code. I’m genuinely excited about how platforms like Ascend.io are making it easier–and more fun–to learn data engineering. 💬 What tools or platforms have helped you level up the fastest in your data journey? #DataEngineering #DataScience #AgenticDataEngineering #Ascend #LearningInPublic #AI #Automation #ModernDataStack

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Every data scientist I know has the same ritual: - Save notebook every 2 minutes - Restart the kernel when it freezes - Pray nothing breaks during presentations Sound familiar? Here's the truth nobody talks about: data science and software development are 𝐍𝐎𝐓 the same. Yet we keep forcing ourselves into tools built for engineers, not data scientists. I've been testing Zerve AI, and honestly? It feels like cheating (the good kind). 𝐖𝐡𝐚𝐭 𝐦𝐚𝐤��𝐬 𝐢𝐭 𝐝𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐭: → 𝐂𝐨𝐧𝐭𝐞𝐱𝐭-𝐀𝐰𝐚𝐫𝐞 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬 – They understand your data, workflows, and compute environment. No more explaining the same thing 50 times. → 𝐕𝐢𝐬𝐮𝐚𝐥 𝐂𝐚𝐧𝐯𝐚𝐬 + 𝐍𝐨𝐭𝐞𝐛𝐨𝐨𝐤𝐬 – Drag, drop, connect blocks. Python, R, and SQL all in one place. When something breaks, you fix that block, not restart your entire Jupyter kernel. → 𝐁𝐮𝐢𝐥𝐭-𝐈𝐧 𝐎𝐫𝐜𝐡𝐞𝐬𝐭𝐫𝐚𝐭𝐢𝐨𝐧 – Parallel processing without the DevOps headache. Scale up, scale down, pay for what you use. The real game-changer? While you're debugging kernel issues in Jupyter, 𝐙𝐞𝐫𝐯𝐞 users are already deploying models. 𝐇𝐞𝐫𝐞'𝐬 𝐰𝐡𝐚𝐭 𝐡𝐢𝐭 𝐦𝐞 𝐡𝐚𝐫𝐝𝐞𝐬𝐭: • Natural language to full workflows in minutes • Git integration that actually makes sense for data work • Real-time collaboration without the "who has the latest version?" Stop fighting with Jupyter environments that weren't built for modern data science. Your future self will thank you when you're shipping models instead of restarting kernels. ♻️ Share this with your data science team 𝐏.𝐒. I share job search tips and insights on data analytics & data science in my free newsletter. Join 18,000+ readers here → https://lnkd.in/dUfe4Ac6

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I collected and analyzed 101 Data Science job openings. The most required skills in 2025 are👇🏽 𝟭/ 𝗣𝘆𝘁𝗵𝗼𝗻 𝗶𝘀 𝘁𝗵𝗲 𝗻𝗼.𝟭 𝗺𝗼𝘀𝘁 𝗿𝗲𝗾𝘂𝗶𝗿𝗲𝗱 𝘀𝗸𝗶𝗹𝗹 𝗶𝗻 𝟮𝟬𝟮𝟱 86% of Data Science job postings mentioned Python. So if you’re starting your DS journey, start with Python. I like DataCamp. I used them to learn Python before landing my first Data Science job offers: https://lnkd.in/gukX_dPX 𝟮/ 𝗦𝗤𝗟 𝗶𝘀 𝗮𝗹𝘀𝗼 𝗿𝗲𝗾𝘂𝗶𝗿𝗲𝗱 𝗳𝗼𝗿 𝗺𝗼𝘀𝘁 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗷𝗼𝗯𝘀 ↗ SQL is in high-demand with 62% of jobs requiring it I built Interview Master to help you ace your SQL interviews & land your dream Data Science jobs. Try out over 200+ practice SQL questions at www.InterviewMaster.ai 𝟯/ 𝟲𝟱% 𝗼𝗳 𝗹𝗶𝘀𝘁𝗲𝗱 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗮𝘀 𝗮 𝗿𝗲𝗾𝘂𝗶𝗿𝗲𝗺𝗲𝗻𝘁… .. but they didn’t mention specific ML domains What this means? ↳ Focus on building a strong foundation in Statistics & Machine Learning before moving to complex models Stanford's Intro to Stats class: https://lnkd.in/greMc4xb 𝟰/ 𝗦𝗼𝗳𝘁 𝘀𝗸𝗶𝗹𝗹𝘀 𝘄𝗲𝗿𝗲 𝗿𝗲𝗾𝘂𝗶𝗿𝗲𝗱 (𝗺𝗼𝗿𝗲 𝗼𝗳𝘁𝗲𝗻 𝘁𝗵𝗮𝗻 𝗻𝗼𝘁) The two most common soft skills mentioned were: ✔️ Business Sense ✔️ Communication I read this Product Management book twice when I finally got my first Data Science job offer: https://amzn.to/3WW2tP8 𝟱/ 𝟰𝟬% 𝗼𝗳 𝗷𝗼𝗯𝘀 𝗽𝗼𝘀𝘁𝗶𝗻𝗴𝘀 𝗮𝘀𝗸𝗲𝗱 𝗳𝗼𝗿 𝗗𝗮𝘁𝗮 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝘀𝗸𝗶𝗹𝗹𝘀 But, specific tools (e.g. Tableau) were rarely mentioned So focus on understanding the fundamentals of Data Viz ↳ Understanding your audience ↳ Defining clear metrics for a business ↳ Transforming vague product needs to dashboards ♻️ If you found this useful, repost it please!

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