Advance Python | Python for Datascience

Explore list comprehensions and generators in Python, learning how to replace multi-line loops with one-line expressions, filter results, and use yield for memory-efficient iteration.

Learn advanced python file handling, including creating, reading, updating, and deleting files with open modes (r, w, a, x) and text vs binary options.

Learn exception handling in Python for data science, using try and except to manage runtime errors like zero division and value errors, with else, finally, and optional error messages.

The course explores object oriented programming in Python for data science, explaining classes, objects, attributes, methods, and inheritance (single, multi-level, multiple) with examples.

Explore decorators and metaclasses in Python for data science, learning how decorators wrap functions to modify behavior and how metaclasses define class rules with type.

Learn to use numpy arrays for data science in Python, create one-dimensional, two-dimensional, and multi-dimensional arrays, and perform elementwise operations with zeros, ones, diagonal, arange, and linspace.

Explore indexing and slicing across one, two, and three dimensional arrays using numpy. Learn positive and negative indexing, access elements with a[b], and slice with start:stop:step.

Explore broadcasting and vectorization in numpy, performing element-wise operations on arrays of different shapes using broadcasting rules. Learn how vectorization replaces loops for faster, loop-less data science expressions.

Explore numpy's mathematical functions, including arithmetic, trigonometric, exponential, logarithmic, rounding, and broadcasting, alongside linear algebra topics like rank, determinant, trace, eigenvalues, and solving linear systems.

Explore array manipulation and reshaping with numpy, including accessing and modifying elements, slicing subsets, and appending, inserting, and deleting rows, then reshaping to one-dimensional, two-dimensional, and three-dimensional arrays.

Discover how pandas uses series and dataframes, customize indices, and build series from lists, tuples, or dictionaries, while appending rows or columns for data analysis tasks.

Learn to import datasets in csv, excel, and text formats with pandas, inspect data frames using head, tail, shape, and info, and transform by drop and insert operations.

Clean and preprocess data in pandas by handling missing values and outliers, using isnull, filling with mean/median/mode, dropping columns, and identifying outliers with the interquartile range.

Master joining, merging, and reshaping data with pandas. Learn inner, outer, left, and right joins, then merge data frames and reshape with pivot and melt.

Master advanced matplotlib techniques for data visualization with matplotlib and seaborn, including scatter plots, bar plots, histograms, 3D plots, and annotations.

Learn seaborn for data visualization by loading datasets such as tips and iris. Create joint plots, density plots, scatter plots, box plots, and heat maps to analyze correlations.

Explore interactive visualizations with Plotly by building scatter plots, density heat maps, pie charts, contour plots with scatter, and 3d plots on the Gibbs dataset.

Explore geospatial data analysis using Geopandas to read and inspect datasets, understand geometry types like polygons, and visualize provinces, districts, and palikas on world and Mapbox maps with Plotly.

Explore the data science workflow from data collection to modeling, and learn linear regression—a supervised learning algorithm—with scikit-learn to predict outcomes from features using train-test splits, coefficients, and intercept.

Explore logistic regression for classification using a Titanic dataset, handle missing values, create dummy variables with get_dummies, split data, fit, predict, and assess accuracy.

Explore supervised learning with support vector machines, decision trees, and random forests, using the iris data to classify three species via sklearn and hyperplane visualization.

Explore unsupervised learning to uncover patterns without labels using k-means clustering and the elbow method. Learn dimensionality reduction with PCA to reduce features for analysis.

Explore how to evaluate model performance using regression and classification validation techniques, including MAE, MSE, RMSE, R-squared, MAPE, and metrics from confusion matrices.

Learn hyperparameter tuning and model selection for classification using iris data, comparing svm, logistic regression, and other models, and applying grid search and cross-validation to find the best parameters.

Apply regression on a real house rent dataset, covering data import, cleaning, encoding, visualization, and model comparison using linear, random forest, and gradient boosting.

Explore heart disease prediction as a binary classification task in a data science workflow, from data cleaning and encoding to decision tree and random forest modeling, evaluation, and visualization.

Explore customer segmentation through clustering on a dataset, clean data and extract age from birth year, apply PCA for 3D visualization, and identify four clusters with k-means and agglomerative methods.