18 Questions
What is Data Science?
Data science is the field of applying advanced analytics techniques and scientific principles to extract valuable information from data for business decision-making, strategic planning, and other uses.
What are the steps of Data Science?
- Define business requirement, 2. Data collection, 3. Data cleaning, 4. Data exploration and Analysis.
Can you provide an example of data collection in Data Science?
For customer credit risk analysis, the perfect data should typically include demographics data, loan information, repayment data, and transaction statements.
Why is data cleaning important in Data Science?
Data cleaning is important because bad data can lead to inaccurate models. The accuracy and effectiveness of analysis highly depend on the quality of data.
What is the purpose of Data Exploration and Analysis in Data Science?
The purpose is to understand data, explore and visualize it to uncover insights, identify important aspects for analysis, and remove data that may distort the analysis.
Why is defining business requirements important in Data Science?
Defining business requirements is crucial as it helps in understanding the problem to be solved and transforming business needs into actionable data science questions.
What is one significant security concern with fake data in big data?
Fake data reduces the ability to identify other issues like fraud.
What is the first step in the data science process?
Data collection and integration
Why is it important to test the linearity assumption in regression models?
To ensure the relationship between the variables is linear
What is the risk of reversing data masking measures?
It can allow malicious actors to reconnect individuals to their confidential information.
What is one of the biggest data security threats with big data?
The diversity and complexity of the data sets used.
Name one application of data science in manufacturing.
Predictive maintenance
How can data science provide a competitive advantage in retail?
By using big data for demand forecasting, product predictions, and in-store optimization
Name one application of data science in the financial services industry.
Reducing fraud or driving innovation.
What is a key concern when deploying data science applications?
Data security
What is data masking used for in data science?
To separate confidential client information from identifying data.
What is one step in the data science process?
Generating or collecting data.
Give one step in the data science process after building the model.
Data validation to test the model's accuracy
Explore the fundamentals of data science, including advanced analytics techniques and scientific principles to extract valuable information from data. Learn how data science helps with business decision-making, strategic planning, and more.
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