Practical Data Science using Python - ML-Data and CRISP-DM

The video tutorial provides an overview of data science and machine learning, explaining their relationship and the various tools and techniques involved. It discusses different types of data sources, such as social, machine, and transactional data, and their significance in data science. The tutorial also outlines the data science project lifecycle, highlighting the CRISP-DM model and its phases, including business understanding, data preparation, modeling, evaluation, and deployment.

What is the relationship between data science and machine learning?

• Machine learning and data science are the same.

• Data science is a subset of machine learning.

• Machine learning is a subset of data science.

• They are completely unrelated fields.

Which of the following is an example of social data?

• Tweets and comments from customers

• Temperature readings from a smart meter

• Financial transaction records

• Sensor data from manufacturing machines

What type of data is generated from daily transactions?

• Sensor data

• Social data

• Machine data

• Transactional data

What is the first phase in the CRISP-DM model?

• Data preparation

• Modeling

• Business understanding

• Evaluation

Which lifecycle model is commonly used in data science projects?

• Agile methodology

• Waterfall model

• CRISP-DM model

• V-Model

What is the purpose of the data preparation phase?

• To deploy the final model

• To transform and prepare data for modeling

• To select the appropriate algorithm

• To define the business problem

Which type of algorithm might be used for classification tasks?

• Clustering method

• Regression algorithm

• Classification algorithm

• Deep learning method

What is the main goal of the evaluation phase?

• To define the business problem

• To prepare data for modeling

• To optimize and validate the model's performance

• To gather data from various sources

What happens after the final model is developed?

• The model is used for data preparation

• The model is discarded

• The model is deployed into production

• The model is used to gather more data

How can the CRISP-DM model be adapted for specific projects?

• By tailoring the processes and steps

• By skipping the evaluation phase

• By strictly following all phases

• By using only the data preparation phase