Advanced Topics

This module introduces two areas where specialized techniques significantly outperform the general-purpose methods covered in earlier modules: time series analysis and neural networks.

Time series data (process measurements, environmental monitoring, sensor streams) violate the independence assumption that underlies most regression and classification methods. Topics 6.1 and 6.2 cover the statistical properties of time series, methods for handling non-stationarity and seasonality, and classical forecasting models (AR and ARIMA).

Neural networks learn hierarchical feature representations directly from data, overcoming the limitations of hand-crafted feature engineering for complex, high-dimensional inputs. Topic 6.3 builds intuition from first principles — a single neuron, activation functions, backpropagation — and demonstrates the MLPRegressor on familiar datasets. Topic 6.4 surveys three widely-used architectures (CNN, LSTM, autoencoder) through minimal PyTorch implementations, connecting each to earlier material: CNNs to LDA/PCA projections, LSTMs to ARIMA forecasting, and autoencoders to the generative models of Module 5.

The overarching theme is that architecture is structured feature engineering: every design choice encodes an assumption about what structure in the data is worth exploiting. Making those choices thoughtfully — and knowing when simpler classical methods suffice — is a core data analytics skill.