Both are taught at techcadd. Here is how they differ on level, tools, projects and where each one can take you.
| Feature | Artificial Intelligence | Data Analytics |
|---|---|---|
| Level | Intermediate | Beginner |
| Duration | 6–9 Months | 3–4 Months |
| Who can join | No prior coding experience; basic school-level maths (percentages, simple algebra) | Basic computer use; no programming or technical background required |
| Key tools | Python, Jupyter Notebook, Google Colab, NumPy, pandas, Matplotlib | Microsoft Excel, Google Sheets, SQL, MySQL, PostgreSQL, Python |
| What you’ll learn | Module 1: Python for AI · Module 2: Data Handling and Maths Essentials · Module 3: Data Analysis and Visualisation · Module 4: Machine Learning | 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 | AI/ML trainee, Data analyst, Junior data scientist | Data Analyst, Business Analyst, MIS Executive |
Begin with Python and the essential maths, then move into machine learning, deep learning, natural language processing and generative AI, applying each concept through hands-on projects.
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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