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Questions and Answers
What is the first step in the standard data science process?
What is the first step in the standard data science process?
Which framework is known as the most widely adopted for developing data science solutions?
Which framework is known as the most widely adopted for developing data science solutions?
What phase of the CRISP-DM process involves understanding the customer’s needs?
What phase of the CRISP-DM process involves understanding the customer’s needs?
Which of the following is NOT part of the standard data science process?
Which of the following is NOT part of the standard data science process?
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What is the purpose of the data science process?
What is the purpose of the data science process?
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What is the primary focus of the Business Understanding phase in the CRISP-DM process?
What is the primary focus of the Business Understanding phase in the CRISP-DM process?
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Which of the following steps involves transforming the data into a usable format?
Which of the following steps involves transforming the data into a usable format?
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The CRISP-DM framework is used for which primary purpose?
The CRISP-DM framework is used for which primary purpose?
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What does the 'Modeling' step in the standard data science process primarily involve?
What does the 'Modeling' step in the standard data science process primarily involve?
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Which data science framework is specifically associated with Six Sigma practices?
Which data science framework is specifically associated with Six Sigma practices?
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Why is the data science process considered iterative?
Why is the data science process considered iterative?
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The CRISP-DM process is best described as which of the following?
The CRISP-DM process is best described as which of the following?
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What are the primary outputs of the Knowledge phase in the data science process?
What are the primary outputs of the Knowledge phase in the data science process?
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What is the primary purpose of the prior knowledge step in the data science process?
What is the primary purpose of the prior knowledge step in the data science process?
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Which of the following best describes the process of uncovering patterns in data?
Which of the following best describes the process of uncovering patterns in data?
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What should be prioritized to ensure the success of the data science process?
What should be prioritized to ensure the success of the data science process?
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Which criteria are most important in determining the validity of discovered patterns?
Which criteria are most important in determining the validity of discovered patterns?
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What does the term 'data frame' refer to in the data science process?
What does the term 'data frame' refer to in the data science process?
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The choice of learning algorithm in data science processes is determined by:
The choice of learning algorithm in data science processes is determined by:
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Which consideration is NOT part of evaluating data quality?
Which consideration is NOT part of evaluating data quality?
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What is the function of an identifier attribute in a dataset?
What is the function of an identifier attribute in a dataset?
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Which of the following software tools is NOT mentioned as an option for developing data science algorithms?
Which of the following software tools is NOT mentioned as an option for developing data science algorithms?
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What role does iteration play in the data science process?
What role does iteration play in the data science process?
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What does a label represent in the context of a dataset?
What does a label represent in the context of a dataset?
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Which step is typically the most time-consuming in preparing a dataset for data science?
Which step is typically the most time-consuming in preparing a dataset for data science?
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What is a potential drawback of uncovering patterns in datasets?
What is a potential drawback of uncovering patterns in datasets?
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What is expected from data when preparing it for data science algorithms?
What is expected from data when preparing it for data science algorithms?
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What role does understanding prior knowledge of data play in data science?
What role does understanding prior knowledge of data play in data science?
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Which of the following best describes the transformation processes applied to data?
Which of the following best describes the transformation processes applied to data?
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What is the main focus of data exploration?
What is the main focus of data exploration?
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Which of the following is NOT a common method for handling missing values?
Which of the following is NOT a common method for handling missing values?
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What is a potential consequence of having inaccurate data in a dataset?
What is a potential consequence of having inaccurate data in a dataset?
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Why is data quality considered an ongoing concern?
Why is data quality considered an ongoing concern?
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Which descriptive statistic is NOT typically used to summarize the characteristics of a distribution?
Which descriptive statistic is NOT typically used to summarize the characteristics of a distribution?
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What is the purpose of data cleansing in organizations?
What is the purpose of data cleansing in organizations?
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In the context of data handling, what is meant by 'outlier records'?
In the context of data handling, what is meant by 'outlier records'?
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Which factor is important to understand when managing missing values in datasets?
Which factor is important to understand when managing missing values in datasets?
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What is the primary purpose of detecting outliers in data science applications?
What is the primary purpose of detecting outliers in data science applications?
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How does having a large number of attributes in a dataset affect a model?
How does having a large number of attributes in a dataset affect a model?
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What is a key benefit of sampling in data science?
What is a key benefit of sampling in data science?
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What does sampling aim to achieve in data analysis?
What does sampling aim to achieve in data analysis?
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What is a potential drawback of using sampling in data science?
What is a potential drawback of using sampling in data science?
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What is a potential benefit of replacing missing credit score values with derived scores?
What is a potential benefit of replacing missing credit score values with derived scores?
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Why is it necessary to convert categorical data into numeric data for linear regression models?
Why is it necessary to convert categorical data into numeric data for linear regression models?
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What is the function of normalization in algorithms like k-nearest neighbor?
What is the function of normalization in algorithms like k-nearest neighbor?
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What are outliers in a dataset typically considered to be?
What are outliers in a dataset typically considered to be?
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What technique can be used to convert continuous numeric data into categorical types?
What technique can be used to convert continuous numeric data into categorical types?
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Which of the following is a reason to ignore data records with missing values?
Which of the following is a reason to ignore data records with missing values?
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When should categorical data be converted for better model performance?
When should categorical data be converted for better model performance?
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What typical values are used to replace missing credit score values?
What typical values are used to replace missing credit score values?
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Study Notes
Fundamentals of Data Science
- This is a data science course (DS302) taught by Dr. Nermeen Ghazy
- Reference books are provided:
- Data Science: Concepts and Practice, Vijay Kotu and Bala Deshpande, 2019
- DATA SCIENCE: FOUNDATION & FUNDAMENTALS, B. S. V. Vatika, L. C. Dabra, Gwalior, 2023
Lecture 2
- The lecture is about the Data Science Process
Chapter 2: Data Science Process
- The methodical discovery of useful relationships and patterns in data is enabled by a series of iterative activities, collectively known as the Data Science Process.
- The standard data science process includes:
- Understanding the problem
- Preparing data samples
- Developing the model
- Applying the model on a dataset to observe performance in the real world
- Deploying and maintaining the models
Stages of CRISP-DM (Cross-Industry Standard Process for Data Mining)
- A process model with six phases naturally describes the data science life cycle
- Business Understanding - understand the need and requirements of the project
- Data Understanding - identify, collect, analyze data sets to understand the customer needs
- Data Preparation - prepare the dataset for the data science task
- Modeling - building models using various algorithms
- Evaluation - applying the model to a dataset to measure performance
- Deployment - deploying and maintaining the models
Prior Knowledge
- Refers to information already known about a subject.
- Helps define the problem, its business context, and needed data.
- Key areas are:
- Objective of the problem
- Subject Area
- Data (quality, quantity, availability, gaps etc.)
Data Preparation
- Preparing datasets is the most time-consuming part of the process.
- Datasets aren’t typically in required formats for algorithms.
- Data format conversion may require functions like pivot, type conversion, join, or transpose to suit algorithms.
- Steps often include:
- Data Exploration
- Data quality assessment
- Handling missing values
- Data type conversion
- Transformation
- Outlier detection
- Feature selection
- Sampling
Data Exploration
- Aims to understand data; involves descriptive statistics and visualizations.
- Tools help uncover data structure, value distributions, extreme values, and interrelationships.
Data Quality
- Focuses on data accuracy and consistency.
- Issues like missing values, outlier values (data entry errors) and data entry errors must be addressed.
- Techniques like alerts, cleansing, and transformations improve data quality.
Handling Missing Values
- A common data quality issue. Methods for managing missing values include:
- Replacing missing values with derived credit score values (mean, minimum, or maximum).
- Ignoring or removing records with missing values
Data Type Conversion
- Converting data into the format required by algorithms (numerical, categorical)
- Techniques like binning convert a range of values to specified categories.
Transformation
- Standardizing or normalizing data attributes, such as credit score (in hundreds) to a more usable scale (0-1).
- This normalization allows for consistent comparisons, preventing dominance by high-value attributes.
Outliers
- Outliers are anomalies (abnormal or unusual values) within datasets.
- These can be correct values, or due to data capture errors. Correct data capture can include extremely high income levels, while erroneous could come from various human or system errors.
- Outliers need understanding and specific treatment, sometimes the outlier detection or removal is the goal of the data science process.
Feature Selection
- Handling a large number of attributes (variables); not all attributes are equally important in predicting a target value.
- Techniques like feature selection help manage complexity by choosing relevant attributes for predicting the target value.
Sampling
- Selecting a subset of data to represent the entire dataset.
- Sampling reduces processing time.
- In many cases, the benefits of sampling outweigh the potential errors that can arise from using only a subset of the entire dataset to represent or predict the entire dataset.
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Description
This quiz covers Lecture 2 of the Fundamentals of Data Science course, focusing on the Data Science Process. Explore the iterative activities involved in discovering valuable insights from data, including problem understanding, data preparation, and model development. Perfect for students looking to solidify their knowledge of the data science framework.
