Big Data Analytics: Online Course with Internship Agreement
Big Data Analytics teaches you to work through the full data lifecycle. You learn to reason in SQL and data models, choose the right chart, and understand warehouses, lakes and lakehouses, ETL/ELT pipelines, text-to-SQL copilots and streaming. You also apply GDPR and the EU AI Act. It is aimed at aspiring junior analysts. The course runs 100 % online for 180 days, includes an internship agreement and costs €200.
- 180 days of access
- 100 % online, at your own pace
- Level: beginner · No prior experience required
- Assessment: 10 quizzes (50 questions) and a final project
- Internship agreement included
- Certificate with a verifiable QR code
- Languages: Spanish, English, French
- Price: €200
Who it is for
- People aiming for a junior data analyst or graduate analyst role
- Business profiles who want to turn company data into decisions
- Professionals in finance, insurance or retail who work with data and reports
- Analysts who need to understand GDPR and EU AI Act obligations around data and models
What do you need to start?
Prior knowledge
- No prior knowledge of data analytics or programming is needed
- Comfort reading a spreadsheet with rows and columns
- Willingness to reason step by step about business questions
Software and equipment
- A computer with an internet connection
- No specific software licence is required to start
What you'll be able to do
- Classify a business question as descriptive, diagnostic, predictive or prescriptive and place it in the data lifecycle
- Reason through SQL queries to aggregate, filter and compare transaction data across tables
- Choose the right chart for a business question and explain why a pie chart would perform worse
- Compare data warehouses, data lakes and lakehouses and judge which fits a given need
- Trace a mismatched figure back through ETL/ELT steps using data quality checks and lineage
- Assess when a text-to-SQL copilot's answer can be trusted and when it needs verification
- Check whether a dataset is truly anonymous under GDPR before sharing it externally
- Determine whether a scoring model falls under EU AI Act Annex III and what that requires
Skills you will practise
- Data lifecycle
- SQL
- Data modelling
- Python
- Data visualisation
- Data warehouses and lakehouses
- ETL/ELT
- Data quality and lineage
- Text-to-SQL
- GDPR and EU AI Act
Syllabus
- Foundations of Data Analytics and the Data Lifecycle — Types of analytical questions and the stages data passes through, from deciding it is worth analysing to acting on findings.
- SQL and Data Modeling — How to reason about queries and table structures to answer questions such as totals by group or highest average values.
- Python and Data Visualization: From the Table to the Chart That Decides — Choosing chart types for business questions, avoiding visual clutter, and using Python to automate repetitive cleaning and transformation.
- Modern Data Architectures: Warehouses, Lakes and Lakehouses — Where company data lives and how architecture choices affect query speed, data trustworthiness and how easily analysts find data.
- Data Engineering Basics: ETL/ELT, Quality and Lineage — How data moves from source systems to reports, and how quality checks and lineage explain figures that do not match.
- Generative AI in Analytics: Copilots and Text-to-SQL — How AI assistants turn plain-language questions into queries, and how to tell a correct answer from a silent failure.
- Data Governance and Compliance: GDPR and the EU AI Act — Applying GDPR and the EU AI Act to everyday analytics work, including the difference between anonymised and merely de-identified data.
- Real-Time and Streaming Analytics — When real-time analysis is worth its cost, and how a streaming pipeline is built from three moving parts.
- EU AI Act Annex III: High-Risk AI Systems in Data-Driven Decisions — Reading Annex III closely to judge whether a model, such as credit scoring, is high-risk and what must change before launch.
- Sector Case Studies: Finance, Insurance and Retail — The full toolkit applied to fraud, underwriting and demand planning, showing what transfers between sectors and what changes.
Internship agreement
With this course, an intern could support a data, analytics or risk team: answering business questions with queries, building charts for reports and checking why dashboard figures differ from source systems. Depending on the host company, tasks might also involve reviewing datasets for GDPR compliance or supporting fraud, underwriting or demand-planning analysis.
The internship can run at the same time as the course, within the 180 days of access.
Frequently asked questions
What kind of job does the Big Data Analytics course prepare me for?
The Big Data Analytics course is built around the work of a junior or graduate data analyst. It covers answering business questions with SQL, presenting results in clear charts, understanding data architectures and pipelines, using AI copilots critically, and handling GDPR and EU AI Act obligations. The final module applies this toolkit to three common employer types: banks, insurers and retailers, covering fraud, underwriting and demand planning.
What could I do during the internship, and who arranges the company?
You find the host company yourself; the school issues the internship agreement and its annex, which the company signs electronically, usually within one or two working days. Suitable placements are on data, analytics, finance, risk or business intelligence teams, where you could write queries, prepare charts and reports, check data quality and lineage, or review datasets for GDPR compliance.
Which tools, methods and regulations does the course cover?
The course covers SQL and data modelling, Python for data cleaning and visualisation, data warehouses, data lakes and lakehouses, ETL/ELT pipelines with data quality checks and lineage, generative AI copilots and text-to-SQL, and real-time streaming pipelines. On regulation, it covers the GDPR, including anonymisation, and the EU AI Act, with a dedicated module on Annex III high-risk AI systems such as credit scoring.
Do I need to be a programmer to work in data analytics?
No. The course treats the idea that Python is only for software engineers as a misconception: analysts use it to automate repetitive cleaning and transformation that spreadsheets cannot handle at larger volumes. The SQL module starts by teaching you to reason through a query before writing any code. The course also stresses that a chart is not a complete analysis; interpretation and decision-making come afterwards.
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Content updated: 07/10/2026