Data Science Techniques and Algorithms
Data Science Context, Methodologies and Processes. Modeling for Data Science. Manipulation, Visualization, Statistical Analysis and Communication of Scientific Data. Tools for Scientific Computing and Data Science. Integrated Development Environments (IDEs) and Analytical Environments. Jupyter Notebook, Numpy, Pandas. Application Program Interface (APIs) and Cloud Environments. Data Science Projects.
Basic Information
Workload
60 hours
Requirements
Introduction to Data Science, Programming Languages, Exploratory Data Analysis and Visualization
Mandatory:
- Aurelien Geron. Hands–On Machine Learning with Scikit–Learn and TensorFlow Concepts, Tools, and Techniques to Build Intelligent Systems. 2ª edição, O’Reilly, outubro de 2019.
- Christopher Bishop. Pattern Recognition and Machine Learning. Springer, 2006. Disponível em https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf
Complementary:
- Stormy Attaway. MATLAB: A Practical Introduction to Programming and Problem Solving. 5th Edition, Elsevier, 2018.
- James, G., Witten, D., Hastie, T., Tibshirani, R. An Introduction to Statistical Learning with Applications in R. Disponível em https://web.stanford.edu/~hastie/Papers/ESLII.pdf
- Wes McKinney. Python for Data Analysis O'Reilly Media.
- The Scipy community. Numpy Manual, 2017. Disponível em https://docs.scipy.org/doc/numpy-1.13.0/contents.html
- Dan Saber. A Dramatic Tour through Python’s Data Visualization Landscape (including ggplot and Altair). Disponível em https://dsaber.com/2016/10/02/a-dramatic-tour-through-pythons-data-visualization-landscape-including-ggplot-and-altair/
- Jupyter Team. The Jupyter notebook User Documentation. Disponível em https://jupyter-notebook.readthedocs.io/en/stable/
- An introduction to machine learning with scikit-learn — scikit-learn 0.24.1 documentation. Disponível em https://scikit-learn.org/stable/tutorial/basic/tutorial.html
- User guide and tutorial — seaborn 0.11.1. Disponível em https://seaborn.pydata.org/tutorial.html