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+ | ====== Computer Sciences & data sciences ====== | ||
+ | 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, | ||
+ | The course includes a significant practical component with programming in the Python language. | ||
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+ | **Summary: | ||
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+ | Lecture 1: Data : The data in data science | ||
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+ | Lab 1 : Python basics adapted from the " | ||
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+ | Lecture 2: Python and pandas : Tabular data in Python | ||
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+ | Lab 2: Notebook on European past floods | ||
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+ | Lecture 3: Machine Learning: history; applications; | ||
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+ | Lab 3: Loading big datasets; Analyzing big datasets | ||
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+ | Lecture 4: Introduction to machine learning from the " | ||
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+ | Lab 4: Dimensionality; | ||
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+ | Lecture 5: Supervised machine learning models from the " | ||
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+ | Lecture 6: Scikit-learn: | ||
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+ | Lab 5: finish the previous notebooks | ||
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+ | Project: Presentation, | ||
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+ | **References** | ||
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+ | **Python for data science** | ||
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+ | [[https:// | ||
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+ | [[https:// | ||
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+ | [[https:// | ||
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+ | **Python for geo data science** | ||
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+ | [[https:// | ||
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+ | [[https:// | ||