CMSC1217

This course introduces data analytics using Python to extract and communicate insights from datasets. Students acquire foundational skills in data manipulation, including transformation and cleansing, alongside techniques for visualizing data distributions and managing file-based data input/output, preparing them for machine learning applications in subsequent semesters. The course focuses on technical concepts such as trend identification through graphical analysis, data structuring for computational efficiency, and processing real-world datasets for artificial intelligence applications. Through programming exercises and a capstone project analyzing a representative dataset, students develop practical Python programming competencies essential for advanced coursework in machine learning. Student Learning Outcomes

• Identify fundamental concepts of data analytics and their role in preparing data for machine learning applications.
• Explain how Python programming streamlines data analysis processes for real-world datasets.
• Manipulate datasets in Python, including filtering, sorting, and handling missing values.
• Compare Python data visualization techniques to represent data trends effectively.
• Assess the quality of a dataset's preparation in Python, identifying areas for improvement in data cleaning and transformation for machine learning readiness.
• Design a data analytics project in Python to extract insights from a real-world dataset.