Week 1 – Course introduction; the role of data visualization in analytics; overview of tools (Excel, Power BI, Tableau)
Week 2 – Data fundamentals: data types, structured vs. unstructured data, sources, and data quality
Week 3 – Principles of visual perception and design; pre-attentive attributes, color theory, and accessibility
Week 4 – Choosing the right chart: comparison, distribution, composition, and relationship visuals
Week 5 – Data preparation and cleaning; shaping and transforming data for analysis
Week 6 – Introduction to Power BI: importing data, the data model, and building basic reports
Week 7 – Working with measures and calculated fields; aggregations and KPIs
Week 8 – Midterm review and exam; portfolio checkpoint
Week 9 – Interactive dashboards: filters, slicers, drill-downs, and user experience design
Week 10 – Exploratory data analysis: identifying trends, outliers, and patterns visually
Week 11 – Statistical visuals: distributions, correlation, scatter plots, and trend lines
Week 12 – Time-series analysis and forecasting visuals
Week 13 – Geographic and spatial visualization; maps and location-based analytics
Week 14 – Data storytelling: structuring a narrative, audience considerations, and presentation best practices
Week 15 – Ethics in data visualization: misleading visuals, bias, and responsible reporting; final project workshop
Week 16 – Final project presentations and course wrap-up