Both are taught at techcadd. Here is how they differ on level, tools, projects and where each one can take you.
| Feature | RAG (Retrieval-Augmented Generation) | Data Science |
|---|---|---|
| Level | Advanced | Intermediate |
| Duration | 2 Months | 5–6 Months |
| Who can join | Open to any stream; no machine learning background needed. Python basics are taught from scratch | Basic school-level maths; no prior coding experience needed |
| Key tools | Python, Jupyter, VS Code, Git and GitHub, OpenAI, Anthropic Claude | Python, Jupyter Notebook, NumPy, Pandas, Matplotlib, Seaborn |
| What you’ll learn | Module 1: Foundations of LLMs and RAG · Module 2: Python for AI Applications · Module 3: Document Processing and Chunking · Module 4: Embeddings and Vector Databases | Module 1: Foundations of Data Science · Module 2: Python Programming for Data Science · Module 3: Statistics and Probability · Module 4: SQL and Databases |
| Portfolio projects | — | — |
| Career roles | RAG developer, LLM application developer, AI engineer | Data analyst, Business analyst, Junior data scientist |
Build AI assistants that answer accurately from your own documents, websites and databases.
Learn to collect, clean, analyse and visualise data, then build predictive models that solve real business problems, using Python, SQL, statistics, machine learning and modern visualisation tools.
A counsellor can map either course to your degree, schedule and career goal — one call is usually enough.
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