Essentials of Data Science

What is the purpose of data cleaning in data science?

• To increase the size of the dataset for better insights.

• The purpose of data cleaning in data science is to enhance data quality for accurate analysis and decision-making.

• To create more complex algorithms for data processing.

• To visualize data trends without any modifications.

Name two common techniques used for data cleaning.

• Data visualization techniques

• Data encryption methods

• Data compression algorithms

• Removing duplicates, Handling missing values

What is the difference between descriptive and inferential statistics?

• Descriptive statistics predict future trends, while inferential statistics summarize data.

• Descriptive statistics analyze relationships between variables, while inferential statistics focus on individual data points.

• Descriptive statistics are used for hypothesis testing, while inferential statistics are used for data visualization.

• Descriptive statistics summarize data, while inferential statistics make predictions or inferences about a population based on a sample.

What is supervised learning in machine learning?

• Supervised learning is a machine learning approach that uses labeled data to train models for making predictions.

• Supervised learning focuses solely on clustering data without labels.

• Unsupervised learning uses labeled data to train models.

• Supervised learning is a method that requires no data for training.

Define overfitting in the context of machine learning models.

• Overfitting is when a model performs well on training data but poorly on unseen data due to excessive complexity.

• Overfitting is when a model performs equally well on both training and unseen data.

• Overfitting occurs when a model is too simple and cannot capture the underlying patterns in the data.

• Overfitting refers to a model that is trained on too much data, leading to poor performance.

What are the key components of a data visualization?

• Programming languages

• Data analysis tools

• Statistical methods

• Data, visual representation, context, audience

Name two popular data visualization tools.

• Tableau, Power BI

• QlikView, SAS

• D3.js, R

• Excel, Google Sheets

What is big data and how is it different from traditional data?

• Big data is large, fast, and diverse data sets that require advanced tools for processing, unlike traditional data which is smaller and more structured.

• Big data is always structured and easy to analyze.

• Big data is only about social media and online transactions.

• Traditional data is larger and requires advanced tools for processing.

What is the role of feature selection in machine learning?

• Feature selection is the process of selecting a subset of relevant features for model training to improve performance and reduce overfitting.

• Feature selection is used to increase the number of features in a dataset.

• Feature selection only applies to unsupervised learning.

• Feature selection is irrelevant in machine learning.

What is the significance of data normalization in data preprocessing?

• Data normalization scales the data to a standard range, improving the performance of machine learning algorithms.

• Data normalization is only necessary for categorical data.

• Data normalization increases the size of the dataset.

• Data normalization is irrelevant for most machine learning tasks.