Block I.- Languages for data science.
1. Characteristics of data science-oriented languages: Matlab, Python
and R.
2. Matlab for scientific computing. Data structures. Multithreaded parallelization and
on GPU. Batch mode. Cluster parallelization.
3. Python for scientific computing. Data structures. Parallelization
multiprocess. Cluster parallelization.
Block II.- Version control and cloud computing
1. Introduction to version control.
2. Introduction to Git. Version control with GitKraken
3. Online repositories and collaborative development: Github, Gitlab, Bitbucket
4. Cloud computing services (Microsoft Azure, Amazon AWS, Google
Cloud), virtualization and deployment.
Block III.- Frameworks for data science and machine learning
1. Data structures and analysis: SciPy (numpy, matplotlib, pandas)
2. Machine learning: Scikit-learn
3. Deep Learning: keras, tensorflow
