Both are taught at techcadd. Here is how they differ on level, tools, projects and where each one can take you.
| Feature | Data Science | Data Analytics |
|---|---|---|
| Level | Intermediate | Beginner |
| Duration | 5–6 Months | 3–4 Months |
| Who can join | Basic school-level maths; no prior coding experience needed | Basic computer use; no programming or technical background required |
| Key tools | Python, Jupyter Notebook, NumPy, Pandas, Matplotlib, Seaborn | Microsoft Excel, Google Sheets, SQL, MySQL, PostgreSQL, Python |
| What you’ll learn | Module 1: Foundations of Data Science · Module 2: Python Programming for Data Science · Module 3: Statistics and Probability · Module 4: SQL and Databases | 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 | Data analyst, Business analyst, Junior data scientist | Data Analyst, Business Analyst, MIS Executive |
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.
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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