NDSS Academy
You can keep up to 10 CVs here -- delete one you don't need.
You can keep up to 10 CVs here -- delete one you don't need.
You can keep up to 10 CVs here -- delete one you don't need.
You can keep up to 10 CVs here -- delete one you don't need.

Computer Applications

MCA · Semester 4

6 MonthsDuration 4Subjects 21Chapters OnlineTests & result
Enquire now Nearest centre Syllabus PDF

Your roadmap · आपका रोडमैप

  1. 1
    Admission & first meetingप्रवेश और पहली मुलाकात

    Enquire at your nearest NDSS centre, meet the teacher, fix the batch time and get your study material.

  2. 2
    Semester 4 — subject classesसेमेस्टर 4 — विषयवार कक्षाएँ

    Every theory subject of the term, unit by unit, with notes, numericals and doubt classes.

    Data AnalyticsInternet of ThingsMachine Learning
  3. 3
    Labs, practicals & project workलैब, प्रैक्टिकल और प्रोजेक्ट

    Experiments / jobs / project work as in the syllabus, with practical file and viva preparation.

    Project
  4. 4
    Unit tests & mid-termयूनिट टेस्ट और मिड-टर्म

    A test after every unit and a mid-term in the university / board pattern.

  5. 5
    Semester 4 mock testमॉक टेस्ट

    A full mock test of all subjects before the exam, with analysis.

  6. 6
    Semester 4 exam (university / board)सेमेस्टर / वार्षिक परीक्षा

    Previous papers, important questions and full revision.

  7. 7
    Online tests & resultऑनलाइन टेस्ट और रिज़ल्ट

    Take the tests online; your marks and result show in your student login and at the centre.

Syllabus · पाठ्यक्रम

4 subjects · 21 chapters · 103 topics

Data Analytics5 chapters · 6 Months Subject page →
01Unit 1: Introduction to Data Analytics 6 topics
  1. Sources and nature of data
  2. Structured, semi-structured and unstructured data
  3. Characteristics of big data: the Vs
  4. Analysis versus reporting; modern analytic tools
  5. Data analytics life cycle: discovery, preparation, model planning, model building, communicating results, operationalisation
  6. Key roles in an analytics project
02Unit 2: Data Analysis Methods 6 topics
  1. Regression modelling
  2. Multivariate analysis
  3. Bayesian modelling and inference
  4. Support vector machines and kernel methods
  5. Time series analysis
  6. Principal component analysis and rule induction
03Unit 3: Mining Data Streams 6 topics
  1. Stream data model and architecture
  2. Sampling data in a stream
  3. Filtering streams: Bloom filter
  4. Counting distinct elements: Flajolet-Martin algorithm
  5. Counting ones in a window: DGIM algorithm
  6. Decaying windows and real-time analytics applications
04Unit 4: Frequent Itemsets and Clustering 6 topics
  1. Market basket analysis
  2. Support, confidence and association rules
  3. Apriori algorithm
  4. PCY and SON algorithms
  5. Clustering: k-means and hierarchical
  6. CLIQUE and PROCLUS; clustering in non-Euclidean spaces
05Unit 5: Frameworks and Visualisation 5 topics
  1. Hadoop, HDFS and MapReduce
  2. Pig and Hive
  3. HBase, NoSQL databases and sharding
  4. Data visualisation techniques
  5. Introduction to R for analytics
Internet of Things5 chapters · 6 Months Subject page →
01Unit 1: Introduction to IoT 6 topics
  1. Definition and characteristics of IoT
  2. Physical design: things and protocols
  3. Logical design: functional blocks, communication models and APIs
  4. Enabling technologies
  5. IoT levels and deployment templates
  6. IoT versus M2M
02Unit 2: Sensors, Actuators and IoT Hardware 6 topics
  1. Sensors and their types
  2. Actuators: motors, relays, solenoids
  3. Analog-to-digital conversion
  4. Arduino Uno
  5. Raspberry Pi
  6. ESP8266 / ESP32 Wi-Fi modules
03Unit 3: IoT Communication Protocols 6 topics
  1. IEEE 802.15.4 and ZigBee
  2. Bluetooth Low Energy and Wi-Fi
  3. LoRaWAN and NB-IoT
  4. RFID and NFC
  5. 6LoWPAN and RPL
  6. Application protocols: MQTT, CoAP, HTTP REST
04Unit 4: IoT Platforms, Cloud and Security 5 topics
  1. Data storage and processing for IoT
  2. IoT cloud platforms: AWS IoT, ThingSpeak, Azure IoT
  3. Edge and fog computing
  4. IoT data analytics
  5. Security and privacy challenges in IoT
05Unit 5: IoT Applications 5 topics
  1. Smart home and smart city
  2. Smart agriculture
  3. Healthcare IoT
  4. Industrial IoT and Industry 4.0
  5. Case study: building a simple IoT system
Machine Learning5 chapters · 6 Months Subject page →
01Unit 1: Introduction to Machine Learning 5 topics
  1. What is learning; well-posed learning problems
  2. Types of learning: supervised, unsupervised, reinforcement
  3. Designing a learning system
  4. Machine learning, AI and data science
  5. Issues in machine learning: overfitting, data quality
02Unit 2: Regression and Classification 5 topics
  1. Linear regression and least squares
  2. Gradient descent
  3. Logistic regression and the sigmoid function
  4. Bayes theorem and Naive Bayes classifier
  5. Support vector machines and kernels
03Unit 3: Decision Trees and Instance-based Learning 5 topics
  1. Decision tree representation
  2. Entropy and information gain; ID3 algorithm
  3. Overfitting and pruning
  4. k-nearest neighbour learning
  5. Case-based reasoning
04Unit 4: Artificial Neural Networks 5 topics
  1. Biological and artificial neurons
  2. Perceptron and its limitations
  3. Activation functions: step, sigmoid, ReLU
  4. Multilayer networks and backpropagation
  5. Introduction to deep learning and CNNs
05Unit 5: Unsupervised Learning and Model Evaluation 6 topics
  1. k-means and hierarchical clustering
  2. Dimensionality reduction with PCA
  3. Train-test split and k-fold cross-validation
  4. Confusion matrix, precision, recall and F1 score
  5. Bias-variance trade-off; bagging, boosting and random forest
  6. Introduction to reinforcement learning
Project6 chapters · 6 Months Subject page →
01Milestone 1: Problem Identification and Synopsis 4 topics
  1. Choosing an industry or research problem
  2. Literature / existing system study
  3. Objectives, scope and methodology
  4. Synopsis submission and guide approval
02Milestone 2: Requirement Analysis and Planning 3 topics
  1. SRS and feasibility study
  2. Project schedule and Gantt chart
  3. Risk identification
03Milestone 3: System Design 4 topics
  1. Architecture and module design
  2. Database design and ER diagram
  3. UML diagrams
  4. User interface design
04Milestone 4: Implementation and Testing 3 topics
  1. Coding and version control
  2. Unit, integration and system testing
  3. Deployment on a server or cloud
05Milestone 5: Dissertation 3 topics
  1. Chapters: introduction, analysis, design, implementation, testing, conclusion
  2. Screenshots, results and references
  3. Plagiarism check and binding
06Milestone 6: Demonstration and Viva 3 topics
  1. Mid-term progress review
  2. Final presentation and live demo
  3. External viva

About this course

You will be able to

  • Complete Data Analytics syllabus with regular tests
  • Complete Internet of Things syllabus with regular tests
  • Complete Machine Learning syllabus with regular tests
  • Complete Project syllabus with regular tests

Who it is for

  • MCA students of Semester 4 (AKTU, Lucknow)
  • Students aiming for data analyst or big data roles

What you need

  • Basic statistics and probability
  • Database and programming knowledge from earlier semesters
Online tests & result Practice and unit tests online; marks and result show in your student login and at the centre. Student login →

Questions

Which syllabus does this Data Analytics course follow?

It follows the AKTU (Lucknow) MCA syllabus for Semester 4. If your own university's syllabus differs, the centre follows your university's syllabus and unit order.

This is an elective. What if my college offers a different elective?

The centre teaches the elective your college has allotted; this course covers the AKTU syllabus of this elective.

Do we use real tools?

Yes. Examples are shown in Python / R and on a small Hadoop setup so that students see each method working.

Are numericals asked?

Yes. Support and confidence, Apriori, Flajolet-Martin and regression problems are practised step by step.

Which syllabus does this Internet of Things course follow?

It follows the AKTU (Lucknow) MCA syllabus for Semester 4. If your own university's syllabus differs, the centre follows your university's syllabus and unit order.

Will we work with real boards?

Yes. Demonstrations use Arduino, ESP8266 and Raspberry Pi with common sensors, and data is sent to a free IoT cloud dashboard.

Which syllabus does this Machine Learning course follow?

It follows the AKTU (Lucknow) MCA syllabus for Semester 4. If your own university's syllabus differs, the centre follows your university's syllabus and unit order.

Will we code ML models?

Yes. Each algorithm is also shown in Python with scikit-learn on small data sets so students can see how it works.

Download the syllabus (PDF)

Tell us who you are — your copy is made with your name, and our counsellor can help you choose the right course and centre.

Want to join right away? Enquire now — our centre will call you.