Math for Data Science Proficiency (Advanced Level)

Topics covered

• apply Poisson distributions to make estimates in real-life situations
• apply the ANOVA test for multiple groups
• build a logistic regression model using principal components
• calculate skewness and kurtosis on real data
• calculate the Euclidean and Manhattan distance between two points using SciPy as well as your own function
• code the steps to apply gradient descent to find the optimum hyperplane
• compute a penalty for a large number of latent factors when computing the factors of a ratings matrix
• compute eigenvalues and eigenvectors
• compute the predicted ratings given by users for various items by using matrix decomposition
• compute the shortest path and a minimum spanning tree for a graph
• create naive Bayes models in Python
• create probability tables for a Bayesian network
• describe how unstable gradients can be mitigated using variants of the ReLU activation function
• estimate a population's mean with confidence intervals
• explore and perform stochastic gradient descent
• illustrate the use of decision trees for continuous values
• implement regression models using libraries
• outline how to create balanced samples from an imbalanced dataset
• use two-way ANOVA with interaction between the independent variables
• work through a calculation of an epoch