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Syllabus |
1. Introduction to Data Science
Definition and importance of Data Science.
The role of a Data Scientist.
Lifecycle of a Data Science project.
2. Tools and Areas of Knowledge
Essential tools for a Data Scientist.
Introduction to algorithms and programming.
3. Introduction to Python and Jupyter Notebooks
Installation, configuration, and testing of development environments.
4. Python Programming
Introduction to Python libraries (Pandas, Matplotlib).
5. Practical Examples of Data Science
Data analysis, cleaning, and transformation.
6. Web Scraping
Data extraction techniques from the web.
Classification vs. Regression.
7. Predictive Models
Decision Trees.
Random Forests.
KNN and Clustering.
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Main Bibliography |
1. Joel Grus, Data Science from Scratch: First Principles with Python, 2nd Edition, O'Reilly Media, 2019.
2. Wes McKinney, Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython, O'Reilly Media, 2017.
3. Jake VanderPlas, Python Data Science Handbook: Essential Tools for Working with Data, O'Reilly Media, 2016.
4. Andreas C. Müller and Sarah Guido, Introduction to Machine Learning with Python: A Guide for Data Scientists, O'Reilly Media, 2016.
5. D.P. Kroese, Z.I. Botev, T. Taimre, R. Vaisman, Data Science and Machine Learning: Mathematical and Statistical Methods, Chapman & Hall/CRC, 2019.
6. Hui Lin, Ming Li, Practitioner’s Guide to Data Science, 2023.
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