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Essential Data Science Concepts 👇 1. Data cleaning: The process of identifying and correcting errors or inconsistencies in data to improve its quality and accuracy. 2. Data exploration: The initial analysis of data to understand its structure, patterns, and relationships. 3. Descriptive statistics: Methods for summarizing and describing the main features of a dataset, such as mean, median, mode, variance, and standard deviation. 4. Inferential statistics: Techniques for making predictions or inferences about a population based on a sample of data. 5. Hypothesis testing: A method for determining whether a hypothesis about a population is true or false based on sample data. 6. Machine learning: A subset of artificial intelligence that focuses on developing algorithms and models that can learn from and make predictions or decisions based on data. 7. Supervised learning: A type of machine learning where the model is trained on labeled data to make predictions on new, unseen data. 8. Unsupervised learning: A type of machine learning where the model is trained on unlabeled data to find patterns or relationships within the data. 9. Feature engineering: The process of creating new features or transforming existing features in a dataset to improve the performance of machine learning models. 10. Model evaluation: The process of assessing the performance of a machine learning model using metrics such as accuracy, precision, recall, and F1 score.