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How is data science used in finance?
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Fraud detection algorithms
What is a common application of data science in marketing?
Customer segmentation and targeting
Describe techniques to preprocess and clean data.
Data cleaning
What are the ramifications of bias in algorithms?
Bias can lead to unfairness and discrimination, which are ethical concerns.
How does supervised learning differ from unsupervised learning?
Supervised learning involves training models on labeled data, while unsupervised learning involves training models on unlabeled data.
Describe reinforcement learning.
Reinforcement learning involves models that learn by interacting with the environment.
What are the methods to collect data from diverse sources called?
Data collection
What is the goal of inferential statistics in data science?
Making predictions or inferences about a population from a sample
Why is collaboration among domain experts, data engineers, and statisticians crucial?
Collaboration ensures the integration of domain knowledge with technical and statistical expertise to achieve effective data science solutions.
What is the purpose of descriptive statistics?
Summarizing and describing data features
Why is continuous learning important in the field of Data Science?
The field is rapidly evolving, requiring professionals to stay updated with new techniques and technologies.
What are some challenges related to data quality in data science?
Handling incomplete data, smoothing noisy data, ensuring data consistency
Why is data privacy an ethical consideration in data science?
Protecting user data is crucial to avoid misuse and maintain trust.
Give an example of a real-world application of data science in healthcare.
Predictive models for disease diagnosis
What is the definition of Data Science?
The intersection of data engineering, scientific methods, and domain expertise.
What processes are involved in changing data into the desired format?
Data transformation
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