Data Wrangling Data wrangling

[sadiksojib125]

Machine Learning Machine Learning (ML) in data science involves developing algorithms that allow computers to learn from and make data-based decisions. ML skills include supervised learning (predicting outcomes), unsupervised learning (identifying patterns), and reinforcement learning (learning by trial and error). Data scientists need to understand different models (like decision trees, neural networks, and SVMs), how to train them with data, and how to evaluate their performance. 4. , or munging, is cleaning and transforming raw data into a more suitable format for analysis.


This includes handling missing values, incorrect data types, and merging australia whatsapp mobile phone number list data from different sources. Effective data wrangling minimizes errors and biases in the analysis, making it a critical step in the data science process. 5. Programming Programming is fundamental in data science for manipulating data and performing analyses. Python and R are the most popular languages due to their powerful libraries and frameworks for data analysis (like Pandas, NumPy, and dplyr). SQL is also essential for database management and data retrieval.



Proficiency in these languages allows data scientists to process large datasets and implement algorithms efficiently. 6. Predictive Modeling Predictive modeling involves using statistical models to predict an outcome based on historical data. It's widely used in finance, healthcare, and marketing to forecast trends and behaviors. Data scientists build models using techniques such as regression, clustering, and time series analysis, and they must be skilled in validating models using methods like cross-validation and AUC-ROC curves. 7.