syllabus lecture numbers

Part I: Data fundamentals 1. Introduction: Big Data/Data Science, course overview 2. An introduction to data and data processing 3. Exercises/Workshop 1: Tools, working with text files 4. Data storage and data structures 5. ʹBig Dataʹ from the Web 6. Exercises/Workshop 2: Computer code and data storage Part II: Data gathering and data preparation 1. Programming with data 2. Data sources, data gathering, data import 3. Exercises/Workshop 3: Programming with data 4. Working with semi‑structured and unstructured data 5. Data preparation and manipulation 6. Exercises/Workshop 4: Data import and data preparation/manipulation 7. Case Study: The Programmable Web, Big Public Data, and Political Economics Part III: Analysis and visualization 1. Understanding basic statistics with R 2. Exercises/Workshop 5: Applied data analysis with R 3. Visualization, dynamic documents 4. Exercises/Workshop 6: Visualization, dynamic documents 5. Wrap‑Up, Q&A

Part I: Data fundamentals 1. Introduction: Big Data/Data Science, course overview 2. An introduction to data and data processing 3. Exercises/Workshop 1: Tools, working with text files 4. Data storage and data structures 5. ʹBig Dataʹ from the Web 6. Exercises/Workshop 2: Computer code and data storage Part II: Data gathering and data preparation 1. Programming with data 2. Data sources, data gathering, data import 3. Exercises/Workshop 3: Programming with data 4. Working with semi‑structured and unstructured data 5. Data preparation and manipulation 6. Exercises/Workshop 4: Data import and data preparation/manipulation 7. Case Study: The Programmable Web, Big Public Data, and Political Economics Part III: Analysis and visualization 1. Understanding basic statistics with R 2. Exercises/Workshop 5: Applied data analysis with R 3. Visualization, dynamic documents 4. Exercises/Workshop 6: Visualization, dynamic documents 5. Wrap‑Up, Q&A

Part I: Data fundamentals 1. Introduction: Big Data/Data Science, course overview 2. An introduction to data and data processing 3. Exercises/Workshop 1: Tools, working with text files 4. Data storage and data structures 5. ʹBig Dataʹ from the Web 6. Exercises/Workshop 2: Computer code and data storage Part II: Data gathering and data preparation 1. Programming with data 2. Data sources, data gathering, data import 3. Exercises/Workshop 3: Programming with data 4. Working with semi‑structured and unstructured data 5. Data preparation and manipulation 6. Exercises/Workshop 4: Data import and data preparation/manipulation 7. Case Study: The Programmable Web, Big Public Data, and Political Economics Part III: Analysis and visualization 1. Understanding basic statistics with R 2. Exercises/Workshop 5: Applied data analysis with R 3. Visualization, dynamic documents 4. Exercises/Workshop 6: Visualization, dynamic documents 5. Wrap‑Up, Q&A

Part I: Data fundamentals 1. Introduction: Big Data/Data Science, course overview 2. An introduction to data and data processing 3. Exercises/Workshop 1: Tools, working with text files 4. Data storage and data structures 5. ʹBig Dataʹ from the Web 6. Exercises/Workshop 2: Computer code and data storage Part II: Data gathering and data preparation 1. Programming with data 2. Data sources, data gathering, data import 3. Exercises/Workshop 3: Programming with data 4. Working with semi‑structured and unstructured data 5. Data preparation and manipulation 6. Exercises/Workshop 4: Data import and data preparation/manipulation 7. Case Study: The Programmable Web, Big Public Data, and Political Economics Part III: Analysis and visualization 1. Understanding basic statistics with R 2. Exercises/Workshop 5: Applied data analysis with R 3. Visualization, dynamic documents 4. Exercises/Workshop 6: Visualization, dynamic documents 5. Wrap‑Up, Q&A

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