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 Analytics |
|---|---|---|
| Level | Intermediate | Beginner |
| Duration | 4–6 Months | 3–4 Months |
| Who can join | Basic computer skills and school-level maths; no prior programming experience required | Basic computer use; no programming or technical background required |
| Key tools | Python, Jupyter Notebook, Google Colab, NumPy, Pandas, SQL | Microsoft Excel, Google Sheets, SQL, MySQL, PostgreSQL, Python |
| 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: Data Analytics Foundations · Module 2: Advanced Excel for Analysis · Module 3: SQL for Data Analysis · Module 4: Statistics for Analysts |
| Portfolio projects | — | — |
| Career roles | Machine Learning Engineer (junior), Data Analyst, Junior Data Scientist | Data Analyst, Business Analyst, MIS Executive |
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 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.
Book a Free Demo