Analyst training

Lesson 1 • Critical Thinking and Bias Awareness

Identifies cognitive biases that distort analytical judgment and teaches mitigation strategies. Builds intellectual discipline essential for objective analysis.

Lesson 2 • What Analysts Do

Defines the analyst role, core responsibilities, and value delivered to organisations. Establishes context for all subsequent skill development in the course.

Lesson 3 • Logical Reasoning Fundamentals

Covers deductive, inductive, and abductive reasoning patterns used in analysis. Equips analysts to build and evaluate sound arguments from evidence.

Lesson 4 • Analytical Frameworks Overview

Introduces widely used strategic and operational frameworks as structured lenses for analysis. Provides a toolkit for approaching diverse business problems.

Lesson 1 • Survey and Interview Design

Teaches designing surveys and structured interviews to collect primary qualitative and quantitative data. Connects primary research skills to broader data collection strategy.

Lesson 2 • Data Sources and Research Methods

Maps internal and external data sources and explains how to evaluate source credibility. Enables analysts to build reliable evidence bases for their work.

Lesson 3 • Types and Structures of Data

Distinguishes quantitative, qualitative, structured, and unstructured data types. Grounds analysts in the vocabulary needed for all data work ahead.

Lesson 4 • Ethical and Compliant Data Handling

Covers data privacy principles, consent requirements, and responsible data use standards. Ensures analysts operate within ethical and regulatory boundaries.

Lesson 1 • Documenting the Data Preparation Process

Establishes practices for logging cleaning decisions to ensure reproducibility and auditability. Connects data preparation discipline to professional analytical standards.

Lesson 2 • Understanding Raw Data Problems

Catalogues common raw data issues including errors, inconsistencies, and formatting problems. Sets the stage for systematic cleaning workflows.

Lesson 3 • Data Integration and Merging

Explains joining datasets from multiple sources while preserving accuracy and avoiding duplication. Enables analysts to build comprehensive, unified datasets.

Lesson 4 • Handling Missing and Erroneous Data

Teaches strategies for imputing, removing, or flagging missing and erroneous values. Directly improves dataset integrity before analysis begins.

Lesson 1 • Spreadsheet Fundamentals for Analysts

Covers cell referencing, formula logic, and workbook organisation for analytical use. Establishes the spreadsheet foundation all subsequent tool skills build upon.

Lesson 2 • Core Analytical Functions

Teaches lookup, logical, statistical, and text functions essential for data manipulation. Directly accelerates data preparation and analysis workflows.

Lesson 3 • Automating Repetitive Analytical Tasks

Covers macro recording, basic scripting concepts, and workflow automation to reduce manual effort. Improves analyst productivity and reduces error in recurring tasks.

Lesson 4 • Introduction to Query Languages

Introduces SQL syntax for selecting, filtering, joining, and aggregating data from databases. Expands analysts' ability to access data beyond spreadsheet environments.

Lesson 1 • Probability and Inferential Basics

Introduces probability concepts, sampling distributions, and hypothesis testing fundamentals. Enables analysts to draw valid inferences from sample data.

Lesson 2 • Trend and Time Series Analysis

Covers decomposing time series into trend, seasonality, and noise components. Prepares analysts to identify patterns and forecast from historical data.

Lesson 3 • Correlation and Regression Analysis

Teaches measuring relationships between variables using correlation and linear regression. Equips analysts to model and explain variable interactions.

Lesson 4 • Segmentation and Comparative Analysis

Applies grouping and comparison techniques to identify differences across segments. Enables targeted insights for decision-making across business units.

Lesson 1 • Narrative Structure for Analysts

Teaches structuring analytical findings as a coherent story with situation, complication, and resolution. Bridges quantitative output to persuasive communication.

Lesson 2 • Principles of Effective Visualisation

Establishes core design principles—clarity, accuracy, and efficiency—for visual communication. Prevents common chart design mistakes that obscure insights.

Lesson 3 • Tailoring Communication to Audiences

Adapts analytical depth, language, and format to technical and non-technical stakeholders. Maximises the impact of findings across organisational levels.

Lesson 4 • Building Dashboards and Reports

Covers layout, hierarchy, and interactivity principles for dashboards and static reports. Connects visualisation skills to recurring analytical deliverables.

Lesson 1 • Distinguishing Insights from Observations

Defines the difference between raw observations, findings, and true insights with business implications. Sharpens the analyst's ability to add interpretive value.

Lesson 2 • Validating and Stress-Testing Conclusions

Applies sensitivity analysis and assumption testing to verify the robustness of analytical conclusions. Prevents overconfident recommendations based on fragile assumptions.

Lesson 3 • Structuring Recommendations

Covers frameworks for formulating clear, prioritised, and evidence-backed recommendations. Connects analytical conclusions directly to decision-maker needs.

Lesson 4 • Presenting Recommendations to Stakeholders

Structures and delivers recommendation presentations that drive stakeholder decisions and action. Integrates communication and analytical skills into a complete deliverable.

Lesson 1 • Network and Relationship Analysis

Introduces graph-based thinking to map relationships, dependencies, and influence flows. Expands analytical scope to interconnected systems and stakeholder networks.

Lesson 2 • Scenario and Simulation Analysis

Covers building multi-scenario models and Monte Carlo simulations to quantify uncertainty. Enables analysts to present decision options with explicit risk profiles.

Lesson 3 • Predictive Modelling Concepts

Introduces classification, regression, and forecasting models used to predict future outcomes. Prepares analysts to commission, interpret, and apply predictive outputs.

Lesson 4 • Optimisation and Decision Analysis

Applies linear programming and decision tree methods to resource allocation and choice problems. Equips analysts to support complex operational and strategic decisions.