Data Science Process - Lecture 2
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Questions and Answers

What is the first step in the standard data science process?

  • Applying the model
  • Developing the model
  • Understanding the problem (correct)
  • Preparing the data samples

Which framework is known as the most widely adopted for developing data science solutions?

  • CRISP-DM (correct)
  • SEMMA
  • DMAIC
  • KDD

What phase of the CRISP-DM process involves understanding the customer’s needs?

  • Data Preparation
  • Modeling
  • Evaluation
  • Business Understanding (correct)

Which of the following is NOT part of the standard data science process?

<p>Sampling data (A)</p> Signup and view all the answers

What is the purpose of the data science process?

<p>To discover relationships and patterns in data (D)</p> Signup and view all the answers

What is the primary focus of the Business Understanding phase in the CRISP-DM process?

<p>Understanding the objectives and requirements of the project (B)</p> Signup and view all the answers

Which of the following steps involves transforming the data into a usable format?

<p>Preparing the data samples (D)</p> Signup and view all the answers

The CRISP-DM framework is used for which primary purpose?

<p>Developing data science solutions (B)</p> Signup and view all the answers

What does the 'Modeling' step in the standard data science process primarily involve?

<p>Building algorithms to extract insights from data (D)</p> Signup and view all the answers

Which data science framework is specifically associated with Six Sigma practices?

<p>DMAIC (A)</p> Signup and view all the answers

Why is the data science process considered iterative?

<p>It allows for revisiting previous steps based on findings. (A)</p> Signup and view all the answers

The CRISP-DM process is best described as which of the following?

<p>A flexible guideline for completing data science projects (B)</p> Signup and view all the answers

What are the primary outputs of the Knowledge phase in the data science process?

<p>Insights and recommendations based on analyzed data (A)</p> Signup and view all the answers

What is the primary purpose of the prior knowledge step in the data science process?

<p>To define the objective and data needed for the problem (C)</p> Signup and view all the answers

Which of the following best describes the process of uncovering patterns in data?

<p>It helps identify relationships but may produce false signals. (B)</p> Signup and view all the answers

What should be prioritized to ensure the success of the data science process?

<p>A clearly defined statement of the problem. (A)</p> Signup and view all the answers

Which criteria are most important in determining the validity of discovered patterns?

<p>Knowledge of the subject matter and business context. (B)</p> Signup and view all the answers

What does the term 'data frame' refer to in the data science process?

<p>A dataset with a defined structure (B)</p> Signup and view all the answers

The choice of learning algorithm in data science processes is determined by:

<p>The analysis question being addressed. (B)</p> Signup and view all the answers

Which consideration is NOT part of evaluating data quality?

<p>Methods of data collection (C)</p> Signup and view all the answers

What is the function of an identifier attribute in a dataset?

<p>To provide context for data interpretation (C)</p> Signup and view all the answers

Which of the following software tools is NOT mentioned as an option for developing data science algorithms?

<p>Tableau (B)</p> Signup and view all the answers

What role does iteration play in the data science process?

<p>It provides feedback for refining previous approaches. (B)</p> Signup and view all the answers

What does a label represent in the context of a dataset?

<p>The output or prediction based on input attributes (D)</p> Signup and view all the answers

Which step is typically the most time-consuming in preparing a dataset for data science?

<p>Data transformation (B)</p> Signup and view all the answers

What is a potential drawback of uncovering patterns in datasets?

<p>It may lead to overfitting the model to the data. (D)</p> Signup and view all the answers

What is expected from data when preparing it for data science algorithms?

<p>It should be structured in a tabular format. (C)</p> Signup and view all the answers

What role does understanding prior knowledge of data play in data science?

<p>To ensure that the relevant data is selected and used appropriately (D)</p> Signup and view all the answers

Which of the following best describes the transformation processes applied to data?

<p>Applying functions to adapt data into the required structure for analysis (C)</p> Signup and view all the answers

What is the main focus of data exploration?

<p>To compute descriptive statistics and visualize data (D)</p> Signup and view all the answers

Which of the following is NOT a common method for handling missing values?

<p>Use of data alerts to monitor for missing values (A)</p> Signup and view all the answers

What is a potential consequence of having inaccurate data in a dataset?

<p>Decreased representativeness of the model (D)</p> Signup and view all the answers

Why is data quality considered an ongoing concern?

<p>Data can be corrupted during collection and processing (D)</p> Signup and view all the answers

Which descriptive statistic is NOT typically used to summarize the characteristics of a distribution?

<p>Trigonometric functions (D)</p> Signup and view all the answers

What is the purpose of data cleansing in organizations?

<p>To eliminate duplicate records and standardize values (A)</p> Signup and view all the answers

In the context of data handling, what is meant by 'outlier records'?

<p>Records that deviate significantly from other data points (D)</p> Signup and view all the answers

Which factor is important to understand when managing missing values in datasets?

<p>The reason behind the missing values (D)</p> Signup and view all the answers

What is the primary purpose of detecting outliers in data science applications?

<p>To identify anomalies in data such as fraud or intrusion (C)</p> Signup and view all the answers

How does having a large number of attributes in a dataset affect a model?

<p>It may lead to the curse of dimensionality and degrade model performance. (C)</p> Signup and view all the answers

What is a key benefit of sampling in data science?

<p>It allows for faster modeling by reducing data size. (A)</p> Signup and view all the answers

What does sampling aim to achieve in data analysis?

<p>To extract relevant insights without processing the entire dataset. (A)</p> Signup and view all the answers

What is a potential drawback of using sampling in data science?

<p>It can introduce error that affects model relevance. (D)</p> Signup and view all the answers

What is a potential benefit of replacing missing credit score values with derived scores?

<p>It can improve model representativeness if values occur randomly. (C)</p> Signup and view all the answers

Why is it necessary to convert categorical data into numeric data for linear regression models?

<p>Linear regression cannot handle categorical data types. (B)</p> Signup and view all the answers

What is the function of normalization in algorithms like k-nearest neighbor?

<p>To ensure all attributes are weighted equally in distance calculations. (A)</p> Signup and view all the answers

What are outliers in a dataset typically considered to be?

<p>Abnormal values that may result from errors or true extremes. (C)</p> Signup and view all the answers

What technique can be used to convert continuous numeric data into categorical types?

<p>Binning. (B)</p> Signup and view all the answers

Which of the following is a reason to ignore data records with missing values?

<p>To ensure the remaining dataset is of higher quality. (D)</p> Signup and view all the answers

When should categorical data be converted for better model performance?

<p>When building linear regression models. (D)</p> Signup and view all the answers

What typical values are used to replace missing credit score values?

<p>Average, minimum, or maximum values from the dataset. (A)</p> Signup and view all the answers

Flashcards

Data Science Process

A series of iterative steps to find useful patterns and relationships in data.

Data Science Process Stages

The steps involved in a typical data science project: understanding the problem, preparing the data, developing the model, applying the model, and deploying/maintaining it.

CRISP-DM

A popular framework (model) for data science projects, consisting of 6 phases.

Why Data Science?

Data science is important because there is a lot of data, and we need to find useful insights from this data.

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Business Understanding

The first phase of CRISP-DM, focusing on understanding the business problem and objectives.

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Data Preparation

The stage where data is cleaned, transformed, and ready for model development.

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Model Development

Stage where a model is created to address the problem.

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Model Application

Using the developed model on a dataset to see model performance in the real world.

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Model Deployment and Maintenance

Putting the model into action and keeping it up to date.

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Data Science Process

A series of steps to find useful patterns in data.

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Data Science Process Stages

Understanding problem, preparing data, model dev, application, and deployment.

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CRISP-DM

A popular framework for data science projects with 6 phases.

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Importance of Data Science

Transforming large data into useful knowledge/information.

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Business Understanding (Phase 1)

Understanding project goals and customer needs in data science.

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Data Preparation

Cleaning, transforming data for model development.

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Model Development

Creating model to solve the problem, based on prepared data.

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Model Application

Using developed model with data to test in real-world.

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Model Deployment & Maintenance

Implementing model and keeping it updated.

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Data Science Process

A general list of steps for addressing analysis questions using data. It's independent of specific algorithms or tools.

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Prior Knowledge (Step 1)

Understanding the business problem, its context, and the needed data for solving it.

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Objective of the problem

The main aim or question the data science process tries to answer.

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Subject area of the problem

The specific area of knowledge or business process related to the problem.

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Data Understanding

Identifying, collecting, and analyzing datasets, leading to insights and data preparation.

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Data

Information that is collected, stored, transformed, reported, and used.

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Prior Knowledge (data)

Information about existing data, its quality, quantity, availability, and gaps.

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Dataset

A structured collection of data with rows and columns (attributes).

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Data Point (Record)

A single instance in a dataset; a row of information.

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Label (Output)

The special attribute predicted from input attributes, in a dataset.

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Identifier (PK)

Unique attribute used to locate or provide context for individual data records.

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Data Preparation

Transforming data to fit data science algorithms (tabular format).

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Data Exploration

Initial step in data preparation; understanding the data.

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Data quality

The assessment of data for accuracy, completeness, and consistency.

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Outlier Detection

Identifying unusual data points in a dataset.

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Feature Selection

Choosing the most important attributes for a data science model.

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Curse of Dimensionality

Problems with models when dealing with many features.

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Sampling

Using a smaller part of the data to represent the whole.

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Representative Sample

A subset of data that maintains properties similar to the original dataset.

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Missing Credit Score Handling

Replacing missing credit scores with calculated values (mean, min, max) or discarding records.

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Data Type Conversion

Changing data types from categorical (text) to numeric (numbers) or vice versa for data models.

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Binning

Converting numeric values into categories by assigning ranges to each category.

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Normalization

Scaling numeric attributes to a consistent range (like 0 to 1) for algorithms.

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Outliers

Data points that deviate significantly from other data values.

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Handling Missing Values

Addressing the issue of empty or incomplete data entries in a dataset.

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Data Type Conversion

Changing the format of data (e.g., text to numbers).

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Data Transformation

Changing the data format to make it suitable for analysis.

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Outliers

Extreme values that differ significantly from other observations.

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Feature Selection

Choosing the most relevant attributes for analysis or modeling.

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Sampling

Selecting a subset of the data for analysis or model training.

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Data Exploration

Initial analysis of data to understand its characteristics.

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Descriptive Statistics

Summarizing key characteristics of data (e.g., mean, median, standard deviation).

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Data Quality

The accuracy and reliability of the data.

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Data Cleansing

Improving data accuracy by removing errors and inconsistencies.

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Data Warehouse

A centralized repository for storing important data.

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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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Fundamentals of Data Science DS302 PDF

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.

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