Chapter -2
- Name three commonly used Python toolkits for data science and
their primary applications.
- What is the purpose of dimensionality reduction in data science?
- Explain the difference between bar charts and line charts with an
example.
- Discuss how APIs can be used to collect data, with an example of the Twitter API.
- What are the steps involved in cleaning and munging data?
Provide a brief explanation.
- Explain how NumPy can be used for manipulating and rescaling data.
- Discuss the role of Python libraries such as Matplotlib,
Scikit-learn, and NLTK in data science. Provide examples of
tasks they are suited for.
- Explain the process of reading files, web scraping, and using
APIs to collect and prepare data for analysis. Provide an example
of each.
- Describe the methods for visualizing data, such as bar charts,
line charts, and scatter plots, and their applications in data
analysis.
CHAPTER-3
- Define overfitting in machine learning and explain how train/testsplits can help mitigate it.
- Differentiate between supervised and unsupervised learning with
examples
- What is Bayes’ theorem? Briefly explain its significance in machine learning.
- Compare and contrast classification and regression in the context
of machine learning.
- Discuss the applications of unsupervised learning in real-world
scenarios.
- What are evaluation matrices? List and explain metrics for classification algorithms
- Explain the working principle of the K-Nearest Neighbors (KNN) algorithm.