Introduction to Data Analytics — Previous Year Questions

PYQ papers for this course will be added here soon.

Check back later for:

• End Term Examination papers
• Mid Term papers
• Important questions by unit

Frequently Asked Topics (by unit)

Unit 1

• Differentiate between data, information and knowledge with examples
• Explain the types of data with a suitable classification (NOIR scale)
• Describe the data analytics lifecycle with a diagram
• Explain probability and non-probability sampling techniques
• Differentiate stratified sampling and cluster sampling
• Explain data cleaning steps; how are missing values and outliers handled?
• Explain data preprocessing: cleaning, integration, reduction, transformation
• Numerical: min-max normalization, z-score standardization, decimal scaling
• Numerical: smoothing by bin means / bin boundaries

Unit 2

• Numerical: mean, median and mode for grouped and ungrouped data
• Numerical: standard deviation, variance, coefficient of variation
• Numerical: Karl Pearson's correlation coefficient; Spearman's rank correlation
• Explain the DIKW/EDA process and its objectives
• Differentiate histogram and bar chart; box plot and histogram
• Draw and interpret a box plot; identify outliers using the IQR method
• Explain Bayes' theorem with a numerical example
• Explain hypothesis testing steps; Type I vs Type II error
• Numerical: one-sample z-test and t-test

Unit 3

• Differentiate supervised and unsupervised learning
• Explain the confusion matrix; compute accuracy, precision, recall, F1
• Numerical: Naïve Bayes classification (play-tennis style problem)
• Numerical: KNN classification with Euclidean distance
• Numerical: simple linear regression — find the equation and predict
• Explain the K-Means algorithm with steps; solve a clustering numerical
• Differentiate K-Means and hierarchical clustering; KNN and K-Means
• Explain linkage methods; construct a dendrogram from a distance matrix
• Define support, confidence and lift; solve an association-rule numerical
• Explain the Apriori algorithm with a complete worked example

Unit 4

• Explain NumPy arrays vs Python lists; broadcasting
• Explain Pandas Series and DataFrame; .loc vs .iloc
• Write Python code for data import/export, groupby, and pivot tables
• Explain the 5 V's of big data
• Explain HDFS architecture with a diagram (NameNode, DataNode, blocks, replication)
• Explain YARN and its components
• Explain MapReduce with the word-count example
• Differentiate Hadoop MapReduce and Apache Spark
• Explain components of the Hadoop ecosystem (Hive, Pig, HBase, Sqoop, Flume)
• Applications of data analytics in any two domains