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 Science |
|---|---|---|
| Level | Intermediate | Intermediate |
| Duration | 6–9 Months | 5–6 Months |
| Who can join | No prior coding experience; basic school-level maths (percentages, simple algebra) | Basic school-level maths; no prior coding experience needed |
| Key tools | Python, Jupyter Notebook, Google Colab, NumPy, pandas, Matplotlib | Python, Jupyter Notebook, NumPy, Pandas, Matplotlib, Seaborn |
| 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: 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 | AI/ML trainee, Data analyst, Junior data scientist | Data analyst, Business analyst, Junior data scientist |
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 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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