Both are taught at techcadd. Here is how they differ on level, tools, projects and where each one can take you.
| Feature | Machine Learning | Data Science |
|---|---|---|
| Level | Intermediate | Intermediate |
| Duration | 4–6 Months | 5–6 Months |
| Who can join | Basic computer skills and school-level maths; no prior programming experience required | Basic school-level maths; no prior coding experience needed |
| Key tools | Python, Jupyter Notebook, Google Colab, NumPy, Pandas, SQL | Python, Jupyter Notebook, NumPy, Pandas, Matplotlib, Seaborn |
| What you’ll learn | Module 1: Python for Machine Learning · Module 2: Maths and Statistics Essentials · Module 3: Data Handling and Visualisation · Module 4: Supervised 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 | Machine Learning Engineer (junior), Data Analyst, Junior Data Scientist | Data analyst, Business analyst, Junior data scientist |
Start with Python, statistics and data handling, then move on to supervised and unsupervised learning, model evaluation and deploying simple models into real applications.
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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