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Oil bosses have big hopes for the AI boom Data centres are fuelling demand for natural gas—for now This week 180,000 people descended on Abu Dhabi to attend ADIPEC, the global oil-and-gas industry’s biggest annual gathering. This year’s focus, perhaps unsurprisingly, was the nexus of artificial intelligence (AI) and energy. On the eve of the jamboree Sultan Al Jaber, chief executive of ADNOC, the Emirati national oil giant, convened a private meeting of big tech and big energy bosses. A survey of some 400 energy, tech and finance bigwigs released in conjunction with the event concluded that AI is set to transform the energy business by boosting efficiency and cutting greenhouse-gas emissions.

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The 'bias machine': How Google tells you what you want to hear "We're at the mercy of Google." Undecided voters in the US who turn to Google may see dramatically different views of the world – even when they're asking the exact same question. Type in "Is Kamala Harris a good Democratic candidate", and Google paints a rosy picture. Search results are constantly changing, but last week, the first link was a Pew Research Center poll showing that "Harris energises Democrats". Next is an Associated Press article titled "Majority of Democrats think Kamala Harris would make a good president", and the following links were similar. But if you've been hearing negative things about Harris, you might ask if she's a "bad" Democratic candidate instead. Fundamentally, that's an identical question, but Google's results are far more pessimistic. "It's been easy to forget how bad Kamala Harris is," said an article from Reason Magazine in the top spot. Source-Link: BBC

Forecasting vs. Predictive Analytics: The Obama Example Analytics can influence elections, not just predict them. This article explores how the Obama campaign used predictive analytics to outmaneuver traditional forecasting. Forecasting vs. Predictive Analytics Nate Silver’s forecasting predicted state outcomes, while Obama’s team used predictive analytics to score individual voters, targeting those most likely to be persuaded. Impact of Predictive Analytics The Obama campaign optimized interactions, avoiding “do-not-disturb” voters and improving ad spending effectiveness by 18%. Conclusion Predictive analytics enables organizations to shape outcomes through personalized insights, distinguishing it from forecasting’s broad predictions.

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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 Join @datasciencefun to learning important data science and machine learning concepts ENJOY LEARNING 👍👍

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Any person learning deep learning or artificial intelligence in particular, know that there are ultimately two paths that they can go: 1. Computer vision 2. Natural language processing. I outlined a roadmap for computer vision I believe many beginners will find helpful. 👇👇 Artificial Intelligence

Day 26: Reinforcement Learning - Concept: Learning through interaction. - Implementation: Q-learning. - Evaluation: Reward function, policy. Day 27: Bayesian Networks - Concept: Probabilistic graphical models. - Implementation: Conditional dependencies. - Evaluation: Inference, learning. Day 28: Hidden Markov Models (HMM) - Concept: Time series analysis. - Implementation: Transition probabilities. - Evaluation: Viterbi algorithm. Day 29: Feature Selection Techniques - Concept: Improving model performance. - Implementation: Filter, wrapper methods. - Evaluation: Feature importance. Day 30: Hyperparameter Optimization - Concept: Model tuning. - Implementation: Grid search, random search. - Evaluation: Cross-validation. Share this channel with your real friends: https://t.me/datasciencefun Like if you want me to continue this series 😄❤️ ENJOY LEARNING 👍👍

Let's start with the topics we gonna cover in this 30 Days of Data Science Series, We will primarily focus on learning Data Science and Machine Learning Algorithms Day 1: Linear Regression - Concept: Predict continuous values. - Implementation: Ordinary Least Squares. - Evaluation: R-squared, RMSE. Day 2: Logistic Regression - Concept: Binary classification. - Implementation: Sigmoid function. - Evaluation: Confusion matrix, ROC-AUC. Day 3: Decision Trees - Concept: Tree-based model for classification/regression. - Implementation: Recursive splitting. - Evaluation: Accuracy, Gini impurity. Day 4: Random Forest - Concept: Ensemble of decision trees. - Implementation: Bagging. - Evaluation: Out-of-bag error, feature importance. Day 5: Gradient Boosting - Concept: Sequential ensemble method. - Implementation: Boosting. - Evaluation: Learning rate, number of estimators. Day 6: Support Vector Machines (SVM) - Concept: Classification using hyperplanes. - Implementation: Kernel trick. - Evaluation: Margin maximization, support vectors. Day 7: k-Nearest Neighbors (k-NN) - Concept: Instance-based learning. - Implementation: Distance metrics. - Evaluation: k-value tuning, distance functions. Day 8: Naive Bayes - Concept: Probabilistic classifier. - Implementation: Bayes' theorem. - Evaluation: Prior probabilities, likelihood. Day 9: k-Means Clustering - Concept: Partitioning data into k clusters. - Implementation: Centroid initialization. - Evaluation: Inertia, silhouette score. Day 10: Hierarchical Clustering - Concept: Nested clusters. - Implementation: Agglomerative method. - Evaluation: Dendrograms, linkage methods. Day 11: Principal Component Analysis (PCA) - Concept: Dimensionality reduction. - Implementation: Eigenvectors, eigenvalues. - Evaluation: Explained variance. Day 12: Association Rule Learning - Concept: Discover relationships between variables. - Implementation: Apriori algorithm. - Evaluation: Support, confidence, lift. Day 13: DBSCAN (Density-Based Spatial Clustering of Applications with Noise) - Concept: Density-based clustering. - Implementation: Epsilon, min samples. - Evaluation: Core points, noise points. Day 14: Linear Discriminant Analysis (LDA) - Concept: Linear combination for classification. - Implementation: Fisher's criterion. - Evaluation: Class separability. Day 15: XGBoost - Concept: Extreme Gradient Boosting. - Implementation: Tree boosting. - Evaluation: Regularization, parallel processing. Day 16: LightGBM - Concept: Gradient boosting framework. - Implementation: Leaf-wise growth. - Evaluation: Speed, accuracy. Day 17: CatBoost - Concept: Gradient boosting with categorical features. - Implementation: Ordered boosting. - Evaluation: Handling of categorical data. Day 18: Neural Networks - Concept: Layers of neurons for learning. - Implementation: Backpropagation. - Evaluation: Activation functions, epochs. Day 19: Convolutional Neural Networks (CNNs) - Concept: Image processing. - Implementation: Convolutions, pooling. - Evaluation: Feature maps, filters. Day 20: Recurrent Neural Networks (RNNs) - Concept: Sequential data processing. - Implementation: Hidden states. - Evaluation: Long-term dependencies. Day 21: Long Short-Term Memory (LSTM) - Concept: Improved RNN. - Implementation: Memory cells. - Evaluation: Forget gates, output gates. Day 22: Gated Recurrent Units (GRU) - Concept: Simplified LSTM. - Implementation: Update gate. - Evaluation: Performance, complexity. Day 23: Autoencoders - Concept: Data compression. - Implementation: Encoder, decoder. - Evaluation: Reconstruction error. Day 24: Generative Adversarial Networks (GANs) - Concept: Generative models. - Implementation: Generator, discriminator. - Evaluation: Adversarial loss. Day 25: Transfer Learning - Concept: Pre-trained models. - Implementation: Fine-tuning. - Evaluation: Domain adaptation.

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Starting your journey as a data analyst is an amazing start for your career. As you progress, you might find new areas that pique your interest: • Data Science: If you enjoy diving deep into statistics, predictive modeling, and machine learning, this could be your next challenge. • Data Engineering: If building and optimizing data pipelines excites you, this might be the path for you. • Business Analysis: If you're passionate about translating data into strategic business insights, consider transitioning to a business analyst role. But remember, even if you stick with data analysis, there's always room for growth, especially with the evolving landscape of AI. No matter where your path leads, the key is to start now.

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