Master the essentials of data types, forecasting methods, and data-driven decision making through interactive exercises
Learn to distinguish between Time-series, Cross-sectional, and Panel data by examining real-world examples and understanding their characteristics.
Data collected on one unit at multiple points in time. Shows how variables change over time.
Data collected at a single point in time across different units or entities.
Combines time-series and cross-sectional data. Same units observed over multiple time periods.
Drag each dataset to the correct category:
Revenue data collected every month for Walmart stores over a 4-year period, showing seasonal trends and growth patterns.
Final exam scores for all students enrolled in the current semester of DATA4400, collected on the same day.
Annual sales data for Apple, Samsung, and Google smartphones tracked over multiple years, showing market share evolution.
Cryptocurrency price recorded every hour for the past 30 days, showing volatility and trading patterns.
Annual revenue data for all 200 companies listed in the ASX 200 index as of December 2024.
Annual salary data tracked for Marketing, IT, and Finance departments across multiple years.
One unit, multiple time points
Multiple units, single time point
Multiple units, multiple time points
Understand when to use qualitative vs quantitative forecasting methods based on data availability and business context.
Based on expert opinions, market research, and subjective analysis. Used when historical data is limited.
Based on historical numerical data and statistical models. Used when sufficient historical data exists.
Learn to distinguish between evidence-based and intuition-based decision making approaches in business contexts.
Decisions based on quantitative evidence, statistical analysis, and objective data interpretation.
Decisions based on intuition, experience, gut feeling, or subjective judgment without statistical backing.