II Year

M.Sc.(Data –Science)-Semester-III

               Course Code:MDS-301

              Course Name: Deep Learning Techniques

              Course Outcomes

              CO1: Understand the fundamentals of Artificial Neural Networks, including neuron models, architectures, learning paradigms, and data preprocessing                           techniques.

            CO2: Apply supervised and unsupervised learning algorithms such as Perceptron, Backpropagation, Hebbian learning, and Self-Organizing Maps.
            CO3: Analyze advanced neural network models including Radial Basis Function Networks, Hopfield Networks, and Boltzmann Machines.
            CO4: Evaluate modern deep learning techniques such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), LSTM, and                                   Generative Adversarial Networks (GANs)

             Course Code:MDS-302

              Course Name: Computer Networks

              Course Outcomes

              CO1: Understand fundamental concepts of computer networks, including OSI and TCP/IP models, transmission media, and data link layer protocols.
            CO2: Apply multiple access techniques, switching methods, and network layer routing and congestion control algorithms.
            CO3: Analyze internetworking concepts, IP addressing schemes, and key protocols such as ARP, DHCP, IPv4, and IPv6.
            CO4: Evaluate transport and application layer protocols including TCP, UDP, DNS, HTTP, and their role in network communication.

            Course Code:MDS-303

              Course Name: Cloud Computing

              Course Outcomes

              CO1: Understand core concepts of cloud computing, including service models (IaaS, PaaS, SaaS), virtualization, resource provisioning, and cloud storage.
            CO2: Apply cloud infrastructure techniques such as scaling, load balancing, containerization, and database management in cloud environments.
            CO3: Analyze cloud security, privacy, compliance, and interoperability challenges along with content delivery mechanisms.
            CO4: Evaluate enterprise cloud solutions, including SOA, workflow systems, enterprise applications, and cloud ecosystem architectures.

            Course Code:MDS-304

              Course Name: Big Data Analytics

              Course Outcomes

              CO1: Understand the fundamentals of Big Data, Hadoop ecosystem, and distributed computing concepts for handling large-scale data.

            CO2: Apply MapReduce programming techniques and optimize data processing using combiners, partitioners, and input/output formats.
            CO3: Analyze Big Data storage solutions including HDFS, NoSQL databases like HBase, and their integration with traditional systems.
            CO4: Utilize high-level tools such as Hive and Pig to perform data analysis and implement different types of analytics in real-world scenarios.

M.Sc.(Data –Science)-Semester-IV

          Course Code:MDS-401

              Course Name: Cryptography

              Course Outcomes

CO1: Understand fundamental concepts of network security, including security attacks, services, mechanisms, and classical encryption techniques.
CO2: Apply modern cryptographic techniques such as AES, RSA, Diffie-Hellman, and key distribution methods for secure communication.
CO3: Analyze cryptographic tools including hash functions, MACs, digital signatures, and their applications in data integrity and authentication.
CO4: Evaluate network and system security protocols such as SSL/TLS, IPsec, firewalls, and intrusion detection systems

              Course Code:MDS-402

              Course Name: Data Mining

              Course Outcomes

              CO1: Understand fundamental concepts of data mining, data preprocessing, and data understanding techniques including similarity and visualization.
            CO2: Apply frequent pattern mining and association rule techniques to discover meaningful patterns in large datasets.
            CO3: Analyze classification methods such as decision trees, Bayesian methods, neural networks, and support vector machines.

            CO4: Evaluate clustering techniques and emerging trends in data mining for real-world applications.

            Course Code:MDS-403

              Course Name: Sentimental Analysis

              Course Outcomes

            CO1: Understand fundamental concepts of sentiment analysis, opinion mining, and types of opinions, emotions, and document-level classification                                    techniques.
            CO2: Apply sentence-level sentiment analysis and subjectivity detection methods, including handling sarcasm and generating sentiment lexicons.
            CO3: Analyze comparative opinions, perform aspect-based summarization, and implement opinion search and retrieval techniques.
            CO4: Evaluate opinion quality by detecting fake reviews, analyzing user behavior, and applying intention mining techniques.

            Course Code:MDS-404

              Course Name: Web Mining

              Course Outcomes

            CO1: Understand fundamentals of web data mining, association rule mining, and sequential pattern mining techniques.
            CO2: Apply machine learning methods including classification and clustering for analyzing web data.
            CO3: Analyze information retrieval models, text preprocessing techniques, and web search mechanisms.
            CO4: Evaluate link analysis algorithms, web crawling strategies, and sentiment classification for web applications.