Practical Data Science using Python

Learn how histograms reveal the normal distribution across income and spending data, use class intervals and frequencies to build a standard unit curve, and perform normal approximation with 68–95–99.7 guidance.

Explore the central limit theorem by drawing samples from any population, showing the distribution of sample means tends toward normal, with implications for sampling, standard deviation, and hypothesis testing.

Explore probability with the addition rule for mutually exclusive events, and apply the binomial formula to compute exact outcomes. Learn about expected value and standard error across practical examples.

Learn hypothesis testing, including framing null and alternative hypotheses, calculating z-test statistics, standard error, and p-values to assess if observed differences reflect real population effects.

Launch Python via Anaconda and work in a Jupyter notebook, learning modular code cells and pandas data frames for data management, while comparing Jupyter with Spider and using OS commands.

Learn definite and indefinite iterations in python through while and for loops, processing lists, tuples, sets, dictionaries, and strings, with increment and decrement operators and key-based value access.

Explore Python lists and other collection types by learning creation, indexing, slicing, mutability, and common operations like append, extend, range operations, popping, and removing elements.

Learn how Python tuples provide immutable sequences, create nested structures from lists and dictionaries, and perform indexing, slicing, and common operations like enumerate, zip, any, all, max, and sum.

Explore Python dictionaries by using items, keys, and values to access data; learn pop item and setdefault behaviors, and iterate with for loops to print keys and values.

Explore how numpy handles numeric data as multi-dimensional arrays, create arrays with zeros, ones, full, identity, random values, arange sequences, and reshape, transpose, and dot products.

Explore pandas series and data frames by creating one-dimensional series from lists or dictionaries, manage indexes, handle missing data, and enable interoperation with numpy and common file formats.

Explore how the describe function computes statistics for numeric columns in a data frame, including count, mean, standard deviation, min, percentiles, and max, plus sorting, transposing, and conditional filtering.

Explore practical data frame manipulation in pandas, including adding records with series, updating cells, creating derived columns, resetting indices, and concatenating data frames in horizontal and vertical axes.

Use exploratory data analysis to uncover insights and prepare data for machine learning, applying univariate and multivariate methods, charts, and feature engineering on Lending Club data to identify default drivers.

Perform exploratory data analysis on the Lending Club dataset to identify features linked to defaults, and engineer a defaulted indicator from loan status, for clearer insights.

Engage in an eda project on loan status, using a bar plot for charged off, fully paid, and current loans, with a heatmap and null-value checks via info and isna.

Identify fields with high missing value percentages using a dataframe function, assess categorical feature distributions, and build income bins to explore default ratios with bar plots.

Bin funded amount into categorical ranges, combine with annual income categories, and visualize default ratios with seaborn to extract insights on loan performance and data relationships.

Analyzes how debt-to-income ratio, employment tenure, loan purpose, and interest rates influence loan defaults using box plots, bins, and cross-tab analyses to guide LendingClub decisions.

Explore how machine learning uses data to train algorithms that build models for predictions and decisions, including voice assistants, stock price forecasts, email filtering, recommendations, predictive maintenance, and reinforcement learning.

Trace the history of machine learning from early neural concepts and the Turing test to the GPU-driven era of deep learning, CNNs, AlexNet, and cloud GPUs.

Explore how machine learning solves problems traditional programming cannot, with spam filtering, image and voice processing, fraud detection, and core types: supervised, unsupervised, semi supervised, and reinforcement learning.

Explore linear regression and multivariate regression for predicting continuous outputs like house price, using gradient descent to minimize error and compare models with MSE, RMSE, MAE, RSS, TSS, and R-squared.

Explore linear regression with OLS assumptions, test residual normality and homoscedasticity, assess multicollinearity via VIF, and use adjusted R-squared, p-values, and feature elimination in a Python car price prediction project.

Identify linear relationships and multicollinearity in the car dataset using pair plots and correlation analysis, drop highly correlated features, and convert ordinal categoricals to dummy variables for stable linear regression.

Split the data into training and test sets, scale features, and build a linear regression model using ordinary least squares to predict car prices, then evaluate with R-squared and p-values.

explore logistic regression for churn prediction through feature engineering, binary to numeric conversion, one-hot encoding of nominal categories, train-test split, and standard scaling.

Explore building a logistic regression model with scikit-learn, noting L2 regularization and default intercept. Evaluate performance using a confusion matrix, accuracy, precision, recall, specificity, and roc auc.

Explore optimizing k-means clustering by assigning labels, inspecting cluster centers with pivot tables, and visualizing clusters for customer segmentation and promotions, using PCA for dimensionality reduction when needed.

Learn the naive Bayes classifier, a fast probabilistic method based on Bayes theorem and independence assumptions, with Gaussian and other variants, applied to spam filtering, sentiment analysis, and disease detection.

Explore how a decision tree turns data into a rule-based, tree-structured model by selecting the best feature and threshold via Gini impurity or entropy, from root to leaf with pruning.

Tune decision trees with hyperparameters such as min samples split, min samples leaf, max depth, and max features. Understand impurity measures like Gini index and entropy, with iris dataset example.

Tune decision tree hyperparameters with grid search cross-validation, optimizing max depth, min samples split/leaf, max features, and criterion (entropy or gini) on the iris dataset for high accuracy.