BCDS501 Introduction to Data Analytics and Visualization Syllabus
AKTU B.Tech · 5th Semester · Unit-wise syllabus (effective 2024-25)
BCDS501 Introduction to Data Analytics and Visualization (also written as KCDS501) is a subject in Semester 5 of the AKTU B.Tech programme, prescribed by Dr. A.P.J. Abdul Kalam Technical University. It carries 4 credits with an L-T-P of 3-1-0. The syllabus is divided into 5 units: Introduction to Data Analytics and Data Analytics Lifecycle, Data Analysis, Mining Data Streams, Frequent Itemsets and Clustering. The complete unit-wise topic list, course outcomes and reference books are given below, taken from the official AKTU syllabus effective from 2024-25.
BCDS501 Subject Details
| Subject Code | BCDS501 / KCDS501 |
|---|---|
| Subject Name | Introduction to Data Analytics and Visualization |
| University | Dr. A.P.J. Abdul Kalam Technical University (AKTU) |
| Course | B.Tech (Bachelor of Technology) |
| Year / Semester | 3rd Year · 5th Semester |
| Credits | 4 |
| L-T-P | 3-1-0 |
| Total Units | 5 |
| Scheme Effective From | 2024-25 |
| Last Verified | 2026-08-19 |
BCDS501 Introduction to Data Analytics and Visualization Unit-Wise Syllabus
Official AKTU syllabus (effective 2024-25) — 5 units.
Unit 1: Introduction to Data Analytics · Data Analytics Lifecycle
Introduction to Data Analytics: Sources and nature of data, classification of data (structured, semi-structured, unstructured), characteristics of data, introduction to Big Data platform, need of data analytics, evolution of analytic scalability, analytic process and tools, analysis vs reporting, modern data analytic tools, applications of data analytics. Data Analytics Lifecycle: Need, key roles for successful analytic projects, various phases of data analytics lifecycle – discovery, data preparation, model planning, model building, communicating results, operationalization.
Unit 2: Data Analysis
Data Analysis: Regression modeling, multivariate analysis, Bayesian modeling, inference and Bayesian networks, support vector and kernel methods, analysis of time series: linear systems analysis & nonlinear dynamics, rule induction, neural networks: learning and generalisation, competitive learning, principal component analysis and neural networks, fuzzy logic: extracting fuzzy models from data, fuzzy decision trees, stochastic search methods.
Unit 3: Mining Data Streams
Mining Data Streams: Introduction to streams concepts, stream data model and architecture, stream computing, sampling data in a stream, filtering streams, counting distinct elements in a stream, estimating moments, counting oneness in a window, decaying window, Real-time Analytics Platform (RTAP) applications, Case studies – real time sentiment analysis, stock market predictions.
Unit 4: Frequent Itemsets and Clustering
Frequent Itemsets and Clustering: Mining frequent itemsets, market based modelling, Apriori algorithm, handling large data sets in main memory, limited pass algorithm, counting frequent itemsets in a stream, clustering techniques: hierarchical, K-means, clustering high dimensional data, CLIQUE and ProCLUS, frequent pattern based clustering methods, clustering in non-euclidean space, clustering for streams and parallelism.
Unit 5
Introduction to Visualization and Stages – Computational Support – Issues – Different Types of Tasks – Data representation – Limitation: Display Space- Rendering Time – Navigation Links. Human Vision – Space Limitation – Time Limitations – Design – Exploration of Complex Information Space – Figure Caption in Visual Interface – Visual Objects and Data Objects - Space Perception and Data in Space – Images, Narrative and Gestures for Explanation.
BCDS501 Course Outcomes
- At the end of course, the student will be able to understand
- CO 1 Describe the life cycle phases of Data Analytics through discovery, planning and building. K3
- CO 2 Understand and apply Data Analysis Techniques. K3, K4
- CO 3 Implement various Data streams. K2, K3
- CO 4 Understand item sets, Clustering, frame works & Visualizations. K2, K4
- CO 5 Understand the Data Visualizations & Human Vision K2, K3
BCDS501 Reference Books
- Michael Berthold, David J. Hand, Intelligent Data Analysis, Springer
- Anand Rajaraman and Jeffrey David Ullman, Mining of Massive Datasets, Cambridge University Press.
- Bill Franks, Taming the Big Data Tidal wave: Finding Opportunities in Huge Data Streams with Advanced
- Michael Minelli, Michelle Chambers, and Ambiga Dhiraj, "Big Data, Big Analytics: Emerging Business
- David Dietrich, Barry Heller, Beibei Yang, “Data Science and Big Data Analytics”, EMC Education Series,
- Frank J Ohlhorst, “Big Data Analytics: Turning Big Data into Big Money”, Wiley and SAS Business Series
- Colleen Mccue, “Data Mining and Predictive Analysis: Intelligence Gathering and Crime Analysis”, Elsevier
- Anil Maheshwari, “Data Analytics”, McGraw Hill Education
- Paul Zikopoulos, Chris Eaton, Paul Zikopoulos, “Understanding Big Data: Analytics for Enterprise Class
- Trevor Hastie, Robert Tibshirani, Jerome Friedman, "The Elements of Statistical Learning", Springer
- Mark Gardner, “Beginning R: The Statistical Programming Language”, Wrox Publication
- Pete Warden, Big Data Glossary, O’Reilly
- Glenn J. Myatt, Making Sense of Data, John Wiley & Sons
- Pete Warden, Big Data Glossary, O’Reilly.
- Peter Bühlmann, Petros Drineas, Michael Kane, Mark van der Laan, "Handbook of Big Data", CRC Press
- Jiawei Han, Micheline Kamber “Data Mining Concepts and Techniques”, Second Edition, ElsevierRobert
Frequently Asked Questions about BCDS501
What is the syllabus of BCDS501 Introduction to Data Analytics and Visualization?
The AKTU BCDS501 Introduction to Data Analytics and Visualization syllabus is divided into 5 units: Introduction to Data Analytics and Data Analytics Lifecycle; Data Analysis; Mining Data Streams; Frequent Itemsets and Clustering. The complete topic list for each unit is published on this page, taken from the official Dr. A.P.J. Abdul Kalam Technical University syllabus effective from 2024-25.
How many units are there in BCDS501?
BCDS501 Introduction to Data Analytics and Visualization has 5 units in the AKTU syllabus, with an L-T-P (Lecture-Tutorial-Practical) structure of 3-1-0 and 4 credits.
Is BCDS501 the same subject as KCDS501?
Yes. AKTU renumbered its B.Tech subject codes from the 2022-23 session onwards, so the same subject appears as BCDS501 and KCDS501 depending on the scheme year. The syllabus content is the same.
Which semester is BCDS501 taught in?
BCDS501 Introduction to Data Analytics and Visualization is taught in Semester 5 of the 3rd year of the AKTU B.Tech programme.
How many credits is BCDS501 worth?
BCDS501 Introduction to Data Analytics and Visualization carries 4 credits in the AKTU evaluation scheme, with an L-T-P of 3-1-0.
Where can I download BCDS501 previous year question papers?
AKTU B.Tech previous year question papers for every semester are available to download free on Ryzenstudy, with no login or registration required.
Is this the official AKTU syllabus for BCDS501?
Yes. Every topic on this page is taken from the official Dr. A.P.J. Abdul Kalam Technical University syllabus document (AKTU B.Tech 3rd Year Computer Science and Engineering (Data Science) Syllabus), effective from 2024-25. A direct link to the original AKTU PDF is provided at the top of this page. Last verified on 2026-08-19.
Related AKTU Syllabus
Source: Dr. A.P.J. Abdul Kalam Technical University official syllabus — AKTU B.Tech 3rd Year Computer Science and Engineering (Data Science) Syllabus, effective from 2024-25. Ryzenstudy is not affiliated with AKTU. Content last verified on 2026-08-19.