Core Topics

• Structured tables, named ranges, and data validation
• Advanced formulas: XLOOKUP, INDEX-MATCH, SUMIFS, COUNTIFS, IFERROR
• Pivot Tables: grouping, filtering, calculated fields, and slicers
• Pivot Charts: bar, line, pie — when to use each and formatting
• Power Query: importing data from CSV/web, basic transformations, append/merge
• Conditional formatting: data bars, colour scales, icon sets for dashboards
• Excel dashboards: combining charts, slicers, and KPI cards

Tools & Tech

Learning Outcomes

• Build an Excel dashboard with Pivot Tables, Power Query-cleaned data, dynamic charts, and slicers — without writing a single macro.

Core Topics

• Descriptive statistics: mean, median, mode, variance, standard deviation
• Distributions: normal, binomial, Poisson — reading and interpreting them
• Correlation vs causation: Pearson correlation, Spearman rank correlation
• Hypothesis testing: null/alternate hypothesis, p-values, significance levels
• T-tests, chi-square tests, and when to use them
• Sampling methods and sampling bias — why it matters for business data
• Practical examples: A/B testing interpretation, survey data analysis

Tools & Tech

Learning Outcomes

• Apply descriptive statistics, correlation analysis, and basic hypothesis tests to real datasets and interpret results correctly for business decision-making.

Core Topics

• SQL fundamentals: SELECT, WHERE, ORDER BY, LIMIT, DISTINCT
• Aggregation: GROUP BY, HAVING, COUNT, SUM, AVG, MIN, MAX
• Joins: INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL OUTER JOIN with real datasets
• Subqueries and Common Table Expressions (CTEs) for readable complex queries
• Window functions: ROW_NUMBER, RANK, LAG, LEAD, PARTITION BY
• Date and string manipulation functions for time-series and text data
• Query optimisation basics: EXPLAIN, indexes, and avoiding full table scans

Tools & Tech

Learning Outcomes

• Write complex analytical SQL queries using CTEs, window functions, and multi-table joins to extract business metrics from a relational database.

Core Topics

• Python setup: Jupyter Notebooks, virtual environments, pip
• numpy: arrays, broadcasting, vectorised operations, random number generation
• pandas Series and DataFrame: creation, indexing, slicing, boolean masking
• Reading data: CSV, Excel, JSON, SQL with pandas
• DataFrame operations: merge, groupby, pivot_table, apply, lambda functions
• Time series analysis: pd.to_datetime, resampling, rolling windows
• Handling large datasets: chunking, memory optimisation with dtypes

Tools & Tech

Learning Outcomes

• Load, transform, and aggregate a multi-column real-world dataset using pandas — producing summary statistics and derived metrics without using Excel.

Core Topics

• Identifying data quality issues: missing values, outliers, duplicates, type errors
• Imputation strategies: mean/median fill, forward fill, drop — when to use each
• Outlier detection: IQR method, Z-score, visualisation approaches
• EDA workflow: univariate → bivariate → multivariate analysis
• Visualisations for EDA: histograms, box plots, scatter plots, correlation heatmaps
• Feature engineering basics: binning, encoding categoricals, creating derived columns
• EDA report: documenting findings and anomalies for stakeholder communication

Tools & Tech

Learning Outcomes

• Perform a complete EDA on a raw dataset — cleaning it, detecting outliers, and producing a structured report of key distributions and relationships.

Core Topics

• matplotlib figure anatomy: Figure, Axes, subplots, twin axes
• Line charts, bar charts, scatter plots, pie charts, histograms in matplotlib
• seaborn high-level API: heatmap, pairplot, violin plot, boxplot, FacetGrid
• Chart design principles: chart junk, data-ink ratio, colour accessibility
• Plotly: interactive charts for web embedding and executive dashboards
• Saving high-resolution charts for reports and presentations

Tools & Tech

Learning Outcomes

• Create publication-quality charts in matplotlib and seaborn following data visualisation best practices, and one interactive Plotly chart for web use.

Core Topics

• Power BI Desktop interface: data view, model view, report view
• Importing data: CSV, Excel, SQL Server, web connectors
• Power Query Editor: transforming raw data before loading
• Data modelling: star schema, relationships, cardinality in Power BI
• DAX basics: calculated columns, measures, CALCULATE, FILTER, time intelligence
• Visualisations: bar, line, donut, map, card, slicer, and custom visuals
• Publishing to Power BI Service, sharing dashboards, and scheduling refreshes

Tools & Tech

Learning Outcomes

• Build and publish an interactive Power BI dashboard with cross-filtering slicers, DAX-calculated KPIs, and a data model connected to a real data source.

Core Topics

• Data storytelling framework: situation → complication → resolution
• Choosing the right chart for the insight you are communicating
• Executive summary design: KPIs, trend lines, and narrative annotations
• Capstone project: full pipeline from raw CSV to Power BI published dashboard
• Presenting data findings: structure, pacing, anticipating questions
• Resume and LinkedIn profile update for data analyst roles
• What hiring managers look for in a data analyst portfolio

Tools & Tech

Learning Outcomes

• Complete and present a full-pipeline data analysis capstone: data cleaning in Python, SQL analysis, and a Power BI dashboard published and shared with mentor.