The complete Data Analysis & Automation Bootcamp

The data lifecycle, variable types (categorical vs numerical) and levels of measurement (nominal, ordinal, interval, ratio).

Range, variance, standard deviation, IQR, skewness & kurtosis, and measures of central tendency (mean, weighted mean, median, mode).

The normal distribution, Z-scores, and detecting and handling outliers.

Confidence intervals, null vs alternative hypotheses, the p-value, and designing and analysing real A/B tests for marketing and growth.

Advanced text functions (TRIM, CLEAN, PROPER, TEXTBEFORE, TEXTAFTER, TEXTSPLIT, SUBSTITUTE) and cleaning up chaotic customer databases.

XLOOKUP and INDEX/MATCH, nested logical functions (IFS, SWITCH, AND, OR), and the FILTER, UNIQUE and SORT arrays.

Importing from multiple sources, unpivoting, fixing errors, and merging files automatically with one-click refresh.

Professional pivot tables, calculated fields, large datasets, Power Pivot and relationships.

Slicers & timelines, dynamic conditional formatting, and financial and operational reports ready for senior management.

RDBMS concepts, ERDs, primary and foreign keys, SELECT queries and filtering with WHERE, IN, LIKE, BETWEEN.

GROUP BY and HAVING, COUNT, SUM, AVG, MIN, MAX, and handling dates and null values.

INNER, LEFT, RIGHT, FULL OUTER, CROSS and SELF-JOIN, combining results with UNION / UNION ALL, and conditional logic with CASE WHEN.

Subqueries in SELECT and WHERE, and building views and CTEs to simplify and speed up heavy queries.

ROW_NUMBER, RANK and DENSE_RANK for ranking, plus LEAD and LAG for growth calculations and running totals.

Desktop, Service and Mobile, connecting to Excel and SQL sources, and cleaning data in the built-in Power Query Editor.

Star schema design, fact and dimension tables, cross-filter direction, and avoiding broken relationships.

Measures vs calculated columns, mastering CALCULATE, and the FILTER, ALL, ALLEXCEPT and RELATED functions.

YoY and MoM comparisons, YTD and MTD calculations, and building dynamic date tables.

Professional formatting, bookmarks, interactive navigation buttons, drill-through, and a polished user experience.

Publishing reports, building dashboards, setting up scheduled refresh, and sharing with departments.

Jupyter Notebook and Google Colab, data types, variables, lists, dictionaries, conditionals and functions.

One- and multi-dimensional arrays, vectorised operations, and fast mathematical and statistical computation.

Series and DataFrames, reading and exporting CSV/Excel/SQL files, and filtering and indexing with loc, iloc and query.

Handling missing values (imputation), removing duplicates, working with dates and text, and applying apply and lambda.

Merge, join and concat, advanced GroupBy operations, and building pivot tables programmatically.

Matplotlib and Seaborn — heatmaps, pairplots, boxplots and distribution plots for business reporting.

Summarising statistical properties, uncovering hidden correlations (correlation matrix), and analysing distributions in code.

Building low-code data pipelines and connecting webhooks and APIs.

Pulling data from web forms and feeding databases and Excel sheets with no manual work.

Sending daily reports and alerts automatically to email, WhatsApp and Slack.

Writing complex SQL queries, reviewing and optimising Python code, debugging, and multiplying your productivity up to 5×.

Extract and organise with SQL, clean and analyse with Python, model and present with Power BI, and automate with n8n — then defend it in front of the instructor panel.

Building your portfolio on GitHub and preparing your CV for the job market.