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Data Science Methodology

Approaching data problems systematically

Data science methodology is the systematic approach of solving data-related problems. It involves defining the problem, collecting and preparing data, exploring and visualizing data, building and evaluating models, and communicating the results. It also emphasizes the importance of iterating and refining the process to improve the accuracy of the results.
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Data Science Methodology provides a structured approach to solving complex problems with data. It typically begins with understanding the business problem, which involves clarifying objectives and identifying key questions. Next comes the analytic approach phase where scientists determine what tech…

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Big Data Scientist - Data Science Methodology Example Questions

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Question 1

What is the purpose of cross-validation in machine learning?

Correct Answer: Cross-validation is used to estimate the performance of a model and to ensure that it generalizes well to new data.

Cross-validation is an important technique for evaluating machine learning models, as it allows the algorithm to be tested on multiple splits of the data, ensuring that it generalizes well to new data.

Question 2

What is the difference between a sample and a population in statistics?

Correct Answer: A population is the complete set of individuals, objects, or measurements that are of interest to the researcher, while a sample is a subset of the population that is used to estimate the characteristics of the population.

In statistics, a population is the entire group of individuals, objects, or measurements that are of interest to the researcher, while a sample is a subset of the population that is selected to represent the population and provide information on its characteristics. The sample is usually taken randomly to ensure that it is representative of the population, and statistical inference is used to make generalizations about the population based on the sample data. Therefore, the correct answer is that a population is the complete set of individuals, objects, or measurements that are of interest to the researcher, while a sample is a subset of the population that is used to estimate the characteristics of the population.

Question 3

What is the difference between a correlation and a regression analysis in data science?

Correct Answer: Correlation identifies the strength of a relationship between two variables while regression predicts the value of one variable based on the value of another variable with the help of a formula

Correlation analysis measures the degree of association between two variables whereas regression analysis helps to predict the dependent variable based on the independent variable. So, the correct answer is that correlation identifies the strength of a relationship between two variables while regression predicts the value of one variable based on the value of another variable with the help of a formula.

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