AKTU Data Analytics · Important Questions 2026Source: ryzenstudy.com PYQs
BCS052 Data Analytics Important Questions 2026 (AKTU)
Repeat analysis + most-asked questions, unit-wise
What we did: built from all 5 AKTU Data Analytics papers (2021-22–2025-26): 117 questions grouped into 94 distinct questions, 18 of which were asked in more than one paper.
- of marks were repeat questions
- 21%
- of marks were repeat questions
- questions asked in 2+ papers
- 18
- questions asked in 2+ papers
- papers analysed (2021-22–2025-26)
- 5
- papers analysed (2021-22–2025-26)
- units, mapped to current syllabus
- 5
- units, mapped to current syllabus
How much of the paper repeats?
- Repeats are real: when we checked each newer BCS052 paper against the papers before it, on average 21% of its marks were questions already asked in an earlier paper.
- 2023-24 paper: 10% of marks were repeats (0 of 2 repeated questions were in our top list).
- 2024-25 paper: 35% of marks were repeats (2 of 8 repeated questions were in our top list).
- 2025-26 paper: 19% of marks were repeats (2 of 4 repeated questions were in our top list).
- Topic weightage is a guide, not a guarantee: our Tier 1 + Tier 2 topics covered 47% of the next paper's marks — picking the same number of topics at random would have covered about 45%.
Across 10 subjects and 23 papers we tested, 43% of marks repeated earlier questions on average.
1.Unit-wise most-asked questions
Ranked by how many papers asked them, then by how recently. Click a paper to open it.
Unit 1: Introduction to Data Analytics · Data Analytics Lifecycle
19% of past marks| Question | Asked |
|---|---|
| Explain the phases of the Data Analytics Lifecycle with objectives of each phase | Asked 2× |
| List any two characteristics of Big Data | Asked 2× |
| Explain the different categories of data analytics with examples | Asked 2× |
| Differentiate between analysis and reporting in the context of data analytics. How do these two aspects contribute to the overall understanding of data? | Asked 2× |
Unit 2: Data Analysis
23% of past marks| Question | Asked |
|---|---|
| Describe the concept of Lasso Regression | Asked 2× |
| What are the different kernel methods of Data Analytics? | Asked 2× |
| Explain Bayesian modeling and the role of prior and posterior probabilities 7 marks2025-26 Q2(c) | Asked 1× |
| The following data represents study hours (X) and scores (Y): (i) Find the linear regression equation. (ii) Predict Y when X = (see original paper) 7 marks2025-26 Q2(b) | Asked 1× |
Unit 3: Mining Data Streams
18% of past marks| Question | Asked |
|---|---|
| Explain and apply Flajolet-Martin algorithm on the following stream of data to identify unique elements in the stream. Given: h(x)=(6x+1) mod (see original paper) | Asked 2× |
| Differentiate between Stream Processing and Traditional Data Processing | Asked 2× |
| What are the different components of a general stream processing model? List few sources of streaming data | Asked 2× |
| A data stream generates 120,000 records per minute. If only 35% data is retained using a decaying window, calculate the number of records retained per minute and justify the use of windowing 7 marks2025-26 Q2(d) | Asked 1× |
Unit 4: Frequent Itemsets and Clustering
19% of past marks| Question | Asked |
|---|---|
| Differentiate between CLIQUE and ProCLUS clustering | Asked 3× |
| Compare hierarchical clustering and K-means clustering with suitable applications | Asked 2× |
| Given transactions: T1 {A, B, C}, T2 {A, B}, T3 {A, C}, T4 {B, C}, T5 {A, B, C} If minimum support = 60%, find all frequent item sets and show pruning | Asked 2× |
| Why PCY algorithm is preferred over Apriori algorithm? | Asked 2× |
Unit 5: Frame Works and Visualization
21% of past marks| Question | Asked |
|---|---|
| Differentiate between NoSQL database and a Relational database. Identify when one should use a NoSQL database instead of a relational database with a suitable example | Asked 3× |
| Draw the architecture of HIVE with its features | Asked 2× |
| Write names of two visualization tools | Asked 2× |
| Design a Hadoop-based framework for storing and processing large-scale data 7 marks2025-26 Q6(b) | Asked 1× |
2.Topic weightage: which topics carry the marks
Blue = Tier 1 + Tier 2 (topics that carried ~80% of past marks). Grey = the rest. It shows where marks have gone, not a promise of what comes next.
Introduction to Big Data platform · U1
5.9% (cum 6%)
Sharding and NoSQL databases · U5
5.8% (cum 12%)
Regression modeling · U2
4% (cum 16%)
Counting oneness in a window · U3
3.8% (cum 20%)
Hierarchical clustering · U4
3.8% (cum 23%)
Descriptive statistics and EDA · U5
3.7% (cum 27%)
Neural networks learning and generalisation · U2
3.6% (cum 31%)
Support vector and kernel methods · U2
3.4% (cum 34%)
Analytic process and tools · U1
3.4% (cum 37%)
Bayesian modeling and inference · U2
3.3% (cum 41%)
Stream data model and architecture · U3
3.2% (cum 44%)
Apriori algorithm · U4
3.2% (cum 47%)
CLIQUE and ProCLUS · U4
3.1% (cum 50%)
Fuzzy logic models · U2
3% (cum 53%)
Counting distinct elements in a stream · U3
2.9% (cum 56%)
Pig, Hive and HBase · U5
2.8% (cum 59%)
K-means clustering · U4
2.5% (cum 61%)
MapReduce framework · U5
2.4% (cum 64%)
Market based modelling · U4
2.4% (cum 66%)
Data analytics lifecycle phases · U1
2.3% (cum 69%)
Limited pass algorithm · U4
2.3% (cum 71%)
Applications of data analytics · U1
2.3% (cum 73%)
RTAP applications and case studies · U3
2.3% (cum 75%)
Principal component analysis · U2
2.2% (cum 78%)
Interaction techniques and systems · U5
2% (cum 80%)
Hadoop and HDFS · U5
2% (cum 82%)
Multivariate analysis · U2
1.8% (cum 83%)
Analysis vs reporting · U1
1.6% (cum 85%)
Sampling data in a stream · U3
1.4% (cum 86%)
Filtering streams · U3
1.4% (cum 88%)
Decaying window · U3
1.2% (cum 89%)
Characteristics of data · U1
1.2% (cum 90%)
Mining frequent itemsets · U4
1.2% (cum 91%)
Introduction to streams concepts · U3
1.1% (cum 93%)
R data import and export · U5
1% (cum 94%)
Analysis of time series · U2
1% (cum 95%)
Visual data analysis techniques · U5
1% (cum 96%)
Classification of data · U1
1% (cum 97%)
Rule induction · U2
0.8% (cum 97%)
Need of data analytics · U1
0.6% (cum 98%)
Stream computing · U3
0.6% (cum 98%)
R graphical user interfaces · U5
0.4% (cum 99%)
Sources and nature of data · U1
0.4% (cum 99%)
Handling large data sets · U4
0.4% (cum 100%)
Frequent itemsets in a stream · U4
0.4% (cum 100%)
| Topic | Papers | Share |
|---|---|---|
| Introduction to Big Data platform · Unit 1 · Tier 1 | 5/5 | 5.9% |
| Sharding and NoSQL databases · Unit 5 · Tier 1 | 4/5 | 5.8% |
| Regression modeling · Unit 2 · Tier 1 | 4/5 | 4% |
| Counting oneness in a window · Unit 3 · Tier 1 | 4/5 | 3.8% |
| Hierarchical clustering · Unit 4 · Tier 1 | 5/5 | 3.8% |
| Descriptive statistics and EDA · Unit 5 · Tier 1 | 3/5 | 3.7% |
| Neural networks learning and generalisation · Unit 2 · Tier 1 | 4/5 | 3.6% |
| Support vector and kernel methods · Unit 2 · Tier 1 | 4/5 | 3.4% |
| Analytic process and tools · Unit 1 · Tier 1 | 3/5 | 3.4% |
| Bayesian modeling and inference · Unit 2 · Tier 1 | 3/5 | 3.3% |
| Stream data model and architecture · Unit 3 · Tier 1 | 2/5 | 3.2% |
| Apriori algorithm · Unit 4 · Tier 1 | 3/5 | 3.2% |
| CLIQUE and ProCLUS · Unit 4 · Tier 1 | 3/5 | 3.1% |
| Fuzzy logic models · Unit 2 · Tier 2 | 3/5 | 3% |
| Counting distinct elements in a stream · Unit 3 · Tier 2 | 3/5 | 2.9% |
| Pig, Hive and HBase · Unit 5 · Tier 2 | 3/5 | 2.8% |
| K-means clustering · Unit 4 · Tier 2 | 1/5 | 2.5% |
| MapReduce framework · Unit 5 · Tier 2 | 3/5 | 2.4% |
| Market based modelling · Unit 4 · Tier 2 | 2/5 | 2.4% |
| Data analytics lifecycle phases · Unit 1 · Tier 2 | 2/5 | 2.3% |
| Limited pass algorithm · Unit 4 · Tier 2 | 1/5 | 2.3% |
| Applications of data analytics · Unit 1 · Tier 2 | 2/5 | 2.3% |
| RTAP applications and case studies · Unit 3 · Tier 2 | 2/5 | 2.3% |
| Principal component analysis · Unit 2 · Tier 2 | 2/5 | 2.2% |
| Interaction techniques and systems · Unit 5 · Tier 2 | 2/5 | 2% |
| Hadoop and HDFS · Unit 5 · Tier 2 | 2/5 | 2% |
| Multivariate analysis · Unit 2 · Tier 3 | 2/5 | 1.8% |
| Analysis vs reporting · Unit 1 · Tier 3 | 2/5 | 1.6% |
| Sampling data in a stream · Unit 3 · Tier 3 | 2/5 | 1.4% |
| Filtering streams · Unit 3 · Tier 3 | 2/5 | 1.4% |
| Decaying window · Unit 3 · Tier 3 | 1/5 | 1.2% |
| Characteristics of data · Unit 1 · Tier 3 | 1/5 | 1.2% |
| Mining frequent itemsets · Unit 4 · Tier 3 | 2/5 | 1.2% |
| Introduction to streams concepts · Unit 3 · Tier 3 | 2/5 | 1.1% |
| R data import and export · Unit 5 · Tier 3 | 1/5 | 1% |
| Analysis of time series · Unit 2 · Tier 3 | 1/5 | 1% |
| Visual data analysis techniques · Unit 5 · Tier 3 | 2/5 | 1% |
| Classification of data · Unit 1 · Tier 3 | 1/5 | 1% |
| Rule induction · Unit 2 · Tier 3 | 1/5 | 0.8% |
| Need of data analytics · Unit 1 · Tier 3 | 1/5 | 0.6% |
| Stream computing · Unit 3 · Tier 3 | 1/5 | 0.6% |
| R graphical user interfaces · Unit 5 · Tier 3 | 1/5 | 0.4% |
| Sources and nature of data · Unit 1 · Tier 3 | 1/5 | 0.4% |
| Handling large data sets · Unit 4 · Tier 3 | 1/5 | 0.4% |
| Frequent itemsets in a stream · Unit 4 · Tier 3 | 1/5 | 0.4% |
3.Paper pattern (2025-26)
| Section | Questions | Rule |
|---|---|---|
| A | 6 × 2 marks | Attempt all |
| B | 5 × 7 marks | Attempt any 3 |
| C | 10 × 7 marks | Attempt any 1 |
4.In the syllabus, never asked yet
Low priority, but read them once — a new paper can always start here.
- Key roles in analytic projects · U1
- Stochastic search methods · U2
- Estimating moments · U3
- Clustering high dimensional data · U4
- MapR and S3 · U5
5.Study order
- 1Introduction to Big Data platform (Unit 1, Tier 1)
- 2Sharding and NoSQL databases (Unit 5, Tier 1)
- 3Regression modeling (Unit 2, Tier 1)
- 4Counting oneness in a window (Unit 3, Tier 1)
- 5Hierarchical clustering (Unit 4, Tier 1)
- 6Descriptive statistics and EDA (Unit 5, Tier 1)
- 7Neural networks learning and generalisation (Unit 2, Tier 1)
- 8Support vector and kernel methods (Unit 2, Tier 1)
- 9Analytic process and tools (Unit 1, Tier 1)
- 10Bayesian modeling and inference (Unit 2, Tier 1)
- 11Stream data model and architecture (Unit 3, Tier 1)
- 12Apriori algorithm (Unit 4, Tier 1)
- 13CLIQUE and ProCLUS (Unit 4, Tier 1)
- 14Fuzzy logic models (Unit 2, Tier 2)
- 15Counting distinct elements in a stream (Unit 3, Tier 2)
- 16Pig, Hive and HBase (Unit 5, Tier 2)
- 17K-means clustering (Unit 4, Tier 2)
- 18MapReduce framework (Unit 5, Tier 2)
- 19Market based modelling (Unit 4, Tier 2)
- 20Data analytics lifecycle phases (Unit 1, Tier 2)
- 21Limited pass algorithm (Unit 4, Tier 2)
- 22Applications of data analytics (Unit 1, Tier 2)
- 23RTAP applications and case studies (Unit 3, Tier 2)
- 24Principal component analysis (Unit 2, Tier 2)
- 25Interaction techniques and systems (Unit 5, Tier 2)
- 26Hadoop and HDFS (Unit 5, Tier 2)
FAQ
Which units of BCS052 carry the most marks?
Unit 2 (Data Analysis, 23% of past marks) and Unit 5 (Frame Works and Visualization, 21% of past marks).
Do AKTU Data Analytics questions repeat?
Yes. On average 21% of the marks in each newer BCS052 paper came from questions already asked in an earlier paper, and 18 questions have appeared in more than one paper.
How is this list made?
Every question from the 5 papers (2021-22–2025-26) was mapped to the current AKTU syllabus and grouped with the same question from other years. Questions are ranked by how many papers they appeared in, then by how recently.
Study this subject
Note: this is based on past AKTU papers, not a guarantee. Questions are mapped to the current syllabus; topics that are no longer in the syllabus are left out.