Fundamentals of Data science & Machine Learning syllabus
MDM-272-ELE · Second Year Electrical Engineering, SPPU 2024 pattern. Every unit, the marks scheme, course outcomes and books, copied from the official syllabus PDF.
Unit-wise syllabus
Python Programming Basics for Data Science
6 hoursa) Python environment: Jupyter, Google Colab b) Data types: int, float, string, list, tuple, dictionary c) Python Functions d) Classes & Objects in Python e) Introduction to NumPy and pandas: array and DataFrame basics f) File operations: Reading and writing CSVs
Fundamentals of Data Science and Applications in Electrical Engg
6 hoursa) Data Science: Concept, Key components and lifecycle b) Structured vs unstructured data, time-series in electrical systems c) Applications in Electrical Engineering: Load forecasting, Smart grids, Fault prediction d) Roles in data science: Analyst, Engineer, Scientist e) Overview of tools: Python, R, Jupyter, Excel
Data Acquisition, Visualization, and Hypothesis Testing
6 hoursa) Data sources: SCADA, IoT sensors, meters b) Data preprocessing: handling missing values, outliers, duplicates c) Visualization tools: matplotlib, seaborn: Line plot, histogram, boxplot, scatter plot d) Descriptive statistics: mean, median, mode, std deviation e) Hypothesis testing: Null and alternate hypotheses, p-value, significance level, type I/II errors, t-test and z-test (conceptual introduction) f) Use case: Load comparison before and after optimization
Introduction to Machine Learning (ML)
6 hoursa) Machine Learning (ML): Concept, scope of ML b) Differences between Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) c) ML Types: Supervised, Unsupervised (overview) d) ML pipeline: Data → Train → Predict → Evaluate e) Electrical Applications: Predictive maintenance, Load and outage forecasting, Fault classification f) Introduction to scikit-learn and model-building steps
Basic ML Algorithms and Model Evaluation
6 hoursSupervised Learning: Linear Regression (for load forecasting), K-Nearest Neighbors (for fault classification) Unsupervised Learning: K-Means Clustering (load profile segmentation) Model Evaluation: Confusion Matrix, Accuracy, Precision, Recall, F1-Score, Cross-validation basics Simple Python implementation using scikit-learn
Marks and credits
| Head | Marks | Credit |
|---|---|---|
| CCE (continuous comprehensive evaluation) | 30 | 2 |
| End-semester exam | 70 |
Course outcomes
- CO1Write and execute Python programs, manipulate data using NumPy and Pandas.
- CO2Differentiate structured and unstructured data, apply in electrical engineering, and identify data science roles and tools.
- CO3Collect, clean, visualize electrical data, apply descriptive statistics, understand hypothesis testing concepts.
- CO4Differentiate AI, ML, DL, describe ML workflow, apply in electrical systems, and build models with scikit- learn.
- CO5Apply Linear Regression, K-Nearest Neighbors, K-Means Clustering to electrical data, evaluate models, implement using scikit-learn.
Books
Text books
- VanderPlas, Jake. Python Data Science Handbook: Essential Tools for Working with Data. O’Reilly Media, 2016.
- Raschka, Sebastian & Mirjalili, Vahid. Python Machine Learning. Packt Publishing, 3rd Edition, 2019.
- McKinney, Wes. Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython. O’Reilly Media, 2nd Edition, 2017.
- Alpaydin, Ethem. Introduction to Machine Learning. MIT Press, 4th Edition, 2020.
Reference books
- Saxena, Atulya K. Data Science and Machine Learning Applications in Engineering. CRC Press, 2021.
- James, Gareth et al. An Introduction to Statistical Learning with Applications in R. Springer, 2nd Edition, 2021.
- IEEE papers and case studies on Smart Grids, Load Forecasting, and Predictive Maintenance
- Online resources [E1]. https://colab.research.google.com – Google Colab environment [E2]. https://scikit-learn.org – Machine Learning Library Documentation [E3]. https://matplotlib.org, https://seaborn.pydata.org – Visualization libraries [E4]. Kaggle Datasets – Sample datasets for electrical applications
FAQ
How many units are in Fundamentals of Data science & Machine Learning?
Fundamentals of Data science & Machine Learning (MDM-272-ELE) has 5 units and 30 hours of theory: Unit I Python Programming Basics for Data Science (6 h); Unit II Fundamentals of Data Science and Applications in Electrical Engg (6 h); Unit III Data Acquisition, Visualization, and Hypothesis Testing (6 h); Unit IV Introduction to Machine Learning (ML) (6 h); Unit V Basic ML Algorithms and Model Evaluation (6 h).
What is the marks scheme for Fundamentals of Data science & Machine Learning?
The official Electrical Engineering 2024 pattern syllabus lists continuous comprehensive evaluation (CCE) for 30 marks and the end-semester exam for 70 marks, for 2 credits.