Data and Visual Analytics (AL-603 (B)) - Important Questions

Define variables and explain different methods of data categorization

Explain data indexing, different indexing techniques and their importance in efficient data retrieval.

Explain the importance of measures of dispersion in statistical analysis and decision making.

Explain the concept of Sampling Distribution. Discuss its characteristics, importance and applications in statistical analysis.

Explain what is meant by measures of central tendency and describe them in detail.

Describe the measures of dispersion (measures of location of dispersion) in detail.

Explain various levels of measurements in detail.

What are statistical inferences? Explain different statistical inferences in detail.

Explain data management and indexing.

Discuss the role of Maximum Likelihood Estimation in Machine Learning and Data Analytics.

Explain Bayesian Networks. Discuss their structure and working.

Write the Short Note:

Explain the Chi-Square test, including how to calculate it with an example.

Describe the maximum likelihood test in detail with an example.

What is Multivariate analysis? Describe in detail.

We have the potato yield from 12 different farms. We know that the standard potato yield for the given variety is $\mu = 20$. $X = [21.5, 24.5, 18.5, 17.2, 14.5, 23.2, 22.1, 20.5, 19.4, 18.1, 24.1, 18.5]$ Test if the potato yield from these farms is significantly better than the standard yield. Apply t-test.

What is Regression Analysis? Explain simple and multiple regression with example.

What is Bayesian modelling? How it works? Describe its advantage and disadvantages.

Explain different types of variables used in Regression Modeling.

Explain the concept of Data Wrangling, its process and its importance in Data Analytics.

Explain the meaning, objectives and significance of multivariate exploration of data.

Define Data Cleaning. Explain various types of data quality problems and discuss suitable methods to resolve them.

Explain the process of designing an effective dashboard. Discuss various components and design principles used in dashboard development.

Define data visualization and explain univariate, bivariate and multivariate exploration of data.

Briefly discuss about Univariate Exploration of data.

Explain and compare common file formats such as CSV, JSON, XML and Parquet with suitable examples.

Explain the architecture, working, features, advantages and limitations of HDFS in detail.

Describe different sources of data used in Data Analytics.

Define Big Data and explain various Big Data processing tools in detail.

What do you understand by Data Analyst Ecosystem? Explain.

Analyze the role of Plotly in Business Intelligence applications and why interactive dashboards are preferred over traditional charts.

How does Power BI support real-time analytics and interactive reporting?

Describe different Python data visualization libraries in detail.

What is Power BI and what are its different tools/components in detail?

How to create your Own Visualization of a complex data set? Explain.

Discuss Pandas in Python.

Write short notes on any Two: