MODULE 1
Questions and datasets
define the analysis, data format, variables and usage boundaries.
Vibecoding
Vibe Coding and Data Analytics: Analyze Big Data with AI
The Vibe Coding and Data Analytics course explores applications for importing datasets, processing data and visualizing results. It also introduces Big Data scaling issues, distinguishing sample-based exploration from architectures needed for large volumes.
Understand. Apply. Evaluate.
Proposed duration12 hours
Programme6 course modules
ArrangementsInformation on request
Why choose this course.
An analytics application should make data provenance and quality visible. The course connects prototyping, checks and interpretation to avoid conclusions based on unverified numbers.
THE SKILLS
What you will learn.
- Design an application for importing and exploring datasets.
- Add quality checks before calculating metrics.
- Interpret correlations without automatically inferring causes.
- Recognize prototype limitations as data volumes grow.
THE PROGRAMME
A course, step by step.
MODULE 2
Import and quality
file uploads, data types, missing values, duplicates and schema checks.
MODULE 3
Assisted processing
filters, aggregations, transformations and generated code checks.
MODULE 4
Exploration and correlations
descriptive statistics, comparisons and distinguishing association from causation.
MODULE 5
Visualization
charts, tables and messages that make assumptions and limitations explicit.
MODULE 6
Scale and testing
sampling, memory, processing time and criteria for moving to suitable data architecture.
What the course includes
- Applied exercise described in the programme
WHO IT IS FOR
Find your starting point.
- Data analysts and business analysts
- Business reporting and analytics leads
- Professionals working with datasets
Before you start
- Basic knowledge of tables, metrics and data analysis.
- Introductory Python or SQL knowledge is helpful; suitability can be assessed with the school.
An example application
Prototype an application that uploads a demonstration dataset, checks its quality, applies filters and compares groups. Outputs include visuals and a note on sample size and analytical limitations.
YOUR QUESTIONS, ANSWERED
Questions about the course.
Can a prototype automatically handle any Big Data volume?
No. Memory, time, infrastructure and access need planning. Demonstration exercises also help identify when more suitable architecture is required.
Do AI-detected correlations establish causation?
No. The programme distinguishes associations from causal hypotheses and requires checking data and context before drawing conclusions.
What is the proposed duration?
The proposed duration is 12 hours. The school will confirm scheduling, session structure, delivery arrangements and tool requirements according to the agreed course.
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Your next step
starts here.
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Tell us what interests you and what you need.