Both are taught at techcadd. Here is how they differ on level, tools, projects and where each one can take you.
| Feature | Deep Learning | Data Science |
|---|---|---|
| Level | Advanced | Intermediate |
| Duration | 3–4 Months | 5–6 Months |
| Who can join | Basic computer skills and school-level maths; Python or machine learning knowledge is useful but not compulsory | 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 and Maths for Deep Learning · Module 2: Machine Learning Foundations · Module 3: Neural Network Fundamentals · Module 4: Building and Tuning Models with TensorFlow, Keras and PyTorch | 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 | Deep Learning Engineer, Machine Learning Engineer, Computer Vision Engineer | Data analyst, Business analyst, Junior data scientist |
Learn how artificial neural networks, CNNs, RNNs, LSTMs and transformers learn from data, working in Python with TensorFlow, Keras and PyTorch on real datasets.
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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