This course is designed to provide students with a comprehensive understanding of data science and analytics concepts and techniques. The course covers the essential steps of the data science process, including data exploration, preparation, manipulation, and analysis. Students will gain hands-on experience with Python and Pandas for data manipulation, as well as statistical methods and data visualization techniques using tools like Matplotlib and Seaborn. The course introduces core machine learning concepts, such as supervised and unsupervised learning, regression analysis, classification techniques, and clustering methods. Students will explore the use of machine learning models, including linear regression, decision trees, and random forests, while learning to evaluate model performance using various metrics. In addition, the course covers important topics such as time series analysis, big data technologies (Hadoop and Spark), and the ethical considerations of data science, including privacy and data protection. Through a capstone project, students will integrate the concepts learned throughout the course, applying them to real-world data analysis challenges and presenting their findings. By the end of the course, students will be equipped with the necessary skills to handle and analyze data, apply machine learning algorithms, and make informed decisions based on data-driven insights.
- Teacher: Mustafa Alattar