This course introduces students to the foundations of data science, covering data collection, cleaning, visualization, statistical analysis, and basic machine learning. Students will gain hands-on experience using Python, Excel, and data visualization tools to analyze real-world datasets. Through a combination of theoretical learning and practical application, students will explore the following topics. Emphasis will be placed on enhancing problem-solving abilities and fostering critical thinking skills within a computing context. By the end of this course, students will have acquired a solid foundation to pursue further studies or apply their knowledge in real-world computing scenarios. Below is a list of specific topics covered on this course:

Week-1: Introduction to Data Science (& Applications)

Week-2: Understanding Data (Types, Formats, Sources)

Week-3: Python for Data Science I (Basics, Pandas, NumPy)

Week-4: Python for Data Science II (Data Cleaning & Transformation)

Week-5: Data Visualization I (Matplotlib & Seaborn Basics)

Week-6: Data Visualization II (Advanced Visuals & Dashboards)

Week-7: Statistics for Data Science (Descriptive & Probability)

Week-8: Introduction to Machine Learning (Regression & Clustering)

Week-9: Data Ethics & Responsible Use

Week-10: Data Storytelling & Communication

Week-11: Capstone Project (Group Work)

Week12: Final Presentations & Course Wrap-up