Data Science Process - DS302 Lecture 2
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What is the primary objective of gathering prior knowledge in data during the data science process?

  • To form a dataset that answers the business question (correct)
  • To ensure data is collected randomly
  • To create new business questions
  • To evaluate the ethical implications of data usage

    • Which of the following best describes a dataset?

  • Any type of data, regardless of organization or type
  • Only recent data collected for analysis
  • A collection of data with a defined structure, such as rows and columns (correct)
  • A random collection of data points without structure

    • What factors should be considered when evaluating data for a business question?

  • The aesthetics of data visualization tools
  • Quality, quantity, and gaps in data (correct)
  • The personal opinions of stakeholders
  • The complexity of data algorithms

    • Which term refers to an attribute used for context or identification within a dataset?

    <p>Identifier</p> Signup and view all the answers

    What is typically the most time-consuming part of the data science process?

    <p>Data preparation</p> Signup and view all the answers

    Which transformation might be necessary if the data is not in tabular format?

    <p>Applying pivot functions</p> Signup and view all the answers

    What distinguishes a label in a dataset?

    <p>It is the target attribute to be predicted from input attributes</p> Signup and view all the answers

    What is a common characteristic of data points in a dataset?

    <p>They can include various data types and structures</p> Signup and view all the answers

    What is the primary goal of data exploration?

    <p>To visualize the inter-relationships within the dataset.</p> Signup and view all the answers

    Which descriptive statistic provides a measure of central tendency in the data?

    <p>Mean</p> Signup and view all the answers

    What is a common issue related to data quality?

    <p>Missing attribute values.</p> Signup and view all the answers

    What is an important first step in managing missing values?

    <p>Understanding the reason behind the missing values.</p> Signup and view all the answers

    Which method can be used to improve data quality?

    <p>Data cleansing practices.</p> Signup and view all the answers

    What is likely to occur if a credit score is recorded as 900?

    <p>It indicates a possible data entry error.</p> Signup and view all the answers

    Which process involves standardizing attribute values in a dataset?

    <p>Data transformation.</p> Signup and view all the answers

    The scatterplot of credit score vs loan interest rate indicates what type of relationship?

    <p>Inverse correlation.</p> Signup and view all the answers

    What is the primary purpose of outlier detection in data science applications?

    <p>To enhance fraud or intrusion detection capabilities</p> Signup and view all the answers

    What issue arises from having a large number of attributes in a dataset?

    <p>Increased likelihood of overfitting the model</p> Signup and view all the answers

    What is the main advantage of using sampling in data analysis?

    <p>It reduces processing time and speeds up model building</p> Signup and view all the answers

    Why might some attributes in a dataset not be useful for predicting the target?

    <p>They introduce unnecessary complexity and noise</p> Signup and view all the answers

    What does sampling help achieve in relation to the original dataset?

    <p>It creates a representative subset with similar properties to the original</p> Signup and view all the answers

    What is one method for handling missing credit score values?

    <p>Use the mean, minimum, or maximum value from the dataset</p> Signup and view all the answers

    Which statement about converting data types is true?

    <p>Credit scores can be expressed as both numeric and categorical values.</p> Signup and view all the answers

    Why is normalization important in algorithms like k-NN?

    <p>It ensures that no attribute dominates the distance calculations.</p> Signup and view all the answers

    What can be a reason for the presence of outliers in a dataset?

    <p>Legitimate extreme values among the observations.</p> Signup and view all the answers

    What is a consequence of ignoring data records with poor quality?

    <p>It reduces the overall size of the dataset.</p> Signup and view all the answers

    In the context of data conversion, what does 'binning' accomplish?

    <p>It converts continuous numerical data into categorical types.</p> Signup and view all the answers

    Which of the following is a primary requirement for linear regression models concerning input attributes?

    <p>They must be in continuous numeric format.</p> Signup and view all the answers

    What kind of data attributes can be derived from a continuous numeric value?

    <p>Both continuous and categorical attributes.</p> Signup and view all the answers

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

    <p>Understanding the problem</p> Signup and view all the answers

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

    <p>CRISP-DM</p> Signup and view all the answers

    In the CRISP-DM process, what is emphasized in the Business Understanding phase?

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

    Which of the following steps involves preparing data samples?

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

    What does the acronym SEMMA stand for in data science frameworks?

    <p>Sample, Explore, Modify, Model, Assess</p> Signup and view all the answers

    What activity comes after Developing the model in the standard data science process?

    <p>Applying the model on a dataset</p> Signup and view all the answers

    Which of the following frameworks is used in Six Sigma practice?

    <p>DMAIC</p> Signup and view all the answers

    Why is the data science process considered important?

    <p>It helps turn large data into useful information.</p> Signup and view all the answers

    What is the primary objective of the data science process?

    <p>To address an analysis question</p> Signup and view all the answers

    Which of the following factors is NOT considered in the prior knowledge step of the data science process?

    <p>Tools available for deployment</p> Signup and view all the answers

    Why is it important to accurately define the objective of a problem in the data science process?

    <p>To select the appropriate dataset and algorithm</p> Signup and view all the answers

    What challenge does the data science process face when uncovering patterns?

    <p>Identifying false or spurious signals</p> Signup and view all the answers

    Which of the following tools is NOT commonly associated with data science algorithms?

    <p>Excel</p> Signup and view all the answers

    What step follows the identification of the data needing to solve a problem in the data science process?

    <p>Data collection</p> Signup and view all the answers

    Which statement best describes prior knowledge in the context of the data science process?

    <p>It encompasses existing information relevant to the problem.</p> Signup and view all the answers

    What iterative nature does the data science process involve?

    <p>Going back to revise previous assumptions and tactics</p> Signup and view all the answers

    Study Notes

    Fundamentals of Data Science

    • Course: DS302
    • Instructor: Dr. Nermeen Ghazy

    Reference Books

    • 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

    Chapter 2: Data Science Process

    • The data science process is a set of iterative activities to discover relationships and patterns in data.
    • The standard data science process has five steps:
      • Understanding the problem
      • Preparing the data samples
      • Developing the model
      • Applying the model to a dataset
      • Deploying and maintaining the model

    Which is:

    • Prior Knowledge
    • Preparation
    • Modeling
    • Application
    • Knowledge

    Why is it important?

    • Wide availability of huge amounts of data and the need for turning it into useful information and knowledge.
    • Data mining is a result of the natural evolution of information technology.

    Data science process frameworks

    • Cross Industry Standard Process for Data Mining (CRISP-DM)
      • Widely adopted framework
    • Other frameworks include:
      • SEMMA (Sample, Explore, Modify, Model, and Assess)
      • DMAIC (Define, Measure, Analyze, Improve, and Control)

    CRISP-DM process

    • Six-phase process model
    • Naturally describes the data science life cycle
    • Helps plan, organize, and implement data science projects
    • The Business Understanding phase focuses on understanding the customer's needs
    • Data understanding focuses on identifying, collecting, and analyzing the data sets

    Data science Process

    • A general set of steps for data science tasks
    • Fundamental objective: address the analysis question.
    • Learning algorithms can be decision trees, neural networks, or scatterplots.
    • Software tools range from custom coding to RapidMiner, R, Weka, SAS, Oracle Data Miner, and Python.

    Data Science Process (Diagram)

    • Has various phases
    • Prior Knowledge
    • Preparation
    • Modeling
    • Application
    • Knowledge

    Prior Knowledge

    • Prior knowledge involves existing information about a subject.
    • Helps define the problem, its business context, and required data.
    • Steps include identifying the problem's objective and subject area, gathering relevant data.

    1. Objective of the Problem

    • The process starts with a problem, question, or business objective.
    • Well-defined objective is crucial.
    • Revising assumptions and strategies is common during the iterative process.

    2. Subject area of the Problem

    • Data science uncovers hidden patterns and relationships in data.
    • False signals are a problem—practitioners must assess patterns for validity.
    • Understanding the subject matter, context, and underlying business process is crucial.

    3. Data

    • Gathering prior data insights and knowledge sources.

    • Understanding source, storage, transformation, and utilization methods.

    • Surveys available data to meet the business needs and source new data.

    • Data quality, quantity, availability

    3-Data

    • Various factors to consider (quality, quantity, availability)
    • Identifying a dataset suitable for addressing the business question.

    Data Preparation

    • Preparing data for data science tasks is the most time-consuming.
    • Datasets are rarely in the desired format.
    • Data must be in a structured tabular format (rows and columns).

    Data Preparation Steps

    • Data Exploration and quality
    • Handling missing values
    • Data type conversion
    • Data transformations
    • Dealing with outliers and possible corrections
    • Feature selection
    • Sampling

    Data Exploration

    • Simple tools for achieving basic data understanding.
    • Use descriptive statistics and visualization.
    • Exposes data structure and inter-relationships.

    2- Data Quality

    • Data quality is crucial and ongoing.
    • Data correctness is key.
    • Data errors impact the representability of the model.

    3 - Handling Missing Values

    • Missing data common and has methods for mitigation.
    • Critical to understand why values are missing.
    • Replace missing values (mean, minimum, or maximum) if necessary

    4- Data Type Conversion

    • Attributes might be numeric, categorical, etc.
    • Data types need conversion for linear regression models
    • Grouping values into categories via binning

    5- Transformation

    • Algorithms sometimes need specific data formats.
    • Normalization (scaling to standard range).
    • This approach prevents one attribute from dominating

    6- Outliers

    • Outliers are data errors and/or data points that are unusual.
    • Outliers could indicate incorrect data recording or relevant to the issue
    • Data science applications require handling outliers

    7 - Feature Selection

    • Datasets may have many attributes to explore.
    • Crucial to look for important and useful aspects
    • Reduce complexity and boost model performance.

    8- Sampling

    • Selecting a subset to represent the original dataset for better analysis.
    • Reduces dataset processing time. This is part of data preparation phase.

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    Fundamentals of Data Science DS302 PDF

    Description

    Explore the fundamentals of the data science process in this quiz from the DS302 course. Learn about the five iterative steps crucial for discovering patterns in data and understand why this methodology is essential in the era of big data. Test your knowledge on key concepts and frameworks discussed in class.

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