Subject Code & Name: PT25305 – Introduction to Data Science
Regulation: R-2025
Semester: III (Third Semester)
Branch: B.Tech. Plastics Technology (Plastics)
Credits / L-T-P: 3 Credits | L-T-P: 2-0-2
Course Objectives
- An understanding of the data operations.
- An overview of simple statistical models and the basics of machine learning techniques of regression.
- An understanding good practices of data science.
- Skills in the use of tools such as python, IDE.
- Understanding of the basics of the Supervised learning.
Full Unit-wise Syllabus
Unit I – Introduction, Toolboxes
Python, fundamental libraries for data Scientists. Integrated development environment (IDE). Data operations: Reading, selecting, filtering, manipulating, sorting, grouping, rearranging, ranking, and plotting
Practical: Data Analysis and Visualization using Python or MATLAB
Activities: Load a dataset and perform basic data operations like filtering, sorting, and grouping using Pandas. Visualize insights using plots with Matplotlib or Seaborn.
Unit II – Descriptive statistics and data preparation
Exploratory Data Analysis data summarization, data distribution, measuring asymmetry. Sample and estimated mean, variance and standard score. Statistical Inference frequency approach, variability of estimates, hypothesis testing using confidence intervals, using values.
Practical: Descriptive Statistics and Hypothesis Testing Python or MATLAB
Activities: Perform Exploratory Data Analysis on a dataset to summarize key features using mean, median, and variance. Visualize data distribution and asymmetry using histograms and box plots.
Unit III – Supervised Learning
First step, learning curves, training-validation and test. Learning models generalities, support vector machines, random forest. Examples.
Practical: Supervised Learning Model Implementation and Evaluation using Python or
Unit IV – MATLAB
Activities: Study on performance metrics to understand model generalization.
Unit V – Regression analysis
Regression: simple linear regression, multiple & Polynomial regression, Sparse model. Unsupervised learning, clustering, similarity and distances, quality measures of clustering, case study.
Practical: Regression and Clustering Analysis using Python or MATLAB
Activities: Apply Simple Linear Regression, multiple, and polynomial regression models to understand relationships between variables. Compare model performance and interpret coefficients to analyze prediction accuracy.
Unit VI – Network Analysis
Graphs, Social Networks, centrality, drawing centrality of Graphs, PageRank, Ego-Networks, community Detection.
Practical: Graph Analysis and Community Detection using Python or MATLAB
Activities: Explore basics of Network Analysis by representing data as graphs with nodes and edges. Compute centrality measures (degree, closeness, betweenness) to identify important nodes.
Course Outcomes (COs)
- CO1: Data Science, data driven decision making, and the skill sets required for data scientists. Apply exploratory data analysis.
- CO2: (EDA) techniques to summarize visualize, and interpret datasets. Analyze supervised learning techniques, including classification.
- CO3: Techniques, including classification methods and Support Vector Machines (SVM), for solving engineering problems.
- CO4: Evaluate the performance of machine learning models such as Linear Regression using appropriate metrics and validation techniques.
- CO5: Design and develop data driven solutions using network analysis concepts and PageRank algorithms for real world applications.
Assessment Pattern (Quick Note)
- Weightage: Continuous Assessment 50% | End Semester Examinations 50%
- Internal methodology: Theory (30%), Practical (10%), Activities (10%)
Source: Official Anna University – B.Tech. Plastics Technology R-2025 Curriculum
Last Updated: September 2026