Introduction to Data Science • What is Data Science? • Data Science vs. other fields (e.g. Statistics, Computer Science) • Applications of Data Science • Tools and technologies used in Data Science Data Collection and Cleaning • Data sources and types • Data collection techniques (e.g. scraping, APIs) • Data cleaning and preprocessing • Data quality assessment and validation Exploratory Data Analysis • Data visualization • Descriptive statistics • Exploratory data analysis techniques (e.g. clustering, dimensionality reduction) Statistical Inference • Probability theory and distributions • Hypothesis testing • Confidence intervals • Regression analysis Machine Learning • Supervised learning (e.g. regression, classification) • Unsupervised learning (e.g. clustering, dimensionality reduction) • Deep learning (e.g. neural networks, convolutional neural network Data Management and Storage • Data management system (ex. database) Data Visualization and Communication • Effective data visualization techniques • Communicating insights and results to stakeholders • Presenting data and analysis in a clear and concise manner Project • Apply Data Science techniques to a real-world problem • Develop a project from data collection and cleaning to analysis and visualization • Present the results to stakeholders in a clear and concise manner.