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 Analytics |
|---|---|---|
| Level | Advanced | Beginner |
| Duration | 2 Months | 3–4 Months |
| Who can join | Open to any stream; no machine learning background needed. Python basics are taught from scratch | Basic computer use; no programming or technical background required |
| Key tools | Python, Jupyter, VS Code, Git and GitHub, OpenAI, Anthropic Claude | Microsoft Excel, Google Sheets, SQL, MySQL, PostgreSQL, Python |
| 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: Data Analytics Foundations · Module 2: Advanced Excel for Analysis · Module 3: SQL for Data Analysis · Module 4: Statistics for Analysts |
| Portfolio projects | — | — |
| Career roles | RAG developer, LLM application developer, AI engineer | Data Analyst, Business Analyst, MIS Executive |
Build AI assistants that answer accurately from your own documents, websites and databases.
Learn to collect, clean, analyse and present data using the tools employers actually ask for, such as Excel, SQL, Power BI, Tableau and Python, through real datasets and hands-on projects.
A counsellor can map either course to your degree, schedule and career goal — one call is usually enough.
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