Mahmoud A.

1- Data analysis process of questioning, wrangling, exploring, analyzing, and communicating data. Learn how to work with data in Python using libraries like NumPy and Pandas. 2- Apply inferential statistics and probability to important, real-world scenarios, such as analyzing A/B tests and building supervised learning models. 3- Apply sound design and data visualization principles to the data analysis process. Learn how to use analysis and visualizations to tell a story with data. 4- Develop and implement machine learning models and algorithms to solve business problems and improve product performance. 5- Collect, preprocess, and analyze large datasets to extract meaningful insights and features for model training. 6- Design and experiment with various machine learning techniques, including supervised and unsupervised learning, deep learning, and reinforcement learning. 7- Evaluate model performance and accuracy using appropriate metrics and validation techniques. Optimize machine learning pipelines and workflows for scalability, efficiency, and performance.