Data Science Fundamentals Lecture 2
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Questions and Answers

What is the first step in the data science process?

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

The CRISP-DM process is primarily focused on the data collection phase.

False (B)

Name one other data science framework aside from CRISP-DM.

SE MMA or DMAIC

The __________ phase in CRISP-DM focuses on understanding the objectives and requirements of the project.

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

Match the following data science frameworks with their descriptions:

<p>CRISP-DM = Cross Industry Standard Process for Data Mining SE MMA = Sample, Explore, Modify, Model, and Assess DMAIC = Define, Measure, Analyze, Improve, Control KDD = Knowledge Discovery in Databases</p> Signup and view all the answers

What is the primary purpose of data mining in the context of data science?

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

The data science process is a linear sequence of steps that does not require iteration.

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

What is the primary purpose of data exploration?

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

List the five main steps of the standard data science process.

<p>Understanding the problem, Preparing the data samples, Developing the model, Applying the model, Deploying and maintaining the models.</p> Signup and view all the answers

Data cleansing is only required before storing data into a data warehouse.

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

What is one common data quality issue faced in datasets?

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

Descriptive statistics like mean, median, and mode provide a _______ summary of the data.

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

Match the following data quality practices with their descriptions:

<p>Data Alerts = Notify users of abnormal data values Cleansing = Removal of duplicate records Transformation = Changing the format of data values Outliers handling = Quarantining data that exceed expected bounds</p> Signup and view all the answers

Which statistical method helps highlight the relationship between credit score and interest rate?

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

Data sourced from well-maintained data warehouses generally have lower quality.

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

What should be the first step in managing missing values?

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

What is the primary objective of the data science process?

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

Prior knowledge in the data science process is not essential to define the problem.

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

What are some software tools used in the data science process?

<p>RapidMiner, R, Weka, SAS, Oracle Data Miner, Python</p> Signup and view all the answers

The ________ area of the problem helps uncover hidden patterns in the dataset.

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

Match the following components of the data science process with their descriptions:

<p>Objective of the problem = The need for data analysis Subject area = Domain knowledge relevant to the data Prior knowledge = Information that is already known about a topic Algorithms = Methods for solving a business question</p> Signup and view all the answers

Which of the following is a common issue faced during the data science process?

<p>False or spurious signals (D)</p> Signup and view all the answers

The data science process is a fixed series of steps without any iterations.

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

What must be carefully defined as the first step in the data science process?

<p>The objective of the problem</p> Signup and view all the answers

What is the purpose of assessing the available data in the data science process?

<p>To determine if new data needs to be sourced (A)</p> Signup and view all the answers

A dataset is always available in the format required by data science algorithms.

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

What is the term used for a single instance in a dataset?

<p>data point</p> Signup and view all the answers

A collection of data with a defined structure is known as a __________.

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

Match the following terms with their definitions:

<p>Dataset = A single instance in a dataset Data point = A collection of data with a defined structure Identifier = Special attributes used for locating records Label = The special attribute to be predicted</p> Signup and view all the answers

Which of the following factors should be considered when assessing data for a business question?

<p>Quality, quantity, and gaps in data (D)</p> Signup and view all the answers

Identifiers in a dataset are used to show the relationships between different attributes.

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

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

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

What is one primary application of detecting outliers in data science?

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

A large number of attributes in a dataset can improve the performance of a model.

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

What is the purpose of sampling in data analysis?

<p>To select a subset of records that represent the original dataset.</p> Signup and view all the answers

What is one method used to handle missing credit score values?

<p>Replace with the maximum value in the dataset (C)</p> Signup and view all the answers

Sampling reduces the amount of data that needs to be processed and speeds up the __________ process of modeling.

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

Match the following terms with their definitions:

<p>Feature selection = Identifying important attributes for prediction Curse of dimensionality = Degradation of model performance due to too many features Outlier detection = Identifying data points that are significantly different from others Sampling = Selecting a subset of data to represent a larger dataset</p> Signup and view all the answers

Ignoring records with missing values increases the size of the dataset.

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

What is the process called that converts numeric values to categorical data types?

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

Normalization helps prevent one attribute from dominating the _______ results.

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

Which of the following is considered an outlier?

<p>A human height recorded as 1.73 cm instead of 1.73 m (D)</p> Signup and view all the answers

All data science algorithms require input attributes to be numeric.

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

What attribute type must be transformed for linear regression models?

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

Match the following terms with their definitions:

<p>Outliers = Anomalies in the dataset Normalization = Scaling attributes to a similar range Binning = Converting numeric data into categories Missing values = Absences of data points in a dataset</p> Signup and view all the answers

Flashcards

Data Science Process

A set of steps in data science, agnostic to specific problems, algorithms, or tools. Its goal is to address analysis questions.

Prior Knowledge

Existing information related to the subject of analysis; essential for defining the problem, its context, and necessary data.

Objective of the problem

The core analysis question or business goal driving the data science project.

Subject area of the problem

Understanding the context, business process, and relevant factors affecting the data.

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Data Understanding

Identifying, collecting, and analyzing datasets to improve the knowledge of the data.

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Data Science Process Steps

A series of steps to solve a business problem or answer a question using data.

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Data Science Process

A set of iterative activities for discovering patterns and relationships in data.

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Data Science Steps

A five-step process including understanding the problem, data preparation, model development, real-world application, model deployment and maintenance.

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CRISP-DM

Cross-Industry Standard Process for Data Mining; a widely adopted framework for data science.

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Business Understanding

The first phase of CRISP-DM focusing on understanding project goals and customer needs.

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Data Mining

The process of discovering knowledge from data.

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Data Science Importance

Data science is important because of the large amounts of data available and the need to derive useful information from it.

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Data Science Frameworks

Structured approaches like CRISP-DM, SEMMA, DMAIC, and others that help organize data science projects.

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Outlier Detection

Identifying unusual data points in a dataset, often used in fraud or intrusion detection.

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Feature Selection

Choosing the most relevant attributes from a dataset to improve model performance and reduce complexity.

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Curse of Dimensionality

The problem where increasing the number of attributes in a dataset makes model building more difficult and performance degrades.

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Sampling

Selecting a portion of data to represent the whole dataset.

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Data attributes

Individual characteristics or properties that describe the data items.

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Significant Increase in Complexity

Adding too many features makes building models significantly more difficult, leading to potentially decreased performance.

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Dataset

A collection of data, organized in rows and columns, frequently used in data science and analytics.

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Representative Sample

A subset of data that shares similar characteristics with the original dataset.

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Prior Knowledge (Data)

Gathering information about how data is collected, stored, transformed, reported, and used; examining existing data to find information needed to answer a business question.

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Handling Missing Credit Scores

Replace missing credit scores with calculated values (mean, min, max) or remove records with missing or poor quality values.

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Data Type Conversion

Transforming data attributes like credit scores from numeric to categorical (e.g., poor, good, excellent) or vice-versa for use with specific algorithms.

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Data Quality

Assessing the accuracy, completeness, and consistency of data.

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Data Quantity

The amount of data available for a project or analysis.

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Credit Score Categorization

Assigning credit scores to categories (e.g., low, medium, high).

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Data Availability

How accessible and obtainable is the needed data?

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Binning

Grouping numeric values into categories based on ranges.

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Data Gaps

Missing or incomplete data points in a dataset.

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Normalization

Scaling attributes to a common range (e.g., 0 to 1), preventing attributes with larger values from dominating distance calculations.

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Dataset

A collection of data organized in rows and columns (table) with defined structure; a well-defined set of data

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k-NN Normalization

Normalization is vital in k-NN algorithms because these algorithms calculate distance between data points.

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Data Point (record)

A single piece of information (row) within a dataset.

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Outliers in Data

Abnormal data points in a dataset that might be due to errors or unusual values.

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Outlier Treatment

Understanding and handling outliers in a dataset, recognizing their source (errors or anomalies) and deciding on how to treat them.

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Label (output)

The attribute (column) in a dataset that describes the thing you are trying to predict (e.g., high/low interest rate).

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Identifier Attributes

Attributes (columns) used to distinguish individual records, such as names or account numbers.

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Data Preparation

Transforming data into a usable format for data science algorithms; making data suitable for analysis.

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Data Exploration

Understanding the characteristics and structure of a dataset.

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Data Exploration

A set of tools for understanding data by computing descriptive statistics and visualizing data.

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Data Frame

Table-like data structure that's used to organize data into rows and columns.

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Descriptive Statistics

Summary measures of data distribution, like mean, median, mode, standard deviation, and range.

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Data Quality

The accuracy and reliability of data, crucial for model representativeness.

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Missing Attribute Values

Data records lacking certain values for one or more attributes.

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Handling Missing Values

Methods used to deal with missing records or data points.

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Data Cleansing

Techniques for improving data quality by eliminating duplicates, outliers, and fixing inconsistencies.

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Data Warehouses

Company-wide repositories that store data, ensuring quality controls for accuracy.

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Outliers

Extreme values that significantly deviate from other data points.

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Feature Selection

Choosing the most important features in the data for analysis.

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Sampling

Selecting a subset (sample) of data to represent the whole dataset.

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Data Type Conversion

Changing the format of data values to the correct data type.

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Transformation

Changing the form of the data to improve it for analysis.

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Study Notes

Fundamentals of Data Science

  • The methodical discovery of useful relationships and patterns in data is enabled by a series of iterative activities known as the data science process.
  • The standard data science process includes: understanding the problem, preparing data samples, developing a model, applying the model to a dataset to see how it works in the real world, deploying the model, and maintaining it.
  • Lecture 2 covers the data science process.

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)

Data Science Process Frameworks

  • CRISP-DM (Cross Industry Standard Process for Data Mining).
  • SEMMA (Sample, Explore, Modify, Model, and Assess).
  • DMAIC (Define, Measure, Analyze, Improve, and Control).
  • These are frameworks for data science solutions.

CRISP-DM Process

  • CRISP-DM is a process model with six phases that naturally describes the data science life cycle.
  • Helps plan, organize, and implement data science (or machine learning) projects.
  • Business Understanding phase focuses on understanding the customer's needs.
  • Data Understanding phase focuses on identifying, collecting, and analyzing data sets.

Data Science Process (General)

  • The fundamental objective of any data science process is to answer the analysis question.
  • The learning algorithm used for a business question can be a decision tree, artificial neural network, or scatterplot.
  • Software tools for developing and implementing data science algorithms include custom coding, RapidMiner, R, Weka, SAS, Oracle Data Miner, and Python.

Prior Knowledge

  • Prior knowledge is information already known about a subject.
  • The step in data science helps define the problem, its context, and needed data.
  • Essential factors to gain information on are:
    • Objective of the problem
    • Subject area of the problem
    • Data
  • The objective of the data science process starts with the need for analysis, or a question, or a business objective..
  • It's imperative to get this first step right.
  • It's common to go back to previous steps and revise assumptions when faced with an iterative process.
  • The process of data science uncovers hidden patterns and relationships.
  • Identifying and isolating useful patterns are crucial from a large number of patterns.

Data

  • Survey all available data to narrow down needed new data.
  • Data quality is a crucial factor. This relates to data quantity, availability, gaps, and if a lack of data compels changing the business question.
  • Data can be collected, stored, transformed, reported, and used.
  • A dataset is a collection of data with a defined structure (like rows and columns).
  • A data point is a single instance, record, or object in a dataset.
  • Each row in the dataset is a data point.
  • Identifiers (PK) are attributes used for locating and providing context to individual records (e.g., names, accounts, employee IDs).

Data Preparation

  • Preparing the dataset is a critical, time-consuming part of the data science process.
  • Data often isn't in the required format for algorithms.
  • The data is typically structured in a tabular format.
  • Raw data needs to be converted to the required format using pivot, type conversion, join, transpose functions.

Data Preparation Details

  • Data exploration, data quality, handling missing values, data type conversion, transformation, outliers, feature selection, and sampling are essential steps.

Data Exploration

  • Data exploration, also called exploratory data analysis.
  • Provides simple tools for basic data understanding.
  • Involves calculating descriptive statistics and visualizations to understand data structure, distributions, extremes, and interrelationships.
  • Descriptive statistics like mean, median, mode, standard deviation, range summarize key characteristics.
  • Scatterplots of variables can show relationships.

Data Quality

  • Data quality is important in all data collection, processing, and storage.
  • Data in a dataset must be accurate.
  • Data sources like data warehouses contain better quality data due to better controls.
  • Data cleansing techniques include eliminating duplicates, handling outliers, standardizing attribute values, replacing missing values.

Handling Missing Values

  • One of the most common data quality issues is missing attribute values.
  • Techniques use to manage missing values include replacing missing values with derived values from the dataset (e.g. mean, minimum or maximum).
  • Alternatively, data records with missing values can be ignored to create a model.
  • This process reduces the size of the dataset

Data Type Conversion

  • Attributes in data sets can be various types (e.g., continuous numeric, integer numeric, categorical).
  • Different algorithms expect different data types.
  • Categorical data (e.g., low, med, high) needs conversion to numeric for models like linear regression.
  • Binning is a technique for grouping numeric values into categories (e.g., < 500 = low, 500 to 700 = med, > 700 = high).

Transformation

  • Algorithms like k-nearest neighbor (k-NN) expect numeric and normalized input attributes.
  • Attributes with large values can distort calculations.
  • Normalization converts ranges to a uniform scale (e.g., 0 to 1).

Outliers

  • Outliers are unusual data points.
  • Correct or erroneous data capture can cause outliers.
  • Their presence needs understanding and special treatment.
  • Outliers might warrant special data science application, like fraud detection.

Feature Selection

  • Datasets may have hundreds or thousands of attributes.
  • A large number of attributes increases model complexity and may decrease model performance.
  • Not all attributes are equally important for the target variable.

Sampling

  • Sampling involves selecting a subset of data for use in data analysis/modeling.
  • Samples should represent the original dataset (similar properties).
  • Sampling reduces processing needed and speeds up building models.
  • Sampling is often crucial for obtaining insights and building representative models.
  • Theoretically, sampling may decrease model relevance, but practical benefits outweigh this risk.

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This quiz assesses your understanding of the data science process, including key frameworks like CRISP-DM, SEMMA, and DMAIC. It covers the iterative activities involved in discovering patterns and relationships within data. Prepare to demonstrate your knowledge of these essential concepts in data science.

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