AD-404 RGPV B.Tech
DATA SCIENCE (DS)
Previous year papers and the official syllabus are free to read, no account needed.
Previous Year Papers
6 papers, latest Jun 2026
- AD-404 paper, Jun 2026
- AD-404 paper, Jun 2025
- AD-404 paper, Dec 2024
- AD-404 paper, Jun 2024
- AD-404 paper, May 2024
- AD-404 paper, Jun 2023
Syllabus, notes and important questions for AD-404
Syllabus units
Topics as the official RGPV syllabus lists them.
Unit 1 Introduction to Data Science 6 topics
- Types of Data: structured and unstructured data
- Data Science Road Map: Frame the Problem; Understand the Data; Data Wrangling; Exploratory Analysis; Extract Features; Model and Deploy Code
- Graphical Summaries of Data: Pie Chart, Bar Graph, Pareto Chart, Histogram
- Measures of central tendency of Quantitative Data: Mean, Median, Mode
- Measures of Variability of Quantitative Data: Range, Standard Deviation and Variance
- Probability: Introduction to Probability, Conditional Probability
Unit 2 Unstructured Data Analytics 4 topics
- Importance of Unstructured Data
- Unstructured Data Analytics: Descriptive, diagnostic, predictive and prescriptive data analytics based on case study
- Data Visualization: boxplots, histograms, scatterplots, features map visualization, t-SNE
- Overview of Advance Excel: Introduction, Data validation, Introduction to charts, pivot table, Scenario manager, Protecting data, Excel macro basics, Introduction to macros
Unit 3 Statistical & Probabilistic analysis 8 topics
- Statistical & Probabilistic analysis of Data
- Multiple hypothesis testing
- Parameter Estimation methods
- Confidence intervals
- Correlation & Regression analysis, logistic regression
- Shrinkage Methods, Lasso Regression, Bayesian statistics
- L1 and L2 regularizations
- Powerful Data Analysis techniques: SUMIFS, SUMPRODUCT, VLOOKUP/XLOOKUP, INDEX + MATCH, handling formula errors, dynamic array formulas, circular references, formula auditing, pivoting
Unit 4 Data Manipulation With Pandas 4 topics
- Introduction to Pandas: understanding DataFrame, handling missing values, data operations, string manipulation, regular expressions, data cleaning, outlier detection
- Visualization tools in Python: representation of pie chart, bar chart, histogram, scatterplots
- Data analysis performance metrics: ROC curve, types of errors, overfitting & underfitting, model evaluation techniques (holdout, random sampling, cross‑validation, bootstrap)
- Ensemble methods: bagging & boosting, gradient boosting, random forests, committee machines
Unit 5 Introduction to Business Intelligence 3 topics
- Introduction to Business Intelligence, types of Business Intelligence, modern Business Intelligence tools
- Data Science and Ethical Issues: unfair discrimination, reinforcing human biases, lack of transparency
- Discussions on privacy, security, ethics, and the role of next‑generation data scientists
Exam insights
Counted from 5 past AD-404 papers.
Marks asked per paper, by unit
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Unit 1 Introduction to Data Science 27 marks
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Unit 2 Unstructured Data Analytics 20 marks
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Unit 3 Statistical & Probabilistic analysis of Data 19 marks
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Unit 4 Data Manipulation With Pandas 23 marks
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Unit 5 Introduction to Business Intelligence 21 marks
Average marks of the questions from each unit in one paper. You answer only some of them, so the total is more than the paper’s maximum.
Asked again and again
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Explain the importance of unstructured data and unstructured data analytics in modern organizations with suitable examples and uses in domains such as healthcare, finance and social media.
Asked 3 times: 2026, 2025, 2024
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Explain the role of next-generation data scientists.
Asked 3 times: 2026, 2024, 2023
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Explain graphical methods of data representation such as pie chart, bar graph and histogram with examples.
Asked 2 times: 2026, 2024