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Software Engineers vs AI Engineers: 👊 Software engineers are often shocked when they learn of AI engineers' salaries. There are two reasons for this surprise. 1. The total compensation for AI engineers is jaw-dropping. You can check it out at AIPaygrad.es, which has manually verified data for AI engineers. The median overall compensation for a “Novice” is $328,350/year. 2. AI engineers are no smarter than software engineers. You figure this out only after a friend or acquaintance upskills and finds a lucrative AI job. The biggest difference between Software and AI engineers is the demand for such roles. One role is declining, and the other is reaching stratospheric heights. Here is an example. Just last week, we saw an implosion of OpenAI after Sam Altman was unceremoniously removed from his CEO position. About 95% of their AI Engineers threatened to quit in protest. Rumor had it that these 700 engineers had an open job offer from Microsoft. 🚀 Contrast this with the events a few months back. Microsoft laid off 10,000 Software Engineers while setting aside $10B to invest in OpenAI. They cut these jobs despite making stunning profits in 2023. In conclusion, these events underline a significant shift in the tech industry. For software engineers, it's a call to adapt and possibly upskill in AI, while companies need to balance AI investments with nurturing their current talent. The future of tech hinges on flexibility and continuous learning for everyone involved."

The Rise of Generative AI in Data Analytics Today, let’s talk about how Generative AI is reshaping the field of Data Analytics and what this means for YOU as a data professional! What is Generative AI in Data Analytics Context? Generative AI refers to AI models that can generate text, code, images, and even data insights based on patterns. Tools like ChatGPT, Bard, Copilot, and Claude are now being used to: ✅ Automate data cleaning & transformation ✅ Generate SQL & Python scripts for complex queries ✅ Build interactive dashboards with natural language commands ✅ Provide explainable insights without deep statistical knowledge How Businesses Are Using AI-Powered Analytics 📊 Retail & E-commerce – AI predicts sales trends and personalizes recommendations. 🏦 Finance & Banking – Fraud detection using AI-powered anomaly detection. 🩺 Healthcare – AI analyzes patient data for early disease detection. 📈 Marketing & Advertising – AI automates customer segmentation and sentiment analysis. Should Data Analysts Be Worried? NO! Instead of replacing data analysts, AI enhances their work by: 🚀 Speeding up data preparation 🔍 Enhancing insights generation 🤖 Reducing manual repetitive tasks How You Can Adapt & Stay Ahead 🔹 Learn AI-powered tools like Power BI’s Copilot, ChatGPT for SQL, and AutoML. 🔹 Improve prompt engineering to interact effectively with AI. 🔹 Focus on critical thinking & domain knowledge—AI can’t replace human intuition! Generative AI is a game-changer, but the human touch in analytics will always be needed! Instead of fearing AI, use it as your assistant. The future belongs to those who learn, adapt, and innovate. Here are some telegram channels related to artificial Intelligence and generative AI which will help you with free resources: https://t.me/generativeai_gpt https://t.me/machinelearning_deeplearning https://t.me/AI_Best_Tools https://t.me/aichads https://t.me/aiindi Last one is my favourite ❤️ React with ❤️ if you want me to continue posting on such interesting & useful topics Share with credits: https://t.me/sqlspecialist Hope it helps :)

⬇️ Pysentimiento: A Python toolkit for Sentiment Analysis and Social NLP tasks Github: https://github.com/pysentimiento/pysentimiento Paper: https://arxiv.org/abs/2106.09462 English model: https://huggingface.co/finiteautomata/bertweet-base-sentiment-analysis

Important Topics to become a data scientist [Advanced Level] 👇👇 1. Mathematics Linear Algebra Analytic Geometry Matrix Vector Calculus Optimization Regression Dimensionality Reduction Density Estimation Classification 2. Probability Introduction to Probability 1D Random Variable The function of One Random Variable Joint Probability Distribution Discrete Distribution Normal Distribution 3. Statistics Introduction to Statistics Data Description Random Samples Sampling Distribution Parameter Estimation Hypotheses Testing Regression 4. Programming Python: Python Basics List Set Tuples Dictionary Function NumPy Pandas Matplotlib/Seaborn R Programming: R Basics Vector List Data Frame Matrix Array Function dplyr ggplot2 Tidyr Shiny DataBase: SQL MongoDB Data Structures Web scraping Linux Git 5. Machine Learning How Model Works Basic Data Exploration First ML Model Model Validation Underfitting & Overfitting Random Forest Handling Missing Values Handling Categorical Variables Pipelines Cross-Validation(R) XGBoost(Python|R) Data Leakage 6. Deep Learning Artificial Neural Network Convolutional Neural Network Recurrent Neural Network TensorFlow Keras PyTorch A Single Neuron Deep Neural Network Stochastic Gradient Descent Overfitting and Underfitting Dropout Batch Normalization Binary Classification 7. Feature Engineering Baseline Model Categorical Encodings Feature Generation Feature Selection 8. Natural Language Processing Text Classification Word Vectors 9. Data Visualization Tools BI (Business Intelligence): Tableau Power BI Qlik View Qlik Sense 10. Deployment Microsoft Azure Heroku Google Cloud Platform Flask Django

📝 New research on text creativity Scientists have shown: texts created by humans are semantically newer than those generated by AI. 🔎 How it was measured They introduced the metric "semantic novelty" — the cosine distance between adjacent sentences. 🧠 Main findings Human texts consistently show higher novelty across different embedding models (RoBERTa, DistilBERT, MPNet, MiniLM). In the "human-AI storytelling" dataset, the human contribution was semantically more diverse. ✨ But there is a nuance What we call AI "hallucinations" can be useful in collaborative storytelling. They add unexpected twists and help maintain interest in the story. 👉 Conclusion: humans are more innovative, AI is more predictable, but together they enhance each other. 🔗 Details