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

Define variables and explain different methods of data categorization with suitable examples.

Explain what is meant by measures of central tendency and describe mean, median and mode in detail with examples.

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 with examples.

Describe the maximum likelihood test in detail with an example.

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

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 of these farms is significantly different from the standard yield using t-Test at 5% level of significance.

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

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

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

Define Big Data and explain various Big Data processing tools such as Hadoop, Hadoop Distributed File System (HDFS), Hive and Spark in detail.

Describe different Python data visualization libraries such as matplotlib, pandas, seaborn, ggplot and plotly in detail with examples.

What is Power BI? Explain its different tools/components and discuss how it supports real-time analytics and interactive reporting.