DATA SCIENCE (AD-404) - Important Questions

Explain graphical methods of data representation such as pie chart, bar graph and histogram with examples.

Explain the differences between structured and unstructured data with examples.

What are structured data and unstructured data? Explain their characteristics, advantages, disadvantages and real-world applications with examples.

Explain the Data Science road map in detail with each phase and suitable examples.

Differentiate between basic probability and conditional probability with suitable numerical examples.

Explain what Data Wrangling is and its importance in data science.

Draw two suitable graphical representations of data for a given sample dataset.

Calculate and interpret the mean, median and mode for the given quantitative data: 5, 8, 12, 15, 20, 20, 25.

What is Data Science? Explain the difference between structured and unstructured data.

Explain the methods to evaluate quantitative data.

Discuss exploratory data analysis.

Explain the concept of conditional probability.

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.

Explain data validation techniques in Excel and their role in improving data accuracy and reducing errors.

Explain different Data Visualization techniques in detail.

Discuss the importance of Pivot Tables and Charts in business reporting.

What is a macro in Excel and how to create/use it with an example?

Explain how t-SNE is used to visualize high-dimensional data with an unstructured data example.

What is data visualization? Discuss feature map visualization technique.

Explain predictive and prescriptive data analysis methods.

List and explain the various charts in Excel.

Briefly explain descriptive data analysis.

What is data visualization? Discuss box plots and Histograms.

Explain the concept of logistic regression for binary classification.

Explain Multiple Hypothesis Testing and Parameter Estimation Methods.

What are L1 and L2 Regularization? Explain in detail.

Explain Bayesian Statistics. Compare Bayesian Statistics with Classical Statistics using examples.

Explain the usage and operations of VLOOKUP and XLOOKUP for data analysis.

Explain how SUMIFS, INDEX + MATCH and Dynamic Array Formulas can be used for customer segmentation in Excel.

Explain the data analysis tools SUMIFS and SUMPRODUCT.

What is correlation analysis? Explain.

Explain the role of regression analysis in data science.

Discuss in detail about losso regression.

What is pivoting? Explain the types of pivoting.

Explain Pandas in Python in detail with example.

Write Python programs to generate Pie Chart and Bar Chart.

Explain different Performance Evaluation Metrics used in Machine Learning.

Explain what Random Forest is, with a suitable example.

Explain the difference between Type I and Type II errors in multiple hypothesis testing with an example.

Explain the structure and features of a Pandas DataFrame. How is it different from a NumPy array?

Illustrate how to create pie charts, bar charts, histograms, and scatter plots in Python using matplotlib or seaborn.

Write the logic to perform string operations in Pandas and apply regular expressions to extract specific patterns.

Write about String manipulation operations in Pandas.

Give an overview of Gradient Bootstrap.

Explain the procedure to create scatter plots using Python.

List and explain visualization tools in Python.

Explain the role of next-generation data scientists.

Explain Business Intelligence and give an overview of modern Business Intelligence tools.

What is Business Intelligence, its types and components?

Explain the ethical issues in Business Intelligence / Data Science

Define Business Intelligence, explain its types and explain how BI supports strategic decision-making.

Compare two modern Business Intelligence tools based on usability, key features, and data handling capabilities.

What is Business Intelligence? Explain the types of Business Intelligence.

Explain the major issues and challenges in Business Intelligence.

What is unfair discrimination? Explain.

Explain the following in detail: Reinforcing human biases

Explain the following in detail: Modern Business Intelligence

Explain the security issues in Data Science

Write short notes on any two of the following: