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 The incredible amount of data that our modern societies are accumulating has led to the development of specific techniques for sorting, ordering, analysing or displaying these data. Data sciences is the discipline based on mathematical, statistical and computer tools, including machine learning, that encompasses these techniques. This course will introduce to the students the basic notions and techniques of data sciences, including machine learning, with practical applications in Biomedical or Environmental engineering. The incredible amount of data that our modern societies are accumulating has led to the development of specific techniques for sorting, ordering, analysing or displaying these data. Data sciences is the discipline based on mathematical, statistical and computer tools, including machine learning, that encompasses these techniques. This course will introduce to the students the basic notions and techniques of data sciences, including machine learning, with practical applications in Biomedical or Environmental engineering.
 The course includes a significant practical component with programming in the Python language. The course includes a significant practical component with programming in the Python language.
-Summary: + 
 + 
 +**Summary:**  
 Lecture 1: Data : The data in data science Lecture 1: Data : The data in data science
 +
 Lab 1 : Python basics adapted from the "Machine learning preparatory week @PSL" (Python & NumPy) Lab 1 : Python basics adapted from the "Machine learning preparatory week @PSL" (Python & NumPy)
  
  
 Lecture 2: Python and pandas : Tabular data in Python Lecture 2: Python and pandas : Tabular data in Python
 +
 Lab 2: Notebook on European past floods Lab 2: Notebook on European past floods
  
 Lecture 3: Machine Learning: history; applications; recent successes from the "Machine learning preparatory week @PSL" Lecture 3: Machine Learning: history; applications; recent successes from the "Machine learning preparatory week @PSL"
 +
 Lab 3: Loading big datasets; Analyzing big datasets Lab 3: Loading big datasets; Analyzing big datasets
  
 Lecture 4: Introduction to machine learning from the "Machine learning preparatory week @PSL" Lecture 4: Introduction to machine learning from the "Machine learning preparatory week @PSL"
 +
 Lab 4: Dimensionality; Dimensionality reduction : Principal Component Analysis; Classification Lab 4: Dimensionality; Dimensionality reduction : Principal Component Analysis; Classification
  
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 Lecture 6: Scikit-learn: estimation and pipelines from the "Machine learning preparatory week @PSL" Lecture 6: Scikit-learn: estimation and pipelines from the "Machine learning preparatory week @PSL"
 +
 Lab 5: finish the previous notebooks Lab 5: finish the previous notebooks
  
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 **References** **References**
-Python for data science 
  
-    [[Programming in Python for Data Science]https://prog-learn.mds.ubc.ca/en/] 
-    Scientific Computing in Python: Introduction to NumPy and Matplotlib 
-    Python Data Science Handbook 
  
-Python for geo data science+**Python for data science** 
 + 
 + 
 +[[https://prog-learn.mds.ubc.ca/en/|Programming in Python for Data Science]] 
 + 
 +[[https://sebastianraschka.com/blog/2020/numpy-intro.html|Scientific Computing in Python: Introduction to NumPy and Matplotlib]] 
 + 
 +[[https://jakevdp.github.io/PythonDataScienceHandbook/|Python Data Science Handbook]] 
 + 
 +**Python for geo data science** 
 + 
 +[[https://geo-python-site.readthedocs.io/en/latest/|Geo-Python course]]
  
-    Geo-Python course +[[https://automating-gis-processes.github.io/site/|Automating GIS-processes]]
-    Automating GIS-processes+
  
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