Exploring Data Science Concepts

What is the purpose of data visualization?

• To create complex algorithms

• To store data in a database

• To increase data processing speed

• The purpose of data visualization is to make data understandable and accessible through visual representation.

Name three common data visualization techniques.

• scatter plot

• box plot

• Bar chart, line graph, pie chart

• heat map

What is statistical inference?

• Statistical inference is the process of drawing conclusions about a population based on sample data.

• Statistical inference is the technique of predicting future events based on past data.

• Statistical inference refers to the process of analyzing data without any samples.

• Statistical inference is the method of collecting data from a population.

Explain the difference between a population and a sample.

• A population is always smaller than a sample.

• A population is a random selection from a sample.

• A sample includes every member of the population.

• A population is the entire group of interest, whereas a sample is a smaller, representative subset of that population.

What is the central limit theorem?

• The central limit theorem applies only to small sample sizes.

• The central limit theorem describes how the means of samples from a population will form a normal distribution as the sample size increases.

• The central limit theorem indicates that sample sizes do not affect the distribution of means.

• The central limit theorem states that all populations are normally distributed.

Describe the concept of p-value in hypothesis testing.

• A p-value below 0.05 guarantees the null hypothesis is false.

• The p-value indicates the strength of the alternative hypothesis.

• The p-value measures the effect size of the observed data.

• The p-value quantifies the probability of observing the data, or something more extreme, under the assumption that the null hypothesis is true.

What are the main types of machine learning algorithms?

• Supervised analysis

• Supervised learning, Unsupervised learning, Reinforcement learning

• Unsupervised classification

• Reinforcement prediction

Define supervised learning and provide an example.

• Unsupervised learning involves training on unlabelled data, such as clustering similar items.

• An example of supervised learning is a model predicting stock prices without any historical data.

• Supervised learning is when a model learns without any data, like random guessing.

• Supervised learning is a type of machine learning where a model is trained on labeled data. An example is image classification, where a model is trained to distinguish between images of cats and dogs.

What is the purpose of regression analysis?

• To create visualizations of data only.

• To analyze relationships and make predictions based on data.

• To perform hypothesis testing exclusively.

• To summarize data in a report.

Differentiate between linear and logistic regression.

• Linear regression predicts probabilities; logistic regression predicts values on a continuous scale.

• Linear regression can only handle categorical data; logistic regression handles continuous data.

• Linear regression is used for time series analysis; logistic regression is used for forecasting.

• Linear regression predicts continuous outcomes; logistic regression predicts binary outcomes.