24 Data Science Term Explained.

Here’s a brief explanation of each data science term in easy-to-understand English:

1. A/B Testing: Comparing two versions of something (like a webpage) to see which one performs better.

2. Algorithm: A step-by-step method for calculations or solving problems.

3. Artificial Intelligence (AI): Machines designed to perform tasks that usually require human intelligence.

4. Big Data: Large, complex data sets that traditional methods can't easily handle.

5. Classification: Categorizing data points into predefined groups or classes.

6. Clustering: Grouping similar data points together based on their characteristics.

7. Data Mining: Finding patterns and useful information in large sets of data.

8. Data Preprocessing: Cleaning and preparing raw data for analysis.

9. Decision Trees: A tree-like model used to make decisions by following a set of choices.

10. Deep Learning: A type of machine learning that uses multiple layers of networks to learn from data.

11. Ensemble Learning: Combining multiple models to improve prediction accuracy.

12. Feature Engineering: Creating new features from raw data to improve model performance.

13. Gradient Descent: An optimization technique that finds the minimum value of a function to improve model accuracy.

14. Hyperparameter Tuning: Adjusting settings in a model to achieve the best performance.

15. Machine Learning: Algorithms that enable systems to learn from data and make decisions.

16. Natural Language Processing (NLP): Teaching machines to understand and interpret human language.

17. Neural Networks: Systems modeled after the human brain, used to recognize patterns in data.

18. Overfitting: When a model learns the training data too well and fails to generalize to new data.

19. Predictive Analytics: Using data to predict future events or trends.

20. Random Forest: A collection of decision trees that improve prediction accuracy.

21. Regression Analysis: Modeling relationships between variables to make predictions.

22. Reinforcement Learning: Learning by receiving rewards or penalties for actions.

23. Supervised Learning: Training a model with labeled data, where the outcome is known.

24. Time Series Analysis: Studying data points collected over time to identify trends or patterns.