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: Introduction to Data Analytics and LifecycleUnit 2: Data Analysis TechniquesUnit 3: Mining Data Streams
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Part 2 of the syllabusपाठ्यक्रम भाग 2Unit-wise coaching with notes and doubt classes.
Unit 4: Frequent Itemsets and ClusteringUnit 5: Frameworks, Visualisation and R
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Unit tests & assignmentsयूनिट टेस्टRegular tests in the university / board pattern.
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Semester 6 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 · 29 topics
01Unit 1: Introduction to Data Analytics and Lifecycle
6 टॉपिक+
- Sources and nature of data: structured, semi-structured, unstructured
- Need of data analytics and analytic scalability
- Analytic process, tools; analysis vs reporting
- Applications of data analytics
- Data analytics lifecycle: discovery, data preparation, model planning, model building, communicating results, operationalisation
- Key roles in an analytics project
02Unit 2: Data Analysis Techniques
6 टॉपिक+
- Regression modelling and multivariate analysis
- Bayesian modelling, inference and Bayesian networks
- SVM and kernel methods
- Time series analysis
- Rule induction and neural networks
- PCA, fuzzy logic and stochastic search methods
03Unit 3: Mining Data Streams
6 टॉपिक+
- Stream concepts, data model and architecture
- Sampling data in a stream
- Filtering streams: Bloom filter
- Counting distinct elements: Flajolet-Martin
- Estimating moments and DGIM for counting ones in a window
- Decaying window, RTAP; sentiment analysis and stock market case studies
04Unit 4: Frequent Itemsets and Clustering
6 टॉपिक+
- Market basket model and frequent itemsets
- Apriori algorithm
- Handling large datasets in main memory and limited-pass algorithms
- Hierarchical and K-means clustering
- Clustering high-dimensional data: CLIQUE, PROCLUS
- Clustering in non-Euclidean space and for streams
05Unit 5: Frameworks, Visualisation and R
5 टॉपिक+
- MapReduce, Hadoop, Pig, Hive, HBase, MapR
- Sharding, NoSQL databases, S3 and HDFS
- Visual data analysis and interaction techniques
- Introduction to R: GUI, data import / export, data types
- Descriptive statistics and exploratory data analysis in R
About this course
आप यह कर पाएँगे
- Classify data and explain the data analytics lifecycle and key roles
- Apply regression, Bayesian, SVM, time series, neural network, PCA and fuzzy techniques to data
- Explain stream data models and algorithms: sampling, Bloom filter, Flajolet-Martin, DGIM
- Mine frequent itemsets with Apriori and apply hierarchical, K-means and high-dimensional clustering
- Explain Hadoop, Pig, Hive, HBase, sharding and NoSQL for analytics
- Perform exploratory data analysis and visualisation in R
यह किसके लिए है
- B.Tech Information Technology students of Semester 6 (AKTU)
- Students of other universities studying the same subject who want unit-wise coaching
आपको क्या चाहिए
- Database Management System and basic statistics
- Basic Python or R
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 Analytics course follow?
It follows the AKTU (Dr. A.P.J. Abdul Kalam Technical University, Lucknow) B.Tech syllabus for Data Analytics. 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.
Is this subject only theory?
The exam is theory and numericals, but algorithms such as Apriori, K-means and Bloom filters are also demonstrated in R / Python.
