Practical Data Analysis and Visualization with Python

Learn to create arrays and matrices in Python using NumPy, from 1d arrays to 4x8 matrices, inspect shapes, build ranges, and reshape data for analysis.

Explore arithmetic operations on NumPy matrices using normally distributed random numbers, including reshaping, rounding, and updating matrices, plus min, max, mean, standard deviation, and transpose.

Learn slicing and filtering matrices in python, including retrieving rows and columns, using exclusive ranges, creating views versus copies, and applying boolean criteria like values over 11.

Explore dataframes in pandas, learn to create and update them from dictionaries or lists, and inspect with head and tail while handling columns, index, and historical data.

Learn how to update and merge dataframes by renaming columns in place, adding new columns, appending rows, and using concat to merge frames, then drop, sort, and manage indices.

Learn to read a public Google spreadsheet into a pandas data frame and prepare it for analysis of poll results, including url tweaks, indexing, and date parsing.

Learn to create line charts with matplotlib, plotting baby weight against age, customizing axes, legends, grids, and annotations, and compare boys and girls across single or side-by-side charts.

Explore how scatterplots reveal relationships between unemployment and interest rates, using pandas, matplotlib, and seaborn to plot Europe and United States data and overlay a linear regression line.

Create pie and donut charts in Python using a religions followers dataset, importing packages, reading data, customizing labels, adding percentages, exploding a slice, and adjusting visuals.

Analyze customer satisfaction using the net promoter score, classifying promoters and distractors, and explore age and gender effects on average ratings with scatter and bar plots.

Split data into training and testing sets to evaluate model accuracy. Use sklearn's train_test_split with test_size 0.15 to allocate 85% training data and 15% testing data, separating X and y (price).

Understand the logic of linear regression, ensure a linear relationship between independent and dependent variables, check for outliers and normalize when needed, and confirm predictor independence before predicting house prices.

Learn to build a linear regression model to predict Boston house prices, including data preprocessing, outlier handling, and evaluating with R-squared and residual analysis.

Explore multiclass classification with logistic regression using one-vs-rest and multinomial approaches. Build and evaluate models on the iris dataset, comparing L1 and L2 penalties, and interpret accuracy and confusion matrices.

Explore multi-class classification with k-nearest neighbor and radius neighbors, comparing uniform versus distance weighting, and validate models on the multiclass iris dataset.

Learn to build a Naive Bayes classifier from prior data, compute feature-based probabilities with long or short hair and female or male labels, and predict a new instance.