How AI is changing data science and what you can do about it

Global demand for data professionals is accelerating. The World Economic Forum projects that Data Science and AI roles will grow by 34% by 2034, making them among the most sought-after careers of the decade. If you are feeling limited in your current role, now is the time to reskill. In just 4 months, you can transition into Data Science and AI with a hands-on, mentor-led program designed to help professionals build job-ready expertise. You will learn how to: • Analyze and visualize data using Python and Power BI • Develop AI solutions to solve real-world business problems • Build a portfolio that proves your readiness to employers Your next opportunity begins with upskilling. Download Brochure: https://hubs.li/Q03NGGfJ0 #eduCLaaS #DataScience #AI #LifelongLearning #FutureOfWork #CareerGrowth #UpskillAsia #DigitalCareers #LearningInnovation

🚀 Data Science in 2025 & what It Means for YOU in 2026 Let’s talk about what’s actually happening in the data world right now and what it means if you’re trying to build or pivot your career in 2026. Here’s what the data shows 👇 📊 Machine Learning & AI skills are exploding: deep learning mentions in job listings have doubled and now appear in 20% of all data science roles. 💻 Python & SQL remain non-negotiable. Over 69% of job posts explicitly require them. 🧮 Statistics & probability are still the backbone: flashy AI models mean nothing without solid fundamentals. ☁️ Cloud, data engineering, and MLOps are now must-haves, not “nice-to-haves.” 📈 Visualization & storytelling continue to be what separates a good data scientist from a great one. 🧠 And most importantly: business sense + human skills (critical thinking, communication, ethical reasoning) are skyrocketing in value. The takeaway? 2026 won’t just be about who can use AI… it’ll be about who can use it wisely, connecting data, business, and humanity together. If you’re breaking into the field now, focus on: ✅ Building a strong foundation (Python, SQL, Stats) ✅ Understanding real-world business problems ✅ Learning how to think, not just how to code This is how you future-proof your career in Data Science and AI. 🎓 Want a clear roadmap to get there? Join my free Data Science Career Webinar — I’ll show you the exact skills, projects, and strategy to land your first role fast: 👉 https://lnkd.in/ekp-jmD2 #DataScience #AI #Analytics #MachineLearning #FutureOfWork #CareerGrowth #DataScienceInfinity

“Switching from Data Analysis to Data Science” 🚀 From Data Analysis → Data Science: My Next Career Chapter After working in Data Analysis, I realized that understanding data is just the beginning — the real magic lies in predicting, automating, and driving decisions using it. That’s what inspired me to take the leap toward Data Science. ~~ What I learned as a Data Analyst: Cleaning, processing, and visualizing data Understanding business KPIs and decision-making Writing complex SQL queries and automation scripts Building dashboards that tell stories through data ~~ What I’m focusing on as a Data Scientist: Machine Learning & Deep Learning models Feature Engineering & Model Evaluation NLP, Generative AI & LLMs (for real-world applications) Deployment with Flask, Streamlit & Docker 💡 The transition wasn’t overnight — it’s a journey of continuous learning, experimentation, and building projects that make an impact. 👉 To everyone planning a similar switch: Start where you are, use what you know, and keep building one skill at a time. Your analysis mindset is your strongest foundation. If it is helpful, please repost and follow Roshan Jha #DataScience #DataAnalysis #CareerSwitch #MachineLearning #AI #DataJourney #Python #JroshanCode #CodeJroshan #LLM #AI #ML

🚀 What It Takes To Be A Difference-Making Data Scientist Moving from data to real-world impact requires a powerful blend of skills. Here's the breakdown: 🔧 Data Technology • Master the data pipeline: collect, store, and synthesize data to build a foundation of truth. • Leverage tools like ML algorithms and data visualization to identify trends and opportunities. 🧠 Math & Analysis • Apply mathematical frameworks to quantify impact and test hypotheses. • Transform raw numbers into actionable insights and robust strategies. 📚 Continuous Training • Stay ahead of the curve by continuously learning new tools, frameworks, and methodologies. • Commit to certifications and ongoing education to maintain a sharp competitive edge. By excelling in these three areas, data scientists become true strategic partners. What would you add to the list? Follow Buzz Data Science where ideas turn into intelligence. #DataScience #ML #AI #DataDriven #TechCareer #ProfessionalDevelopment #Analytics

The roadmap from Data Analyst to Deep Learning expert is shorter than you think (and it's free). . I wasted my first 6 months in data science. 😵💫 . I was drowning in a sea of outdated tutorials and messy GitHub repos. It felt like collecting puzzle pieces without ever seeing the picture on the box. . The real breakthrough wasn't learning another complex algorithm. It was about simplifying my focus. . It reminds me of the tools we use. Everyone knows TensorFlow, but did you know HuggingFace—the giant in NLP—was named after the 🤗 emoji? Their goal was to make a complex field feel friendly and accessible. . That’s the secret: making complexity accessible. . It's why we created our free tip sheets at DataSimple, distilling everything into the 69 most crucial tips across: . 📊 Data Analysis (Pandas, Seaborn) 🤖 Machine Learning (SKLearn, XGBoost) 🧠 Deep Learning (TensorFlow, Transformers) . No fluff. Just the skills that actually move the needle, all in one place. Because learning data science shouldn't be a full-time job before you get the job. . What's the one tool or library that was a total game-changer for you? 👇 . Unlock your personalized learning path: https://lnkd.in/d53ZuzHA . #data #datascience #dataanalysis #dataanalyst #datajobs #datasciencejobs #python #pandas #seaborn #plotly #machinelearning #deeplearning #sklearn #tensorflow #career #tech #coding #education

Machine Learning isn’t magic — it’s math! If you’re a beginner or junior data scientist. These are the must-know formulas you’ll use in day-to-day projects 👇 🔹 Linear Regression → Predict numbers (sales, prices) 🔹 Logistic Regression → Probabilities for classification 🔹 MSE → Measure error in predictions 🔹 Accuracy, Precision, Recall, F1 → Evaluate classification models 🔹 Entropy → Core of decision trees 🔹 Gradient Descent → Teaches models how to learn 💡 Mastering these simple formulas makes ML less scary and more logical. 👉 Save this post and keep it as your quick reference guide! Follow DataElements.ai for quality content #machinelearning

Let’s talk about the kind of DSA that actually matters in Data Science👇 Here’s what that really looks like: 1️⃣ Arrays, Matrices & Tensors Think NumPy, Pandas, PyTorch. They’re the backbone of every preprocessing step, linear algebra operation, or deep learning model you’ll ever build. 2️⃣ Hash Maps / Dictionaries Perfect for feature mapping, label encoding, vocab lookups, or frequency counts. You’ll use them in NLP, config files, or caching model metadata way more often than you think. 3️⃣ Heaps / Priority Queues These shine in ranking problems like Top-K recommendations, hyperparameter tuning, or efficient sampling in pipelines. 4️⃣ Graphs Used in Graph Neural Networks, recommendation systems, or even dependency tracking in complex ML pipelines. 5️⃣ Trees The foundation of every tree-based model - Decision Trees, XGBoost, CatBoost. If you understand splits and structure, you understand why your model behaves the way it does. 6️⃣ Stacks & Queues (Lightweight) They show up in backtracking, parsing streaming data, and buffering during ETL processes. Not flashy, but essential in real-time systems. 7️⃣ Arrays + Sliding Windows + Two Pointers Your best friends in time-series and anomaly detection. Used for rolling statistics, sequence optimization, and feature extraction. 8️⃣ Recursion & Divide-and-Conquer Still relevant - underpins sorting, tree traversals, distributed training, and even ensemble model logic. Data Scientist / ML Engineer → 25% DSA + 75% Stats | ML | Python | SQL So, to prepare for all the topics in Data Science, I’d recommend Bosscoder Academy’s Data Science Program. Check it here - bcalinks.com/mAcCF2G They’ve helped 2200+ working professionals through: ✅Structured curriculum covering ML, Deep Learning & GenAI ✅Industry projects with real world datasets that build your portfolio ✅1:1 mentorship from Data Scientist at top product companies ✅Placement support to help you land your dream data role #dsa #datascience #sponsoredpost

"Data Science Workflow Explained: Turning Data into Actionable Insights" "Data Science: From Raw Data to Real Decisions" Every organization today generates enormous amounts of data — but without proper analysis, it remains an untapped resource. Data Science bridges this gap by combining statistical modeling, machine learning, and programming to deliver actionable insights. At its core, the workflow typically involves: Data Collection & Cleaning: Handling structured (databases, CSVs) and unstructured data (text, images, logs) while addressing missing values, outliers, and inconsistencies. Exploratory Data Analysis (EDA): Using tools like Pandas, NumPy, Matplotlib, Seaborn to detect trends, correlations, and anomalies. Feature Engineering: Converting raw attributes into meaningful variables that improve model accuracy and generalization. Modeling & Machine Learning: Applying algorithms such as regression, decision trees, clustering, or neural networks with scikit-learn, TensorFlow, PyTorch. Model Evaluation: Leveraging metrics like accuracy, precision, recall, F1-score, and ROC-AUC to measure performance. Deployment & Monitoring: Scaling solutions via Cloud Computing (AWS, Azure, GCP) and maintaining performance through MLOps practices. I am currently building my foundation in this field through Python, AI, and Cloud Computing. My next step is to experiment with real-world datasets — such as sports statistics, customer reviews, or financial data — to practice EDA, feature engineering, and supervised learning models. Even seemingly simple datasets can uncover powerful insights once structured and analyzed correctly. What excites me most is the versatility of Data Science: 🏥 Healthcare → Predictive diagnostics with supervised learning 💳 Finance → Fraud detection using anomaly detection algorithms 📦 Retail → Recommendation systems powered by collaborative filtering 🌍 Climate Science → Forecasting trends with time-series models ✨ For me, Data Science isn’t just about analyzing data; it’s about engineering scalable solutions that create measurable impact. 👉 I’d love to connect with data professionals and explore more about MLOps, model deployment, and real-time analytics. #DataScience #MachineLearning #AI #Python #CloudComputing #MLOps #BigData

The "Data Scientist" Title Is About to Break By 2026, the job description for "Data Scientist" as we know it will be obsolete. When I started, Python, SQL, and Machine Learning were the keys to standing out. Today, that’s just the baseline. The Data Scientist of 2026 won’t just build models; they will be the Business Translator and AI Architect. They'll bridge the huge gap between technical work and strategic decisions. Here's the new mandatory skill stack you need to master: AI Tooling & Integration: Knowing how to intelligently leverage LLMs and AI automation, not just building models from scratch. Work smarter. Data Storytelling: Turning complex insights into clear, persuasive narratives that force executives to act. Influence, not just information. Deep Domain Expertise: Understanding the business why behind every dataset. The connection between data and strategy is where the real value is created. Stop chasing only tools. Focus on the skill that translates insights into influence. #DataScience #AI #CareerGrowth #DataAnalytics #FutureOfWork

AI Engineering is the new Data Science. Remember when every other ad said “Become a Data Scientist in 6 months”? That era built a generation of professionals who knew Python, Pandas, and Power BI. But now, we’re stepping into the next wave and soon you’ll start seeing ads everywhere saying , “Become an AI Engineer.” And it’s not hype — it’s evolution. Let’s break this down technically 👇 When Data Science became big, the core stack was: Data → Statistics → Machine Learning → Insights. Now with AI Engineering, that stack looks like: Data → Machine Learning → MLOps → LLMs → Agentic Systems. It’s the same foundation — but extended into real-world intelligence and automation. AI Engineers don’t just analyze or predict they design systems that learn, reason, and act. Their day-to-day involves: -Building and deploying ML models using TensorFlow, PyTorch, and Scikit-learn. -Designing MLOps pipelines using Docker, Kubernetes, Jenkins, or ArgoCD. Integrating LLMs & RAG systems with APIs and databases. -Optimizing inference latency with caching, quantization, and prompt engineering. -Handling versioning & monitoring using MLflow, DVC, and Prometheus. -Creating Agentic workflows with memory, tools, and planning layers. Basically — everything a Data Scientist dreamed of doing, but now end-to-end and in production. So yes AI Engineering is the next Data Science. And right now is the perfect time to get ahead of the curve before it gets overcrowded. Because once the trend hits full swing, you won’t just need to know AI you’ll need to engineer it. 🚀 That’s exactly what we’ll cover in the upcoming AI Engineering Bootcamp from SQL & Python to ML, MLOps, GenAI, and Agentic AI hands-on, practical, and industry-aligned. 👉 Join the waitlist here https://lnkd.in/g89QQDrb Big thanks to Rahul Shukla, Shashank Mishra 🇮🇳 , SHAILJA MISHRA🟢, Aman Kumar, and Sahil Choudhary for shaping how we teach real-world AI at Grow Data Skills 🙌 #AIEngineering #DataScience #MachineLearning #MLOps #GenAI #AgenticAI #ArtificialIntelligence #GrowDataSkills #CareerGrowth #FutureOfWork