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 AnalyticsUnit 2: Data Analysis MethodsUnit 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 and Visualisation
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Unit tests & assignmentsयूनिट टेस्टRegular tests in the university / board pattern.
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Semester 4 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.
Syllabus · पाठ्यक्रम
1 subject · 5 chapters · 29 topics
01Unit 1: Introduction to Data Analytics
6 topics+
- Sources and nature of data
- Structured, semi-structured and unstructured data
- Characteristics of big data: the Vs
- Analysis versus reporting; modern analytic tools
- Data analytics life cycle: discovery, preparation, model planning, model building, communicating results, operationalisation
- Key roles in an analytics project
02Unit 2: Data Analysis Methods
6 topics+
- Regression modelling
- Multivariate analysis
- Bayesian modelling and inference
- Support vector machines and kernel methods
- Time series analysis
- Principal component analysis and rule induction
03Unit 3: Mining Data Streams
6 topics+
- Stream data model and architecture
- Sampling data in a stream
- Filtering streams: Bloom filter
- Counting distinct elements: Flajolet-Martin algorithm
- Counting ones in a window: DGIM algorithm
- Decaying windows and real-time analytics applications
04Unit 4: Frequent Itemsets and Clustering
6 topics+
- Market basket analysis
- Support, confidence and association rules
- Apriori algorithm
- PCY and SON algorithms
- Clustering: k-means and hierarchical
- CLIQUE and PROCLUS; clustering in non-Euclidean spaces
05Unit 5: Frameworks and Visualisation
5 topics+
- Hadoop, HDFS and MapReduce
- Pig and Hive
- HBase, NoSQL databases and sharding
- Data visualisation techniques
- Introduction to R for analytics
About this course
You will be able to
- Classify data sources and describe big data characteristics and the data analytics life cycle
- Apply regression, multivariate analysis, Bayesian methods and PCA to data
- Explain stream data models and algorithms such as Bloom filters, Flajolet-Martin and DGIM
- Find frequent itemsets and association rules using Apriori and its variants
- Group data using k-means, hierarchical and grid-based clustering
- Describe Hadoop, MapReduce, Pig, Hive, HBase and NoSQL tools and visualise results
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.
