The Data Literacy Course: Learn How to Work With Data

Explore big data and its three defining dimensions: volume, variety, and velocity, and the challenges of real-time processing across diverse sources like ERP, CRM, and websites or tweets.

Explore the storage systems data consumers rely on, including databases, data warehouses, and data lakes, and distinguish traditional data systems from big data systems as prep for upcoming lessons.

Master how relational databases organize data in linked tables of rows and columns, use keys to relate records, and employ sql to query, manipulate, and define data.

Learn how a data warehouse serves as an integrated, query-focused repository for large historical data, enabling reporting, BI-driven analysis, and structured data exploration across the business.

Data lakes store data in its native format at scale, handling structured, semi-structured, and unstructured data for exploration, and require maintenance to avoid data swamps alongside data warehouses.

Explore fog computing, also known as edge computing, where IoT devices process data on the edge to reduce latency, enable offline operation, and protect privacy through on-device analytics.

Discover how graph databases store connections with nodes and edges and enable fast queries for social networks, recommendations, and fraud detection, illustrated by the Panama Papers case.

Discover how business intelligence (BI) analyzes data to provide historical, current, and predictive insights for operations and decisions, through reporting, dashboarding, OLAP, and FP&A.

Explore supervised learning by training models on labeled data, using training and validation sets to learn input-output mappings, and applying regression, time series forecasting, and classification techniques.

Explore how time series forecasting uses past values to predict future data, employing time series regression with external predictors to capture trends, seasonality, and demand patterns.

Unsupervised learning analyzes unlabeled data to identify structure, extract useful features, and organize data, using clustering and association to reveal patterns without supervision.

Explore clustering as an unsupervised machine learning technique that groups unlabeled data into clusters by similarity or distance, revealing business-relevant segments and applications like sorting emails, documents, or products.

Learn how the correlation coefficient quantifies relationships in scatter plots, from minus one to plus one, including Pearson and Spearman types, and interpret strengths as weak, moderate, or strong.

Understand that correlation shows a statistical relationship, not causation, using a 0.85 example between daylight and temperature. Explore direct, reverse, and bidirectional causality and the role of a third factor.

Assess the goodness of fit in simple linear regression using R-squared, the coefficient of determination, and the proportion of variance explained.

Interpret the p-value as the probability results arise by chance, guiding rejection of the null hypothesis. See how sample data, test statistics, and significance levels reveal real differences.

Learn how classification uses labeled data to predict yes or no outcomes. Evaluate models with out-of-sample validation, and interpret confusion matrices and evaluation metrics.