Basic Data Analytics Course

Lesson 1 • What Data Analytics Actually Is

Defines analytics, distinguishes it from reporting and data science, and frames its business value. Establishes shared vocabulary used throughout the course.

Lesson 2 • The Four Analytics Categories

Introduces descriptive, diagnostic, predictive, and prescriptive analytics with real examples. Helps students select the right category for a given business question.

Lesson 3 • The Analytics Workflow

Maps the end-to-end process from problem definition to insight delivery. Provides a repeatable framework students apply in every subsequent chapter.

Lesson 1 • Methods of Data Collection

Surveys, web scraping, APIs, sensors, and manual entry are compared for reliability and cost. Students match collection methods to project constraints.

Lesson 2 • Introduction to SQL Querying

Teaches SELECT, WHERE, GROUP BY, and JOIN statements to extract and aggregate data. Students write queries against sample datasets by section end.

Lesson 3 • Non-Relational and Cloud Storage Options

Introduces document, key-value, and columnar stores alongside cloud data warehouses. Helps students recognize when relational databases are insufficient.

Lesson 1 • Data Transformation Techniques

Teaches normalization, encoding, binning, and date parsing to reshape raw data. Transformed features feed directly into the analysis chapters that follow.

Lesson 2 • Handling Missing and Erroneous Data

Covers deletion, imputation, and flagging strategies for missing values and outliers. Students apply each strategy and evaluate trade-offs for their dataset.

Lesson 3 • Assessing Data Quality

Defines completeness, accuracy, consistency, and timeliness as quality dimensions. Students audit a dataset and document its quality issues systematically.

Lesson 1 • Bivariate and Multivariate Analysis

Scatter plots, correlation matrices, and grouped charts expose relationships between variables. Students identify candidate predictors for later modeling chapters.

Lesson 2 • Univariate Visual Exploration

Histograms, box plots, and bar charts reveal the distribution of individual variables. Students select the appropriate chart type based on data type and question.

Lesson 3 • Descriptive Statistics in Practice

Covers mean, median, mode, variance, standard deviation, and percentiles with business context. Students compute and interpret these measures on sample data.

Lesson 1 • Choosing the Right Chart Type

Maps analytical goals—comparison, composition, distribution, relationship—to chart families. Students build a chart-selection decision tree for their reference.

Lesson 2 • Crafting a Data Narrative

Applies story structure—situation, complication, resolution—to data presentations. Students produce a slide deck that guides an audience from insight to action.

Lesson 3 • Building Interactive Dashboards

Teaches layout, filters, and drill-down design for dashboards intended for business users. Students assemble a multi-panel dashboard from a provided dataset.

Lesson 1 • Common Statistical Tests

Covers t-tests, chi-square tests, and ANOVA for comparing groups and testing associations. Students select and run the correct test for each provided scenario.

Lesson 2 • Hypothesis Testing Framework

Introduces null and alternative hypotheses, p-values, significance levels, and error types. Students apply the framework to a structured business decision scenario.

Lesson 3 • Confidence Intervals

Explains how confidence intervals quantify estimation uncertainty for means and proportions. Students construct and correctly interpret intervals from sample data.

Lesson 4 • Practical Significance vs. Statistical Significance

Distinguishes effect size from p-value significance to prevent misleading conclusions. Students calculate effect sizes and recommend business actions accordingly.

Lesson 1 • Model Evaluation Metrics

Introduces RMSE, MAE, accuracy, precision, recall, F1, and ROC-AUC for model assessment. Students select metrics aligned with the cost of each error type.

Lesson 2 • Cross-Validation and Model Selection

Teaches k-fold cross-validation and hyperparameter tuning to produce reliable model estimates. Students compare two models and justify their final selection.

Lesson 3 • Logistic Regression for Classification

Extends regression to binary outcomes using log-odds and probability thresholds. Students build a classifier and interpret its coefficients in business terms.

Lesson 4 • Linear Regression

Covers simple and multiple linear regression, coefficient interpretation, and assumptions. Students fit a regression model and diagnose assumption violations.

Lesson 1 • Data Acquisition and Cleaning

Students apply collection, storage, and cleaning skills from earlier chapters to their chosen dataset. Documented cleaning decisions form part of the final deliverable.

Lesson 2 • Modeling or Advanced Descriptive Analysis

Students build a predictive model or advanced descriptive analysis appropriate to their question. Model or analysis outputs feed directly into the final presentation.

Lesson 3 • Scoping and Planning the Project

Guides students to define a business question, identify data sources, and create a project plan. A clear scope prevents scope creep and keeps analysis focused.

Lesson 4 • Presenting Findings to Stakeholders

Students deliver a structured presentation combining dashboard, narrative, and recommendations. Peer and instructor feedback simulates a real stakeholder review.