Data Science Process - DS302 Lecture 2
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Questions and Answers

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

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

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

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

<p>Applying pivot functions (A)</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 (B)</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 (D)</p> Signup and view all the answers

What is the primary goal of data exploration?

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

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

<p>Mean (C)</p> Signup and view all the answers

What is a common issue related to data quality?

<p>Missing attribute values. (C)</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. (B)</p> Signup and view all the answers

Which method can be used to improve data quality?

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

Which process involves standardizing attribute values in a dataset?

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

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

<p>Inverse correlation. (B)</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 (B)</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 (A)</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 (C)</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 (B)</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 (D)</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 (B)</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. (D)</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. (A)</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. (B)</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. (C)</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. (B)</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. (D)</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. (D)</p> Signup and view all the answers

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

<p>Understanding the problem (C)</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 (B)</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 (D)</p> Signup and view all the answers

Which of the following steps involves preparing data samples?

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

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

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

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

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

Why is the data science process considered important?

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

What is the primary objective of the data science process?

<p>To address an analysis question (C)</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 (D)</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 (A)</p> Signup and view all the answers

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

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

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

<p>Excel (C)</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 (D)</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. (C)</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 (D)</p> Signup and view all the answers

Flashcards

Data Understanding

The stage in the data science process where data sets are identified, collected, and analyzed.

Data Science Process

A series of steps used in data science to tackle analysis problems. It's independent of the specific problem, algorithm, or tool.

Prior Knowledge

Existing information about the subject area or problem that guides the data science process.

Objective of the problem

The goal or desired outcome of the data analysis; the overarching question.

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Subject area of the problem

The specific domain or context of the problem, which helps distinguish valuable insights from noise.

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Data Science Algorithm

The method or technique used to analyze data and solve the problem. Examples include decision trees, neural networks, and scatter plots.

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Prior Knowledge in Data

Understanding how data is collected, stored, transformed, reported, and used in the data science process.

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Data Quality

Characteristics of data, including accuracy, completeness, consistency, and timeliness, impacting data science tasks.

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Data Quantity

The amount of data available for analysis.

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Data Availability

Extent to which data is accessible for the intended use.

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Data Gaps

Missing or incomplete data points that may affect the accuracy of a chosen business question.

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Dataset

A structured collection of data, organized in rows and columns (like a table).

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Data Point

A single piece of information within a dataset.

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Label (Data Science)

The attribute in a dataset to be predicted based on other attributes.

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Identifier (Data Science)

Attribute used to uniquely identify each data point.

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Data Preparation

Transforming data into a suitable format for data science algorithms, often the most time-consuming step.

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Data Exploration

Initial analysis of data to understand its properties and patterns before applying data science techniques.

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Data Science Algorithms

Specific programs meant for data analysis tasks.

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Outlier Detection

Identifying unusual data points in a dataset.

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Missing Credit Score Values

Missing credit scores can be replaced with a calculated value from the dataset (mean, minimum, or maximum depending on data characteristics).

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Data Type Conversion

Converting data attributes between types like numeric (continuous or integer) and categorical (e.g. 'poor', 'good', 'excellent').

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Feature Selection

Choosing the most important features from a dataset to improve model performance.

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Data Normalization

Scaling attributes to a uniform range (e.g., 0 to 1), important for algorithms like k-NN, to prevent one attribute dominating distances.

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Curse of Dimensionality

The problem that arises when dealing with many attributes, making models harder and less efficient (more complex problems).

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Sampling

Selecting a subset of data to represent the entire dataset.

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Outliers

Anomalous data points in a dataset, potentially resulting from correct data entry or errors.

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Credit Score Categorization

Representing credit scores as categorical values (e.g., 'poor', 'good', 'excellent') or as numeric scores.

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Representative Sample

A sample with similar characteristics to the original dataset.

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Removing Poor Quality Data

Discarding data records with missing values or poor data quality to reduce dataset size.

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Binning Technique

Converting numeric values into categorical data by assigning ranges of values to categories.

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k-Nearest Neighbour (k-NN)

A data science algorithm that calculates distances between data points to find similar data points, requiring numerical and normalized input attributes.

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Data Exploration

A set of simple tools to understand data, including descriptive statistics and visualizations. It helps reveal data structure, distributions, outliers, and relationships.

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Descriptive Statistics

Summary measures like mean, median, mode, standard deviation, and range, used to describe data distribution.

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Data Quality

The trustworthiness and accuracy of data. Ensuring data is reliable for model building.

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Missing Values

Records with missing attribute values, a common data quality issue.

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Handling Missing Values

Methods for dealing with records having missing attribute values. Different methods have advantages and disadvantages.

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Data Cleansing

Techniques to improve data quality, including removing duplicates, managing outliers, standardizing values, and handling missing data.

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Data Warehouses

Company-wide repositories for high-quality data, with controls to maintain accuracy.

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Outliers

Extreme values that fall far from other values in a dataset.

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Feature Selection

Choosing the most relevant features (attributes) from a dataset for model building.

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Sampling

Selecting a representative subset of data from a larger dataset.

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Data Type Conversion

Changing the format of data from one type to another.

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Transformation

Changing the form or structure of data for use in modeling.

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Data Science Process

A series of iterative activities to discover useful patterns in data.

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CRISP-DM

Cross Industry Standard Process for Data Mining - a popular data science framework.

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Data Science Phases

A set of 6-phases in the data science life cycle.

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Business Understanding

The phase in CRISP-DM to understand project objectives and requirements, focusing on customer needs.

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Data Preparation

Gathering, cleaning, organizing data samples for the analysis.

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Model Development

Creating and refining the data science model.

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Model Application

Using the developed model on a dataset to understand its real-world performance.

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Model Deployment & Maintenance

Using and maintaining the data science model in real-world scenarios.

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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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