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 Analytics |
|---|---|---|
| Level | Advanced | Beginner |
| Duration | 3–4 Months | 3–4 Months |
| Who can join | Basic computer skills and school-level maths; Python or machine learning knowledge is useful but not compulsory | 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 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: Data Analytics Foundations · Module 2: Advanced Excel for Analysis · Module 3: SQL for Data Analysis · Module 4: Statistics for Analysts |
| Portfolio projects | — | — |
| Career roles | Deep Learning Engineer, Machine Learning Engineer, Computer Vision Engineer | Data Analyst, Business Analyst, MIS Executive |
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 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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