Podcast
Questions and Answers
Detecting outliers is often the primary purpose of some data science applications like fraud detection.
Detecting outliers is often the primary purpose of some data science applications like fraud detection.
True (A)
A dataset with fewer attributes is more prone to the curse of dimensionality.
A dataset with fewer attributes is more prone to the curse of dimensionality.
False (B)
Sampling is a method used to select a subset of records to represent the original dataset.
Sampling is a method used to select a subset of records to represent the original dataset.
True (A)
Including irrelevant attributes can improve the performance of a predictive model.
Including irrelevant attributes can improve the performance of a predictive model.
The error introduced by sampling is usually outweighed by the benefits of reducing the amount of data processed.
The error introduced by sampling is usually outweighed by the benefits of reducing the amount of data processed.
The data science process includes five phases: Understanding the problem, Preparing the data, Developing the model, Applying the model, and Deploying the models.
The data science process includes five phases: Understanding the problem, Preparing the data, Developing the model, Applying the model, and Deploying the models.
CRISP-DM is an acronym that stands for Cross Regional Innovation and Standard Process for Data Management.
CRISP-DM is an acronym that stands for Cross Regional Innovation and Standard Process for Data Management.
Data mining is a relatively new concept and has no significant historical background.
Data mining is a relatively new concept and has no significant historical background.
The DMAIC framework is used for data science projects and stands for Define, Measure, Analyze, Improve, and Control.
The DMAIC framework is used for data science projects and stands for Define, Measure, Analyze, Improve, and Control.
The Knowledge Discovery in Databases process includes the phases: Selection, Preprocessing, Transformation, Data Mining, and Interpretation.
The Knowledge Discovery in Databases process includes the phases: Selection, Preprocessing, Transformation, Data Mining, and Interpretation.
The Business Understanding phase of CRISP-DM only focuses on data collection without considering customer requirements.
The Business Understanding phase of CRISP-DM only focuses on data collection without considering customer requirements.
The standard data science process framework consists of six phases.
The standard data science process framework consists of six phases.
SEMA stands for Sample, Explore, Modify, Model, and Assess, which is one of the frameworks used in data science.
SEMA stands for Sample, Explore, Modify, Model, and Assess, which is one of the frameworks used in data science.
Descriptive statistics help summarize the key characteristics of data distributions.
Descriptive statistics help summarize the key characteristics of data distributions.
Outliers should be included in data analysis without any assessment.
Outliers should be included in data analysis without any assessment.
Data exploration includes both computing descriptive statistics and visualizing data.
Data exploration includes both computing descriptive statistics and visualizing data.
A credit score of 900 is an acceptable value for data accuracy.
A credit score of 900 is an acceptable value for data accuracy.
The data science process relies solely on the quantity of data collected.
The data science process relies solely on the quantity of data collected.
The interest rate typically decreases as the credit score increases.
The interest rate typically decreases as the credit score increases.
A dataset is defined as a collection of data with a well-structured format.
A dataset is defined as a collection of data with a well-structured format.
Data cleansing practices do not include standardizing attribute values.
Data cleansing practices do not include standardizing attribute values.
The label in a dataset refers to an input attribute that must be predicted.
The label in a dataset refers to an input attribute that must be predicted.
Organizations benefit from maintaining data warehouses for higher data quality.
Organizations benefit from maintaining data warehouses for higher data quality.
Preparing the dataset for data science tasks is usually the simplest part of the process.
Preparing the dataset for data science tasks is usually the simplest part of the process.
Managing missing values first requires understanding the reasons behind their absence.
Managing missing values first requires understanding the reasons behind their absence.
Identifiers are attributes used to provide context to individual records in a dataset.
Identifiers are attributes used to provide context to individual records in a dataset.
In a data structure, each row represents a data point.
In a data structure, each row represents a data point.
Data transformation is unnecessary if the data is originally in a tabular format.
Data transformation is unnecessary if the data is originally in a tabular format.
Quality of data is less important than availability when answering business questions.
Quality of data is less important than availability when answering business questions.
The fundamental objective of any data science process is to address the analysis question.
The fundamental objective of any data science process is to address the analysis question.
Prior knowledge refers to data that is yet to be discovered about a subject.
Prior knowledge refers to data that is yet to be discovered about a subject.
A well-defined statement of the problem is crucial for selecting the right dataset in the data science process.
A well-defined statement of the problem is crucial for selecting the right dataset in the data science process.
Data science can use any kind of algorithm without concern for the business question being addressed.
Data science can use any kind of algorithm without concern for the business question being addressed.
It is possible to ignore the subject matter expertise in the data science process.
It is possible to ignore the subject matter expertise in the data science process.
Spurious signals in data science refer to genuine patterns that are highly relevant to the analysis.
Spurious signals in data science refer to genuine patterns that are highly relevant to the analysis.
The data science process is a linear series of steps that must be followed exactly.
The data science process is a linear series of steps that must be followed exactly.
Custom coding is one of the software tools that can be used to implement data science algorithms.
Custom coding is one of the software tools that can be used to implement data science algorithms.
Missing credit score values can only be replaced with the mean value of the dataset.
Missing credit score values can only be replaced with the mean value of the dataset.
Data records with missing values can be ignored to build a representative model.
Data records with missing values can be ignored to build a representative model.
In linear regression models, input attributes can be categorical.
In linear regression models, input attributes can be categorical.
Binning is a technique used to convert categorical data into numeric values.
Binning is a technique used to convert categorical data into numeric values.
Normalization helps prevent one attribute from dominating distance calculations in algorithms like k-NN.
Normalization helps prevent one attribute from dominating distance calculations in algorithms like k-NN.
Outliers are considered normal variations in a dataset.
Outliers are considered normal variations in a dataset.
Income and credit score must be on the same scale for distance calculations in clustering algorithms.
Income and credit score must be on the same scale for distance calculations in clustering algorithms.
The presence of outliers in a dataset is inconsequential and does not require any action.
The presence of outliers in a dataset is inconsequential and does not require any action.
Flashcards
Data Science Process
Data Science Process
A set of iterative activities for discovering useful patterns and relationships in data.
Data Science Process Steps
Data Science Process Steps
Understanding the problem, preparing data, developing a model, applying it, deploying, and maintaining the model.
CRISP-DM
CRISP-DM
Cross Industry Standard Process for Data Mining, a widely used framework for data science projects.
Why Data Science is Important
Why Data Science is Important
Signup and view all the flashcards
Data Science Process Frameworks
Data Science Process Frameworks
Signup and view all the flashcards
Business Understanding
Business Understanding
Signup and view all the flashcards
Data Science Process
Data Science Process
Signup and view all the flashcards
Data Understanding
Data Understanding
Signup and view all the flashcards
Prior Knowledge
Prior Knowledge
Signup and view all the flashcards
Objective of the problem
Objective of the problem
Signup and view all the flashcards
Subject area of the problem
Subject area of the problem
Signup and view all the flashcards
Analysis Question
Analysis Question
Signup and view all the flashcards
Data
Data
Signup and view all the flashcards
Business Question
Business Question
Signup and view all the flashcards
Prior Knowledge (Data)
Prior Knowledge (Data)
Signup and view all the flashcards
Data Quality
Data Quality
Signup and view all the flashcards
Data Quantity
Data Quantity
Signup and view all the flashcards
Data Availability
Data Availability
Signup and view all the flashcards
Data Gaps
Data Gaps
Signup and view all the flashcards
Dataset
Dataset
Signup and view all the flashcards
Data Point
Data Point
Signup and view all the flashcards
Label (Data)
Label (Data)
Signup and view all the flashcards
Identifier (Data)
Identifier (Data)
Signup and view all the flashcards
Data Preparation
Data Preparation
Signup and view all the flashcards
Data Exploration
Data Exploration
Signup and view all the flashcards
Data Transformation
Data Transformation
Signup and view all the flashcards
Missing Values
Missing Values
Signup and view all the flashcards
Data Quality Issues
Data Quality Issues
Signup and view all the flashcards
Data Exploration
Data Exploration
Signup and view all the flashcards
Descriptive Statistics
Descriptive Statistics
Signup and view all the flashcards
Data Cleansing
Data Cleansing
Signup and view all the flashcards
Data Warehouse
Data Warehouse
Signup and view all the flashcards
Outliers
Outliers
Signup and view all the flashcards
Feature Selection
Feature Selection
Signup and view all the flashcards
Sampling
Sampling
Signup and view all the flashcards
Missing Credit Scores
Missing Credit Scores
Signup and view all the flashcards
Rare Missing Values
Rare Missing Values
Signup and view all the flashcards
Ignoring Poor Data
Ignoring Poor Data
Signup and view all the flashcards
Data Type Conversion
Data Type Conversion
Signup and view all the flashcards
Categorical to Numeric
Categorical to Numeric
Signup and view all the flashcards
Numeric to Categorical
Numeric to Categorical
Signup and view all the flashcards
Normalization
Normalization
Signup and view all the flashcards
Outliers
Outliers
Signup and view all the flashcards
Outlier Detection
Outlier Detection
Signup and view all the flashcards
Feature Selection
Feature Selection
Signup and view all the flashcards
Curse of Dimensionality
Curse of Dimensionality
Signup and view all the flashcards
Sampling
Sampling
Signup and view all the flashcards
Dataset Attributes
Dataset Attributes
Signup and view all the flashcards
Dataset Label
Dataset Label
Signup and view all the flashcards
Study Notes
Fundamentals of Data Science - DS302
- Course taught by Dr. Nermeen Ghazy
- Reference books include:
- 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 - Data Science Process
- Data science is a process of discovering relationships and patterns in data
- It involves iterative activities
- The standard data science process includes:
- Understanding the problem
- Preparing data samples
- Developing a model
- Applying the model to a dataset
- Deploying and maintaining models
Key Steps in Data Science
- Prior Knowledge: This involves understanding the problem's objective, the subject area, and the data itself.
- Preparation: This involves exploring, defining issues of data quality, handling missing values, converting data types, transforming data, removing outliers, and then sampling.
- Modeling: This involves building and applying models.
- Application: This involves using the model on data.
- Knowledge: This involves gaining knowledge based on the findings.
Data Science Process - CRISP-DM
- CRISP-DM is a six-phased process model
- It naturally describes the data science life cycle, analogous to a set of roadmaps
Why Data Science is Important
- Huge amount of available data
- Need to transform data into valuable insights and knowledge
- Natural evolution of information technology
Data Science Process Framework
- Common data science frameworks include:
- CRISP-DM (Cross Industry Standard Process for Data Mining)
- SEMMA (Sample, Explore, Modify, Model, and Assess)
- DMAIC (Define, Measure, Analyze, Improve, and Control), used in Six Sigma practice.
CRISP-DM Process Steps
- Business Understanding: Understand project objectives and customer needs
- Data Understanding: Identify, collect, analyze data sets
- Data Preparation: Focus on preprocessing, transformation, and modification of data. Explore various methods for handling missing data: mean, minimum, maximum, etc. Note that converting data to be used within a linear regression model requires numerical values. Different methods such as binning can be used to convert continuous data to categorical data. Focus on data structures (e.g. data frame).
- Modeling: Build and apply models
- Evaluation: Model evaluation and improvement.
- Deployment: Deployment and maintenance of models
Prior Knowledge
- Refers to existing information about a subject
- Guides problem definition, context, and required data
- Key components include:
- Problem objective
- Subject area
- Data
Data
- Factors to consider include quality, quantity, availability, gaps, etc
- Understanding the data collection and reporting is essential in data science
- Dataset: Collection of data with defined structure
- Data point: Single instance in the dataset. Also known as record, object, or example.
Data Preparation
- This is often the most time-consuming part of the process.
- Datasets are rarely in the required form for algorithms.
- Tabular format (records in rows, attributes in columns) is usually required.
Data Preparation Steps
- Data exploration
- Data quality
- Handling missing values
- Data type conversion
- Transformation
- Feature selection
- Sampling
Data Exploration
- Aims to understand characteristics of a dataset
- Includes descriptive statistics and visualizations
- Shows dataset structure, value distributions, extreme values, and inter-relationship of attributes
Data Quality
- A critical aspect of data science
- Deals with accuracy, completeness, consistency, and timeliness of data.
Handling Missing Values
- One of the common data quality issues
- Various methods exist to deal with missing values
Data Type Conversion
- Converting data to the suitable data type for a specific model.
- Continuous or numeric attributes may need to become continuous or categorical.
Transformation
- Normalization plays an important role, preventing any one attribute from dominating results due to large attribute values. Useful in algorithms such as k-nearest neighbor where comparisons of one dataset to another are made. Normalization usually converts a dataset into a more uniform scale between 0 and 1.
Outliers
- Abnormal values in a dataset
- May have to be identified and managed based on their origin
- These values need special consideration
Feature Selection
- Identifying relevant attributes needed to train a model
- Helps in mitigating issues with dimensionality
Sampling
- Selecting a subset of data to represent the original dataset
- Reduces processing time
Studying That Suits You
Use AI to generate personalized quizzes and flashcards to suit your learning preferences.
Related Documents
Description
This quiz covers the key steps involved in the data science process as outlined in DS302. Participants will explore essential activities such as problem understanding, data preparation, model development, and deployment. Test your knowledge of the iterative nature and methodologies in data science.
