Data Science — What This Subject Is About
To learn the basics of Data Science and its use in electronics; gain skills in collecting, cleaning and analysing data; use Python with tools like NumPy and Pandas; understand basic machine learning and how it applies to electronics; and apply data science in IoT, automation and signal processing.
Subject 6.2.4 — Elective (Pathway), Option 4 (Theory) • Total 100 Marks • 3 Credits • L 3 – P 0 • Choose this OR Managerial Economics OR Finance Fundamentals OR Cyber Security • 6th Semester — Electronics Engineering • Session 2026–27
What You Will Study
Unit 1 — Introduction to Data Science (8 Periods, 20%)
What is Data Science? Evolution and Importance, Data Science vs. Data Analytics vs. Machine Learning, Applications in Electronics: IoT, Automation, Signal Processing, Overview of Data Science Workflow
What data science is, how it differs from data analytics and machine learning, its uses in electronics, and the workflow from data to decision.
Unit 2 — Data Handling and Visualization (8 Periods, 20%)
Types of Data: Structured, Unstructured, Time-Series, Data Collection Methods (from sensors, logs, APIs), Data Cleaning Techniques, Introduction to Excel and CSVs, Visualization with Matplotlib / Seaborne / Excel, Basics of Statistics (mean, median, mode, std. dev.)
Handling data: its types, collecting it from sensors, logs and APIs, cleaning it, visualising it, and the basic statistics that describe it.
Unit 3 — Python for Data Science (8 Periods, 20%)
Why Python for Data Science? Basics of Python Programming (variables, loops, functions) , Libraries: NumPy, Pandas – Data Manipulation , Reading sensor data from files , Mini project: Handling electronics-related datasets (e.g., temperature, voltage logs)
Python for data work: the basics, NumPy and Pandas for manipulating data, and a mini-project on electronics datasets like temperature and voltage logs.
Unit 4 — Introduction to Machine Learning (8 Periods, 20%)
Supervised vs. Unsupervised Learning, Algorithms Overview: Linear Regression, KNN, Decision Trees, Real-world examples in electronics (e.g., fault prediction, pattern detection), Model Training, Testing, and Evaluation.
Machine learning basics: supervised vs unsupervised learning, linear regression, KNN and decision trees, and how to train, test and evaluate a model.
Unit 5 — Real-World Applications of Data Science (10 Periods, 20%)
Data Science in IoT & Smart Devices, Signal and Image Data Analysis Basics, Introduction to Big Data Concepts (brief) , Ethics in Data Science and AI , Capstone Project Guidelines
Data science in the real world: IoT and smart devices, signal and image data, big data in brief, ethics, and the capstone project.
Complete Syllabus
The full examination scheme, the complete practicals list and the reference books from the official syllabus. The unit-wise topics are in the "What You Will Study" section above.
Examination Scheme
| Code | Paper | Type | L – P | Credits | Internal | External | Total |
|---|---|---|---|---|---|---|---|
| 6.2.4 | Elective (Pathway): Data Science | Theory | 3 – 0 | 3 | Theory 40 | Theory 60 (3 hrs) | 100 |
| Data Science Total | 3 | 40 | 60 | 100 | |||
Units at a Glance
| Unit | Topic | Periods | Marks Share |
|---|---|---|---|
| 1 | Introduction to Data Science | 8 | 20% |
| 2 | Data Handling and Visualization | 8 | 20% |
| 3 | Python for Data Science | 8 | 20% |
| 4 | Introduction to Machine Learning | 8 | 20% |
| 5 | Real-World Applications of Data Science | 10 | 20% |
Reference Books
As listed in the syllabus:
- “Doing Data Science” by Cathy O'Neil & Rachel Schutt
- “Data Visualization with Python and JavaScript” by Kyran Dale
- “Python Data Science Handbook” by Jake VanderPlas
- “Machine Learning for Absolute Beginners” by Oliver Theobald
- “Data Science for IoT Engineers” by Perry Lea
- Data Science & Machine Learning by Rashmi Singh
Frequently Asked Questions
How many marks is Data Science worth in the 6th semester?
Elective (Pathway): Data Science (6.2.4) is 100 marks. Total: 100 marks, 3 credits. The full scheme table is in the Complete Syllabus section above.
Which units carry the most weightage in Data Science?
Real-World Applications of Data Science and Python for Data Science and Introduction to Machine Learning and Introduction to Data Science and Data Handling and Visualization, 20% each. The full unit-wise weightage table is in the Complete Syllabus section above.
Which books should I refer to for Data Science?
The syllabus lists “Doing Data Science” by Cathy O'Neil & Rachel Schutt, and “Data Visualization with Python and JavaScript” by Kyran Dale, among others. The full list is in the Complete Syllabus section above.
Is Data Science a theory subject or a practical subject?
It is assessed as theory — see the examination scheme in the Complete Syllabus section above.
How can I download the Data Science syllabus PDF?
The PDF is free to download — use the download link in the Download section below. It is prepared by KAMALDNP Academy from the official BTEUP syllabus.
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Download the Data Science Syllabus PDF
Download the complete Data Science syllabus PDF below — prepared by KAMALDNP Academy, with the full topic list, practicals and reference books.
Download the Data Science Syllabus PDF
Source: BTEUP 3rd Year curriculum (folder2627), approved in C.D.C. meeting held on 16.07.2026. Topic lists follow the official syllabus; short explanations are the author's own plain-English summaries, not syllabus text. Please confirm with your own polytechnic for any local changes.
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