CSC 2623 - Exploring Data

• Write and debug Python programs using control flow, data structures, and objects to solve basic computational problems
• Collect and evaluate real-world data, recognizing potential sources of bias and error in measurement and sampling
• Use Python data libraries (e.g., Pandas, Matplotlib) to work with tabular data sets
• Choosing appropriate visualization techniques
• Assess appropriate research questions that can be answered with a given data set
• Identifying issues in data and applying appropriate data cleaning techniques
• Identify and describe different data types and levels of measurement, understanding how they influence analysis and interpretation
• Interpret results of fundamental statistical concepts such as correlation, probability distributions, and p-values to explore data patterns and test hypotheses
• Differentiate between data science and machine learning
• Differentiate between supervised and unsupervised learning
• Explain and apply introductory machine learning algorithms, including K-Nearest Neighbors and K-Means clustering
• Evaluate the results of applying a machine learning model to a particular data set
• Explain and apply ethical principles in data and AI, understanding common failures and responsible AI practices
• Explore advanced AI applications, including deep learning, text embeddings, and generative models, while assessing their strengths and limitations

• Programming experience in a high-level language