Business and Data Analytics in Python

Explore data governance, data quality, dashboards, and predictive analytics to transform data into actionable insights, guiding data-informed decisions and revealing hidden trends.

Explore the art and science of business analytics, balancing intuition with data through descriptive and predictive analytics, using Python and real-world cases to inform decisions.

Explore how business analytics in python addresses big data, defined by volume, velocity, variety, veracity, and value, with thresholds, storage, processing, and data quality considerations.

Execute data science projects using CRISP-DM, starting with business understanding and problem framing, then data collection, ETL, preparation, quality checks, and align with SMEs for measurable outcomes.

Explore typical data sets in tabular form, define observations and variables, and learn to describe data with statistics and visualizations like bar charts and histograms.

Identify and classify data types by contrasting quantitative and qualitative variables, then distinguish discrete from continuous, and nominal from ordinal and interval from ratio with practical salary and temperature examples.

Explore difference between probabilities and statistics: probabilities predict future events with rules. Statistics analyzes past data to infer rules, as with an unfair coin and two consecutive flips at 52%.

Explore descriptive statistics hands-on in Google Colab, using numpy to define random variables and compute deviations, variance, and standard deviation, with pandas for subsequent frequency distribution.

Load a real data set with pandas and set the id column as the index. Compute descriptive statistics, quantiles, and frequencies, then convert counts to percentages with a lambda function.

Model Washington DC 311 service calls as a random variable, derive distributions and probabilities, and visualize with geolocation and descriptive statistics including skewness and kurtosis.

Apply cluster sampling by dividing the population into clusters, randomly selecting clusters, and sampling five elements from each chosen cluster.

Practice hands-on calculation and interpretation of confidence intervals using loan data, central limit theorem, z and t distributions, and adjusted world method for proportions.

Load a headless data set in pandas by setting header=None, then define semantic column names and, if needed, use the id column as the index.

Handle arff format data by fetching a zip from a url, extracting it, reading arff data with a specialized library, and loading the result into a pandas dataframe for analysis.

Practice reading json into pandas data frames, handle line-delimited json, fix id types, access location data, and fetch currency exchange rates via an API.

Learn how to read data from a relational database using SQLite in Python and load the results into a pandas dataframe. Explore reading configuration from property files and SQL queries.

Document data with clear project context, data sources, formats, and licensing details. Describe data collection protocols, access credentials, environment, consent, and governance to ensure reproducibility.

Explore data preprocessing tasks in data engineering, including handling missing values, outliers, normalization, standardization, feature extraction and selection, for numerical and categorical data within the CRISP-DM framework.

Explore feature engineering by applying feature extraction and feature selection to improve data quality, normalize data, handle missing values, encode categoricals, and derive date parts to reduce overfitting.

Identify and fix missing values using a Crisp-dm pre-processing approach, converting zeros to NaN to preserve data quality in the Pima Indians diabetes data set.

Identify outliers through visual methods such as histograms, scatter plots, and box plots, and understand their impact on models like linear regression, as they may signal emerging trends or anomalies.

Learn to transform numeric data with normalization and standardization to scale variables for machine learning and outlier detection, including min-max normalization, z-score standardization, and decimal scaling.

Apply z-score normalization by subtracting mean and dividing by the standard deviation to center data at zero and express values in standard deviation units; note decimal scaling as another transformation.

Explore decimal scaling normalization that expresses values as decimals by dividing by ten to number of digits, and compare original, min-max, z-score, and decimal scaling transformations in a pandas dataframe.

Apply the interquartile range method to a data frame to identify columnwise outliers, using Q1, Q3, and 1.5 IQR thresholds, then report and review candidates.

Apply equal frequency binning using quantile cut to create intervals with approximately equal frequency. Label bins with interval syntax and control precision to convert numeric data to categorical.

Explore turning a two-value categorical into a single binary variable using dummy variables. Learn to apply a drop-first trick to avoid multicollinearity in analytics.

Learn how oversampling addresses data imbalance by generating duplicate records with a non-majority strategy using the oversample function and a random over sampler, a key pre-processing step.

Explain how SSR, SSE, and SST relate to the regression line and data variability; introduce r-squared as the coefficient of determination; discuss typical value ranges across fields.

Explore how a simple linear regression estimates sugar content and ratings, using standard errors, t tests, and 95% confidence intervals to judge coefficient reliability and model quality.

Explore residuals in linear regression, verify homoscedasticity, independence, and normality, and apply tests such as the white test, breusch-pagan test, durbin-watson, ljung-box, shapiro-wilk, anderson-darling, and qq plots.

Use the f-statistic to test the significance of all coefficients in simple and multiple regression. Compare to the intercept-only model and interpret the p-value to reject null hypothesis.

Compare AIC and BIC to rank models by log likelihood and complexity, and apply multiple regression checks like linearity, normality, independence, homoscedasticity, with adjusted R-squared and F statistics.

Test residual independence and homoskedasticity by plotting residuals against predictions, using the Durbin-Watson result of 2.03 and the Breusch-Pagan test, then apply a log transformation if needed.

Build a milk production forecast in Google Colab using simple linear regression on monthly data, detrend, and apply month-based seasonal means in an additive model for future predictions.

Explore hands-on time series forecasting by building a one-year forecast using linear trend and seasonal indexes, then evaluate quality with MAD, MAPE, and MSE to compare models.

Explore time series analysis for stock prices in Python, examining predictability debates and applying techniques to make informed decisions even when data appears random.

Explore autocorrelation in time series, showing how current values relate to past ones. Learn about momentum, mean reversion, stationarity, non-stationary walks, and the limits of linear forecasting in stock prices.

Explore stationarity by creating a stationary time series, testing with the augmented Dickey-Fuller test, and examining autocorrelation plots, then apply differencing to a random walk and evaluate a naive forecast.

Explore how the simple moving average acts as a lagging signal from price crossovers and buy-sell signals. See how rolling calculations in pandas reveal signal timing with a 53-period window.

Explore the directional moving index (plus DMI, minus DMI, and ADX) to gauge trend direction and strength, using MACD crossovers for buy and sell signals when ADX exceeds 25.

Explore causal inference using the potential outcome framework, counterfactuals, and parallel trends, applying difference-in-differences and the Google Causal Impact method to compare treatment and control effects.

Identify the intervention date, set pre and post windows, select a control group via correlation with Tesla, and compare using difference-in-differences or the Google Causal Impact framework.

Explore the Google causal impact analysis framework, building counterfactuals with a Bayesian structural time series on pre- and post-treatment data to assess treatment effects while accounting for confounding and uncertainty.

Explore partitioning k-means clustering to form homogeneous groups by minimizing within-cluster variability and maximizing between-cluster differences, using centroids, distances, and Lloyd's algorithm in an unsupervised setting.

Explore how the k medoids variation uses real data points as cluster centers, replacing centroids to improve interpretability, and learn the criterion that minimizes within-cluster variability.

Compare k medoids to k-means by selecting medoids from the data set to improve explainability, and learn how to install scikit learn extra to run the medoids method.

Explore k-means on spherical data with unequal variance and varying densities to see how density influences centroid allocation and spillover, and how seeds affect stability.

Learn how gaussian mixture models overcome k-means limitations by using multimodal data to segment customers with multiple normal distributions, via an expectation maximization approach and flexible covariance structures.

Apply a gaussian mixture model to segment bank customers, select five components based on AIC/BIC values, profile segments with descriptive statistics, and craft business-focused recommendations for leadership.