This course is a brief introduction to the use of Python applied to data science.
| Session | Date | Chapter | Topics | Evaluation |
|---|---|---|---|---|
| 1 | 28/08/2023 | Introduction | Presentation of the course, introduction to artificial intelligence, introduction to handling python (programming environment, variables, types of variables, operators, conditional executions, iterations) | |
| 2 | 4/09/2023 | Python | Lists, dictionaries, tuples, object-oriented programming | |
| 3 | 11/09/2023 | Numerical computing | numpy, arrays, array computing, aggregations, indexing, broadcasting, object-oriented programming | |
| 4 | 18/09/2023 | Data manipulation with pandas | Introduction to DataFrame, indexing, operations, missing data handling, hierarchical indexing, grouping, aggregation, pivot tables, line plots, scatter plots, error display, histograms, subplots | |
| 5 | 25/09/2023 | Visualization | Line plots, scatter plots, error visualization, histograms, subplots, customization, 3D figures, visualization with seaborn | |
| 6 | 02/10/2023 | Time series | time stamps vs periods, indexing, frequency conversion, autoregression, moving average, ARIMA model | |
| 7 | * | Machine learning I | Overview of machine learning in finance, types of machine learning, overfitting vs underfitting, cross validation, metrics for model evaluation, model selection. | |
| 8 | * | Machine Learning II | Linear Regression, Regularized Regression, k-nearest neighbors, support vector machines (regression), combined learning methods (boosting and bagging) | |
| 9 | * | Regression Applications in Finance | Stock Price Prediction, Risk Tolerance Prediction, Yield Curve Prediction |