Intro to Data Science Using Python: Your Best Starting Point

Learn how data analysis unlocks insights from raw data to answer questions and predict outcomes, using a car price scenario to explore features like color, brand, and horsepower.

Explore core data science libraries for analysis, visualization, and machine learning in Python, including data frames, arrays, and statistical modeling tools with regression, classification, and clustering.

Learn how to import data from csv files into a data frame using read_csv, handle headers, view with head and tail, and export the data frame back to csv.

learn to read any data using the read_csv function from the bison package, load data into a notebook, inspect with head and tail, assign column headers, and export to csv.

Pre-processing data in Python covers cleaning and data wrangling, including handling missing values, harmonizing formats across sources, normalizing with centering and scaling, and converting categorical values to numeric for modeling.

Learn to handle missing values in data with drop and replace strategies, using mean or mode imputation while assessing data quality.

Standardize diverse data formats and units to enable clean comparisons, convert mpg to liters per 100 kilometers, and ensure correct data types for reliable analysis.

Normalize data to bring feature ranges to a common scale, enabling fair comparisons and easier analysis in Python, supporting linear regression, using feature scaling, min max, and standard score.

binning in python groups numerical values into bins to simplify data and improve predictive model accuracy. use cut to create equal-width bins and visualize distribution across low, medium, and high.

transform the categorical fuel type into numeric inputs by applying one-hot encoding with get dummies, creating gas and diesel features for model training.

Learn the basics of exploratory data analysis to summarize data and uncover relationships, using descriptive statistics, grouping data with groupby, ANOVA, and correlation heat maps to analyze car price drivers.

Learn how descriptive statistics summarize data with numerical measures and categorical counts. Use box plots to visualize distributions and scatter plots to explore relationships like engine size versus price.

Group by drive wheels and body style to compare vehicle prices, compute mean prices for each category, then visualize with a pivot table and a heat map.

Explore how correlation measures interdependence between variables, illustrated with engine size versus price and mpg versus price, including positive, negative, and weak relationships, while noting correlation does not imply causation.

Explain how to measure correlation with the correlation coefficient and p value, interpret strong, negative, or zero associations in linear relationships, using a correlation heat map as an example.

Analyze how a categorical variable, such as car make, affects price using one-way anova to compare group means and assess significance with the f statistic and p-value.

Develop models to predict car prices using simple linear, multiple linear, and polynomial regression; evaluate with mean squared error, and learn how independent variables like highway mpg shape the price.

Learn simple and multiple linear regression to relate predictors to a dependent variable, fit intercept and slope parameters from training data, and make predictions with a trained model.

Learn to evaluate models with visualization by analyzing residuals, linearity, and distribution, using seaborn plots to compare predicted and actual values and assess variance.

Use polynomial regression to fit curves by transforming predictors into polynomial features and applying linear regression. Build and use pipelines that standardize features, create higher-order terms, and produce predictions.

Evaluate model performance numerically with mean squared error and r squared, the coefficient of determination, showing how closely the regression line fits data and overfitting risks.

Evaluate predictions and decisions by visualizing results and using numerical measures such as mean square error and R squared. Identify linear versus non-linear trends and interpret coefficients.

Learn to evaluate and refine predictive models using training and testing splits, cross-validation with k folds, and measures of generalization error to predict unseen data.

Explore how polynomial order affects mean square error on training and test sets, distinguishing underfitting, overfitting, irreducible error, and using r squared to choose the optimal model.

Apply ridge regression to shrink coefficients and curb overfitting by tuning the alpha parameter, using training and validation data with cross-validation to maximize r squared.