CS25C16 Applied Data Science – Semester IV – Mech / Aero / Auto / Mfg / IEM / Marine / Mechatronics / MechAuto / Robotics / Aerospace / Mech(Auto) / Mech(Smart) – R-2025

Subject Code & Name: CS25C16 – Applied Data Science

Regulation: R-2025

Semester: IV (Fourth Semester)

Branch: B.E. Mechanical Engineering (Mech) / B.E. Aeronautical Engineering (Aero) / B.E. Automobile Engineering (Auto) / B.E. Manufacturing Engineering (Mfg) / B.E. Industrial Engineering and Management (IEM) / B.E. Marine Engineering (Marine) / B.E. Mechatronics Engineering (Mechatronics) / B.E. Mechanical and Automation Engineering (MechAuto) / B.E. Robotics and Automation (Robotics) / B.E. Aerospace Engineering (Aerospace) / B.E. Mechanical Engineering (Automobile) (Mech(Auto)) / B.E. Mechanical Engineering (Smart Manufacturing) (Mech(Smart))

Credits / L-T-P: 3 Credits | L-T-P: 3-0-0

Course Objectives

• This course introduces the core concepts of data science, including data preprocessing, exploratory analysis, and machine learning techniques.
• It aims to develop the ability to analyze and interpret data using modern tools for real-world problem solving while emphasizing ethical and responsible use of data.

Full Unit-wise Syllabus

Unit I – Introduction and Mathematics for Data Science

Linear equations and solutions, Matrices and their Properties; Eigenvalues and eigenvectors; Matrix Factorizations, Inner products, Distance measures; Projections; Notion of hyperplanes; halfplanes. Descriptive statistics, Inferential statistics, Hypothesis testing, Regression analysis Statistical modelling.

Activities: Perform statistical analysis and regression on a dataset using programming language to understand data distribution and relationships.

Unit II – Introduction and Mathematics for Data Science

Data Science Definition, Data Science lifecycle, Types of data (structured, unstructured, semi-structured), Data sources and data collection methods- Role of Data Scientist- Applications in healthcare, banking, e-commerce.

Unit III – Data Preprocessing & Wrangling

Data cleaning (missing values, noise handling), Data transformation and normalization, Feature engineering, Data integration, Handling Outliers-Data pipelines-Tools: Python (Pandas, NumPy)

Activities: Clean and preprocess a real dataset by handling missing values, outliers, and applying normalization using Pandas.

Unit IV – Exploratory Data Analysis (EDA) & Visualisation

Descriptive statistics, Data visualisation techniques, Bar charts, histograms, box plots, Scatter plots, heatmaps, Correlation and covariance- Identifying patterns and trends, Tools: Matplotlib, Seaborn

Activities: Analyze and visualize a dataset using plots (histogram, scatter, heatmap) to identify patterns and correlations.

Unit V – Machine Learning Techniques

Supervised Learning: Linear Regression, Logistic Regression, Decision Trees, k-NN, Unsupervised Learning: K-Means Clustering, Hierarchical Clustering, Model evaluation: Accuracy, Precision, Recall, F1-score, Confusion Matrix, Overfitting & Underfitting.

Activities: Build and evaluate a machine learning model (e.g., regression or classification) using Scikit-learn.

Unit VI – Applied Data Science & Case Studies

Real-world applications, Recommendation systems, Fraud detection, Traffic Route Optimisation, Customer segmentation, Introduction to Big Data concepts, Ethics in Data Science, Data privacy and security basics.

Activities: Develop a mini project (e.g., house price prediction or customer segmentation) and present insights with ethical considerations.

Course Outcomes (COs)

• CO1: Explain the concepts of data science lifecycle, mathematics, preprocessing, EDA, and ML techniques.
• CO2: Describe data preprocessing, feature engineering, and visualization techniques.
• CO3: Apply data preprocessing, visualization, and machine learning algorithms using tools.
• CO4: Analyze datasets using statistical and analytical methods to derive insights.

Assessment Pattern (Quick Note)

• Weightage: Continuous Assessment 40% | End Semester Examinations 60%
• Internal methodology: Quiz (5%), Assignments (25%), Review of GATE/ESE Questions (20%), Internal Examinations (50%)

Source: Official Anna University – B.E. Mechanical Engineering R-2025 Syllabus
Last Updated: October 2026