Data Analytics Lifecycle and Predictive Models
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Questions and Answers

Which activity is NOT part of the Discovery phase in the Data Analytics Lifecycle?

  • Conducting stakeholder interviews
  • Framing the business problem
  • Performing data cleaning (correct)
  • Assessing available resources

    • What does the acronym ETLT stand for in data preparation?

  • Extract, Transform, Load, Test
  • Extract, Trust, Load, Transform
  • Extraction, Transformation, and Loading Techniques
  • Extract, Transform, Load, and Enrich (correct)

    • During which phase does the data science team explore data relationships and select key variables?

  • Model Planning (correct)
  • Discovery
  • Data Preparation
  • Model Building

    • Which is an objective of the personal loan approval model discussed in the content?

    <p>Reducing default rates</p> Signup and view all the answers

    What is the primary purpose of conducting stakeholder interviews in the Discovery phase?

    <p>To gather requirements and objectives</p> Signup and view all the answers

    What type of changes are made to applicant income and employment history during data preparation?

    <p>Standardizing formats for analysis</p> Signup and view all the answers

    Which aspect is emphasized in the Model Planning phase?

    <p>Selecting appropriate modeling techniques</p> Signup and view all the answers

    What is the overarching goal of the entire Data Analytics Lifecycle?

    <p>To solve business and organizational problems</p> Signup and view all the answers

    What is a primary goal of the model building phase in data science?

    <p>To develop an analytical model and evaluate its performance</p> Signup and view all the answers

    What is a critical aspect of deploying a loan approval prediction model?

    <p>Setting up continuous monitoring and updating of the model</p> Signup and view all the answers

    Which of the following best describes the relationship between the model planning and model building phases?

    <p>They may overlap, allowing for iteration between the phases</p> Signup and view all the answers

    What should be documented during the model building phase?

    <p>Decisions regarding data transformation and feature creation</p> Signup and view all the answers

    Why is careful data preparation essential before applying a Linear Regression model?

    <p>Improperly prepared data can significantly affect model accuracy</p> Signup and view all the answers

    In what scenario is a Linear Regression model typically most appropriate?

    <p>When the relationship between variables is assumed to be linear</p> Signup and view all the answers

    Which software tools are commonly used during the model building phase?

    <p>A mix of commercial and open-source software</p> Signup and view all the answers

    What is a potential disadvantage of complex modeling techniques?

    <p>They can be more time-consuming compared to the data preparation phase</p> Signup and view all the answers

    What does Occam’s Razor principle suggest about possible explanations for an event?

    <p>The simplest explanation is usually the best.</p> Signup and view all the answers

    Which method can be effective in removing highly correlated input data?

    <p>Calculating pairwise correlations to identify and remove correlated pairs.</p> Signup and view all the answers

    What characterizes outliers in a dataset?

    <p>They are significantly different from the majority of data points.</p> Signup and view all the answers

    How does collinearity affect regression analysis?

    <p>It complicates the ability to determine the individual effects of predictors.</p> Signup and view all the answers

    What type of distribution benefits linear regression reliability the most?

    <p>Gaussian distribution.</p> Signup and view all the answers

    What is the purpose of normalization in data preparation?

    <p>To transform observations to resemble a normal distribution.</p> Signup and view all the answers

    When dealing with non-linear problems, what approach can simplify the solution?

    <p>Non-linear transformations to model as linear problems.</p> Signup and view all the answers

    What effect does data transformation have on achieving a Gaussian-like distribution?

    <p>It makes the distribution conform more closely to Gaussian characteristics.</p> Signup and view all the answers

    Which of the following tools is specifically a procedural language for PostgreSQL that allows R commands to be executed?

    <p>PL/R</p> Signup and view all the answers

    What is primarily addressed by predictive models?

    <p>Predicting attributes of labeled objects</p> Signup and view all the answers

    In the context of predictive models, what is the main purpose of a classification problem?

    <p>To label new, unseen data based on training data</p> Signup and view all the answers

    What distinguishes predictive models from unsupervised models like K-Means Clustering?

    <p>Predictive models rely on labeled data to form predictions</p> Signup and view all the answers

    Which of the following programming languages is mentioned as having functionalities similar to Matlab?

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

    Which data mining package is known for its analytic workbench and Java API?

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

    What type of dataset is provided to models during the training phase of predictive modeling?

    <p>A set of labeled examples</p> Signup and view all the answers

    Which of the following Python libraries is NOT mentioned in relation to data visualization?

    <p>scikit-learn</p> Signup and view all the answers

    What does the standard deviation measure regarding a set of numbers?

    <p>How far the numbers are spread from their average value</p> Signup and view all the answers

    In the Pearson's correlation coefficient formula, what do the variables $µ_x$ and $µ_y$ represent?

    <p>The means of the two datasets</p> Signup and view all the answers

    What is the formula for calculating the mean of a dataset X?

    <p>$ rac{ ext{Sum of all } x_i}{N}$</p> Signup and view all the answers

    Which of the following formulas represents standard deviation for dataset X?

    <p>$s_x = rac{ ext{Sum of }(x - µ_x)^2}{N - 1}$</p> Signup and view all the answers

    What does Pearson’s correlation coefficient measure?

    <p>The strength of association between two variables</p> Signup and view all the answers

    What is represented by the symbol $N$ in the formulas provided?

    <p>The total number of data points</p> Signup and view all the answers

    How is the sample variance for dataset Y expressed mathematically?

    <p>$s_y^2 = rac{ ext{Sum of }(y_i - µ_y)^2}{N - 1}$</p> Signup and view all the answers

    What is a key characteristic of the standard deviation formula when calculating sample variance?

    <p>It divides by N-1</p> Signup and view all the answers

    Study Notes

    Data Analytics Lifecycle

    • Discovery: The data science team learns about the business, assessing the resources available for the project.
    • Data Preparation: The team prepares the data for analysis; this includes an Extraction, Transform, and Load (ETLT) process.
    • Model Planning: The team determines the methods, techniques, and workflow for the model-building phase.
    • Model Building: The team develops the analytical model, fits it on the training data, and evaluates its performance on test data.

    Predictive Models

    • Predictive models are used for predicting specific attributes of a given object.
    • Predictive models can be used in tasks where the goal is to classify something (eg: will a patient survive a specific disease).
    • Predictive models are different from unsupervised models which are limited to finding patterns within data.

    Linear Regression Model

    • Linear regression models predict a target value using a linear equation that represents the relationship between the target and input variables.
    • It's important to prepare data before applying a linear regression model.
    • Some strategies for data preparation include making data distributions Gaussian (normal), rescaling the input, and handling outliers.

    Key Concepts for Linear Regression

    • Standard Deviation: Measures how far the random numbers are spread out from their average.
    • Pearson's Correlation Coefficient: Measures the strength of association between two variables.
    • Collinearity: This occurs when two or more predictors are closely related, making it difficult to determine their individual influence on the output.
    • Outliers: Data points that differ significantly from other data points; outliers may influence model results.
    • Non-Linear Transformation: Non-linear transformations help to transform a non-linear problem into a linear one so it can be solved using linear regression.

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    COS10022_Lecture 02_Linear Regression.pdf

    Description

    This quiz covers key concepts in the data analytics lifecycle including discovery, data preparation, model planning, and model building. It also delves into predictive models, focusing on their use in classification tasks and the specifics of linear regression. Test your understanding of these vital data science topics.

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