Data Analysis & Trends in Computer Science

Unit

Topics

Teaching Hours

I

Data Analysis: Sensitivity Analysis with Data Tables, Goal seek, Scenario Manager, Optimization with EXCEL Solver, Introduction to MATLAB, Summarizing Data with Histograms and Descriptive Statistics.

Pivot Tables: Summarizing Data with database statistical functions, using correlation, Multiple Regression, ANOVA, Using Resampling to Analyze Data, Working with charts, Introduction to Statistical Analysis of data.

12

II

Introduction to Machine Learning: Understand the problem and Data, Knowledge discovery process, data preprocessing, unbalanced data, exploratory analysis, types of machine learning: supervised, unsupervised and reinforcement learning.

Unsupervised Learning: Association rules, Apriori algorithm and FP tree algorithm. Clustering: k-means, hierarchical clustering, and DBSCAN.

Supervised Learning: Model Construction, performance metrics, attribute selection, Decision tree, Linear Regression, Logistic Regression, Naïve Bayes and Support Vector Machines(SVM) and Artificial Neural network. Types of ANN, deep learning (CNNs, RNNs, Transformers), generative models, Explainable AI (XAI), AI ethics, and model interpretability.

Data Visualization: Principles of data visualization, perception and cognition, exploratory vs. explanatory visualization.

12

III

Cyber Security: Fundamentals of cybersecurity, cryptographic techniques, network security, malware analysis, threat detection, authentication and access control, ethical hacking, security in cloud computing, risk assessment, and mitigation strategies.

Internet of Things (IoT): Introduction to IoT architecture, communication protocols, sensor networks, edge and cloud computing in IoT, security challenges in IoT, IoT applications in healthcare, smart cities, and industrial automation.

12

IV

Review of Elementary Data Structures: Greedy method, Knapsack problem, job sequencing with deadlines, Dynamic Programming: Multistage graphs Optimal binary search trees, 0/1 knapsack, The traveling salesperson problem, Flow shop scheduling.

Basic search and traversal techniques: The techniques, Code Optimization, Biconnected components and depth first search.

Backtracking: The 8–Queens Problem, Hamiltonian cycles

Object Oriented Paradigm: Characteristics of object oriented approach, objects, classes, inheritance, reusability.

Unified Modeling Language: Basic structures and modeling classes, common modeling techniques, relationships, common mechanism, class diagrams.

12

V

New Computing Paradigms & Services: Cloud computing, Edge computing, Grid computing, Utility computing.

Introduction to Cloud Computing: Cloud Computing Architectural Framework, Cloud Deployment Models, Virtualization in Cloud Computing, Parallelization in Cloud Computing, Security for Cloud Computing, Cloud Economics.

Basics of Service Models: Software as a Service (SaaS), Infrastructure as a Service (IaaS), Platform as a Service (PaaS).

Foundational Elements of Cloud Computing: Introduction to Grid technology, Browser as a platform (BaaP).

12