Data Analytics Course

Lesson 1 • Data Sources and Structures

Surveys structured, semi-structured, and unstructured data sources. Prepares students to identify appropriate data for any analytical task.

Lesson 2 • What Data Analytics Is

Defines data analytics and its scope within modern organisations. Establishes vocabulary used throughout the course.

Lesson 3 • Ethics and Governance in Analytics

Covers data privacy principles, bias risks, and responsible use standards. Grounds all subsequent technical work in ethical practice.

Lesson 4 • Types of Analytics

Contrasts descriptive, diagnostic, predictive, and prescriptive analytics. Connects each type to real business decisions.

Lesson 1 • Data Pipeline Basics

Explains ETL and ELT processes and pipeline orchestration concepts. Gives students a mental model for automated data movement.

Lesson 2 • Data Collection Methods

Examines surveys, web scraping, transactional systems, and sensor data. Connects collection method choice to data quality outcomes.

Lesson 3 • Introduction to SQL

Teaches core SQL syntax for querying and filtering data. Directly supports data extraction tasks in all subsequent chapters.

Lesson 4 • Relational Database Fundamentals

Introduces tables, keys, and relationships in relational databases. Enables students to read and design basic schemas.

Lesson 1 • Understanding Data Quality

Defines accuracy, completeness, consistency, and timeliness as quality dimensions. Establishes the standard against which all cleaning work is measured.

Lesson 2 • Handling Missing and Duplicate Data

Covers detection and treatment strategies for nulls, blanks, and duplicate records. Directly reduces bias and error in downstream analysis.

Lesson 3 • Outlier Detection and Treatment

Introduces statistical and visual methods for identifying outliers. Teaches context-driven decisions about retaining or removing extreme values.

Lesson 4 • Feature Engineering Fundamentals

Covers creating new variables from existing data to improve analytical power. Bridges data preparation and modelling chapters.

Lesson 1 • Hypothesis Formulation

Teaches how to translate business questions into testable hypotheses from EDA findings. Connects exploratory work to confirmatory analysis.

Lesson 2 • Univariate Analysis

Examines single-variable distributions using histograms, box plots, and frequency tables. Builds pattern-recognition skills applied to multivariate analysis next.

Lesson 3 • Bivariate and Multivariate Analysis

Explores relationships between two or more variables using scatter plots, heatmaps, and cross-tabulations. Reveals correlations and interactions in the data.

Lesson 4 • Descriptive Statistics

Covers measures of central tendency, spread, and shape for summarising distributions. Provides the numerical foundation for all EDA work.

Lesson 1 • Correlation and Regression Analysis

Introduces Pearson correlation and simple linear regression for modelling relationships. Bridges statistical inference and predictive modelling.

Lesson 2 • Hypothesis Testing

Teaches t-tests, chi-square tests, and ANOVA for comparing groups. Connects statistical decisions to business questions introduced in EDA.

Lesson 3 • Multiple Regression and Model Validation

Extends regression to multiple predictors and covers overfitting and validation strategies. Prepares students for machine learning concepts ahead.

Lesson 4 • Probability Fundamentals

Covers probability rules, distributions, and the central limit theorem. Provides the mathematical foundation for all inferential techniques.

Lesson 1 • Data Storytelling Techniques

Applies narrative structure, context, and tension to data presentations. Transforms static charts into persuasive analytical stories.

Lesson 2 • Principles of Visual Design

Covers pre-attentive attributes, Gestalt principles, and chart selection frameworks. Establishes design rules applied in every subsequent visualisation.

Lesson 3 • Core Chart Types and Use Cases

Builds proficiency with bar, line, scatter, pie, and map charts. Connects each chart type to the analytical question it best answers.

Lesson 4 • Dashboard Design

Teaches layout, hierarchy, and interactivity principles for multi-chart dashboards. Prepares students to build operational and executive dashboards.

Lesson 1 • Machine Learning Concepts

Defines supervised, unsupervised, and reinforcement learning with business examples. Establishes the conceptual map for all modelling work in this chapter.

Lesson 2 • Regression and Forecasting Models

Extends regression to polynomial and regularized forms and introduces time-series forecasting. Connects predictive modelling to operational planning.

Lesson 3 • Clustering and Segmentation

Applies k-means and hierarchical clustering to segment customers and data points. Enables unsupervised pattern discovery without labelled data.

Lesson 4 • Model Selection and Deployment Basics

Covers cross-validation, hyperparameter tuning, and model deployment concepts. Prepares students to move models from notebooks to production.

Lesson 1 • Analytics Roadmap Development

Guides students through building a phased analytics roadmap with milestones and resource plans. Synthesises all course learning into a strategic deliverable.

Lesson 2 • Analytics Governance and Data Quality

Establishes data governance structures, stewardship roles, and quality standards at scale. Ensures analytics outputs are trustworthy and compliant.

Lesson 3 • Measuring Analytics ROI

Covers frameworks for quantifying the financial and operational impact of analytics projects. Builds the business case for continued analytics investment.

Lesson 4 • Building a Data-Driven Culture

Addresses change management, data literacy programmes, and executive sponsorship. Connects individual analytical skills to organisational transformation.