Your roadmap · आपका रोडमैप
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Admission & first meetingप्रवेश और पहली मुलाकातEnquire at your nearest NDSS centre, meet the teacher, fix the batch time and get your study material.
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Part 1 of the syllabusपाठ्यक्रम भाग 1Unit-wise coaching with notes and doubt classes.
Unit 1: Data Warehousing and Multidimensional ModelUnit 2: Data Warehouse Process, Technology and OLAPUnit 3: Data Mining and Pre-processing
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Part 2 of the syllabusपाठ्यक्रम भाग 2Unit-wise coaching with notes and doubt classes.
Unit 4: Classification and Association RulesUnit 5: Clustering and Advanced Mining
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Unit tests & assignmentsयूनिट टेस्टRegular tests in the university / board pattern.
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Semester 5 exam (university / board)विश्वविद्यालय / बोर्ड परीक्षाPrevious papers and full revision before the exam.
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Online tests & resultऑनलाइन टेस्ट और रिज़ल्टTake the tests online; your marks and result show in your student login and at the centre.
सिलेबस · पाठ्यक्रम
1 subject · 5 chapters · 27 topics
01Unit 1: Data Warehousing and Multidimensional Model
6 टॉपिक+
- Definition and components of a data warehouse
- Building a data warehouse and mapping to multiprocessor architecture
- Database system vs data warehouse
- Multidimensional data model and data cubes
- Star, snowflake and fact constellation schemas
- Concept hierarchy, three-tier architecture and data marts
02Unit 2: Data Warehouse Process, Technology and OLAP
4 टॉपिक+
- Warehousing strategy and data management
- OLAP servers: ROLAP, MOLAP, HOLAP
- OLAP operations: roll-up, drill-down, slice, dice, pivot
- Data warehouse backup, testing and tuning
03Unit 3: Data Mining and Pre-processing
5 टॉपिक+
- Data mining definition and functionalities
- Data cleaning: missing values and noisy data (binning, regression, clustering)
- Data integration and transformation (normalisation)
- Data reduction: cube aggregation, dimensionality reduction, compression, numerosity reduction
- Discretisation and concept hierarchy generation
04Unit 4: Classification and Association Rules
6 टॉपिक+
- Data generalisation and attribute relevance analysis
- Statistical, distance-based and decision tree classification
- Mining class comparisons
- Association rules: support and confidence
- Apriori algorithm
- FP-growth
05Unit 5: Clustering and Advanced Mining
6 टॉपिक+
- Similarity and distance measures
- Partitioning: K-means and K-medoids
- Hierarchical clustering: agglomerative, divisive, CURE, Chameleon
- Density based: DBSCAN, OPTICS; grid based: STING, CLIQUE
- Model based clustering
- Time series, web and spatial mining
About this course
आप यह कर पाएँगे
- Differentiate a data warehouse from an operational database and explain warehouse architecture
- Design star, snowflake and fact constellation schemas and data cubes
- Apply OLAP operations: roll-up, drill-down, slice, dice and pivot; compare ROLAP, MOLAP and HOLAP
- Pre-process data: cleaning, integration, transformation, reduction and discretisation
- Apply decision tree classification and Apriori / FP-growth association mining
- Apply partitioning, hierarchical, density and grid based clustering; explain web, spatial and time series mining
यह किसके लिए है
- B.Tech Information Technology students of Semester 5 (AKTU)
- Students of other universities studying the same subject who want unit-wise coaching
आपको क्या चाहिए
- Database Management System
- Basic statistics
Online tests & result
Practice and unit tests online; marks and result show in your student login and at the centre.
Student login →
सवाल
Which syllabus does the Data Warehousing and Data Mining course follow?
It follows the AKTU (Dr. A.P.J. Abdul Kalam Technical University, Lucknow) B.Tech syllabus for Data Warehousing and Data Mining. If your own university's syllabus differs, the centre follows your university syllabus.
Will this help in the university examination?
Yes. Teaching is unit-wise as per the AKTU scheme, with previous-year question practice, MCQ tests and doubt sessions before the semester exam.
Are tools such as Weka used?
Yes. Along with theory and numericals, Weka / Python demonstrations of classification, association and clustering are shown.
