Podcast
Questions and Answers
What is the primary purpose of data exploration?
What is the primary purpose of data exploration?
Which of the following is NOT a method used for improving data quality?
Which of the following is NOT a method used for improving data quality?
What is an outlier in the context of data quality?
What is an outlier in the context of data quality?
What is one of the first steps to take when managing missing values?
What is one of the first steps to take when managing missing values?
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Which descriptive statistic provides a summary of the central tendency of a dataset?
Which descriptive statistic provides a summary of the central tendency of a dataset?
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Why is data sourced from well-maintained warehouses considered to have higher quality?
Why is data sourced from well-maintained warehouses considered to have higher quality?
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What can high-quality data impact positively in an organization?
What can high-quality data impact positively in an organization?
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Which method can be used to deal with missing values?
Which method can be used to deal with missing values?
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What is the first step in the data science process?
What is the first step in the data science process?
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Which of the following is NOT considered part of prior knowledge in the data science process?
Which of the following is NOT considered part of prior knowledge in the data science process?
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Why is understanding the subject area of the problem critical in the data science process?
Why is understanding the subject area of the problem critical in the data science process?
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What is one major challenge practitioners face when uncovering patterns in datasets?
What is one major challenge practitioners face when uncovering patterns in datasets?
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Which learning algorithms are mentioned as potential options in the data science process?
Which learning algorithms are mentioned as potential options in the data science process?
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What does the iterative nature of the data science process imply?
What does the iterative nature of the data science process imply?
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Which software tools are mentioned for implementing data science algorithms?
Which software tools are mentioned for implementing data science algorithms?
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What is the main objective of a data science process?
What is the main objective of a data science process?
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What is the first step in the standard data science process?
What is the first step in the standard data science process?
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Which of the following is NOT a framework mentioned for data science processes?
Which of the following is NOT a framework mentioned for data science processes?
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In the CRISP-DM process, what does the Business Understanding phase focus on?
In the CRISP-DM process, what does the Business Understanding phase focus on?
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What does the acronym SEMMA stand for in data science process frameworks?
What does the acronym SEMMA stand for in data science process frameworks?
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What is the purpose of the Application step in the data science process?
What is the purpose of the Application step in the data science process?
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Which of the following correctly lists the components of the data science process?
Which of the following correctly lists the components of the data science process?
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Which phase of the CRISP-DM framework is aimed at identifying project objectives?
Which phase of the CRISP-DM framework is aimed at identifying project objectives?
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Why is data mining considered important in data science?
Why is data mining considered important in data science?
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What are the key factors to consider when evaluating data for the data science process?
What are the key factors to consider when evaluating data for the data science process?
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What does a 'label' in the context of a dataset refer to?
What does a 'label' in the context of a dataset refer to?
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What is necessary to prepare a dataset for use in data science algorithms?
What is necessary to prepare a dataset for use in data science algorithms?
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Which of the following is NOT a step in the data preparation process?
Which of the following is NOT a step in the data preparation process?
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What is a dataset typically described as?
What is a dataset typically described as?
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What is the purpose of identifying gaps in data during the data science process?
What is the purpose of identifying gaps in data during the data science process?
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What are the columns in a dataset typically referred to?
What are the columns in a dataset typically referred to?
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Which data transformation technique is NOT mentioned as necessary for preparing data?
Which data transformation technique is NOT mentioned as necessary for preparing data?
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What is a primary purpose of detecting outliers in data science applications?
What is a primary purpose of detecting outliers in data science applications?
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How does a large number of attributes in a dataset affect model performance?
How does a large number of attributes in a dataset affect model performance?
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What is the main benefit of sampling in data analysis?
What is the main benefit of sampling in data analysis?
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What is a potential downside of sampling when analyzing data?
What is a potential downside of sampling when analyzing data?
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Why is not all attributes in a dataset considered equally important?
Why is not all attributes in a dataset considered equally important?
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What is a suitable method for replacing missing credit score values when they occur randomly and infrequently?
What is a suitable method for replacing missing credit score values when they occur randomly and infrequently?
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Which of the following accurately describes the concept of binning in data type conversion?
Which of the following accurately describes the concept of binning in data type conversion?
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Why is normalization important in algorithms like k-nearest neighbor (k-NN)?
Why is normalization important in algorithms like k-nearest neighbor (k-NN)?
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What constitutes an outlier in a dataset?
What constitutes an outlier in a dataset?
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Which method can be employed to handle records with missing values or poor data quality?
Which method can be employed to handle records with missing values or poor data quality?
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What kind of data types are physical measurements like height or income typically classified as?
What kind of data types are physical measurements like height or income typically classified as?
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When transforming categorical data for linear regression models, what must be ensured?
When transforming categorical data for linear regression models, what must be ensured?
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What common problem may arise due to outliers in a dataset?
What common problem may arise due to outliers in a dataset?
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Study Notes
Fundamentals of Data Science
- The methodical discovery of useful relationships and patterns in data is enabled by a set of iterative activities known as the data science process.
- The standard data science process includes:
- Understanding the problem
- Preparing data samples
- Developing the model
- Applying the model to a dataset to see how it works in the real world
- Deploying and maintaining the models
Reference Books
- Data Science: Concepts and Practice, by Vijay Kotu and Bala Deshpande (2019)
- DATA SCIENCE: FOUNDATION & FUNDAMENTALS, by B. S. V. Vatika, L. C. Dabra (2023)
Lecture 2
- Covers the data science process.
Chapter 2: Data Science Process
- The data science process is a generic set of steps.
- The fundamental objective is to address the analysis question.
- Algorithms used to solve business questions can include decision trees, artificial neural networks, or scatterplots.
- Software tools range from custom coding to RapidMiner, R, Weka, SAS, Oracle Data Miner, and Python.
Data Science Process
- A process model with six phases that naturally describes the data science life cycle.
- Includes phases like:
- Business Understanding
- Data Understanding
- Preparing the Data
- Modeling
- Evaluation
- Deployment
Prior Knowledge
- Refers to information already known about a subject.
- Helps define the problem, business context, and necessary data. Key parts include:
- Objective of the problem
- Subject area of the problem
- Data needed to solve the problem.
Prior Knowledge: Objective of the Problem
- The data science process starts with a need for analysis, a question, or a business objective.
- It is the most important step; without a well-defined problem, finding the right dataset and algorithm is impossible.
- Revisions to assumptions, approach, and tactics are common during the process.
Prior Knowledge: Subject Area of the Problem
- The data science process uncovers hidden patterns and relationships between attributes.
- Identifying false or spurious signals (patterns) is essential.
- Knowing the subject matter, context, and business process generating the data is crucial.
Prior Knowledge: Data
- Understanding the data collection, storage, transformation, reporting, and usage is essential.
- Surveying existing data helps to narrow down the need for new data. Specific data quality factors include
- Quality
- Quantity
- Availability
- Gaps
- Business questions
Data Terminology
- Dataset: A collection of data with a defined structure.
- Data frame: A table structure with rows and columns (headers)
- Data Point (Record, Object, Example): A single instance within a dataset (a single row).
Data Preparation
- Data preparation is the most time-consuming step in data science process.
- Data is rarely in the suitable format, so transformation is required.
- Tabular format with records in rows and attributes in columns is typical for most data science algorithms.
Data Preparation steps
- Data Exploration
- Data Quality
- Handling missing value
- Data type conversion
- Transformation
- Outliers
- Feature selection
- Sampling
Data Exploration
- Provides basic understanding of data.
- Involves computing descriptive statistics and visualization.
- Exposes data structure, value distribution, extreme values, and inter-relationships.
- Use of statistics such as mean, median, mode, standard deviation, and range to describe data. A scatterplot can help visualize data.
Data Quality
- A continual concern in data collection, processing, and storage.
- Data accuracy and quality is essential.
- Data warehouses are used to store and maintain the data quality. Common quality techniques include:
- Removing duplicates
- Identifying and handling outliers
- Standardizing attribute values
- Handling missing values.
Handling Missing Values
- A common data quality issue is missing attribute values.
- Methods exist for dealing missing values:
- Replacing with derived values—e.g. mean, minimum, or maximum
- Ignoring the records with missing values in the data
Data Type Conversion
- Data attributes can be numeric (interest rate), integer numeric (credit score), or categorical.
- Categorical data may need to be converted to numeric for model applications, including linear regression models.
- A technique called binning converts numeric ranges to categorical values based on bins.
Transformation
- Some data science algorithms (e.g., k-nearest neighbor) require numeric and normalized attributes.
- Normalization converts values to a consistent scale (often 0 to 1) to prevent attributes with larger values to dominate comparisons.
Outliers
- Outliers are anomalies in a dataset; they need to be understood and addressed.
- They can arise from data errors (incorrect entry) or valid data captures (very high income for example)
- Outliers require special treatment depending on the data science application.
Feature Selection
- A large number of attributes complicates models and can significantly degrade performance
- Not all attributes are important for prediction of interest
- Feature selection reduces the model complexity, boosts performance, and avoids "curse of dimensionality".
Sampling
- A subset of records (representative samples) from the original data is selected.
- Sampling reduces the amount of data needing processing, speeding up data science tasks.
- The use of representative samples allows for data insight gathering.
- The risk of sampling is that it could impact the relevance of the model, but benefits often outweigh the risk.
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Description
This quiz focuses on the data science process, covering the key steps involved in methodically discovering patterns in data. It emphasizes understanding the problem, preparing data, developing models, and applying them in real-world scenarios. Ideal for students of data science looking to cement their understanding of this critical chapter.
