Data Validation and Standardization Quiz
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Questions and Answers

What is a method for correcting address data using geographic names?

  • Employing social security numbers
  • Verifying phone numbers
  • Using online search engines
  • Using dictionaries on geographic names and zip codes (correct)

    • What constitutes an age validation error based on the specified rules?

  • An age of 25
  • An age of 30
  • An age of 50
  • An age of 70 (correct)

    • In a validation rule that requires certain categorical values, which of the following would be detected as an error?

  • Category W (correct)
  • Category B
  • Category C
  • Category A

    • Which of the following can lead to ambiguous values in data entry?

    <p>Employing the same category value for different meanings</p> Signup and view all the answers

    Why is standardizing data values necessary?

    <p>To ensure values are consistent and have a uniform format</p> Signup and view all the answers

    Which of the following date representations demonstrates a lack of standardization?

    <p>October 19, 2009</p> Signup and view all the answers

    What is one example of standardizing string data?

    <p>Converting names to upper or lower case</p> Signup and view all the answers

    What is a correct procedure for resolving abbreviations in data?

    <p>Applying predefined conversion rules</p> Signup and view all the answers

    What is the first step in managing missing values in a dataset?

    <p>Understand the reason behind why the values are missing</p> Signup and view all the answers

    Which method is most appropriate for handling missing values that occur randomly and infrequently?

    <p>Replacing missing values with the mean or median value</p> Signup and view all the answers

    What must be done to categorical data before using linear regression models?

    <p>It must be converted to continuous numeric attributes.</p> Signup and view all the answers

    What is the purpose of normalization in algorithms like k-nearest neighbor (k-NN)?

    <p>To eliminate the influence of minor attribute variations</p> Signup and view all the answers

    How can numeric values be transformed into categorical data?

    <p>By using the binning technique</p> Signup and view all the answers

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

    <p>It may lead to biased results due to selective data removal.</p> Signup and view all the answers

    Which attribute type is typically used for interest rates in a dataset?

    <p>Continuous numeric</p> Signup and view all the answers

    In data transformation, what scale can attributes be normalized to?

    <p>0 to 1</p> Signup and view all the answers

    What are outliers in a dataset?

    <p>Anomalies that can occur due to accurate or erroneous data capture</p> Signup and view all the answers

    Why does a large number of attributes in a dataset create challenges?

    <p>It may lead to the curse of dimensionality, complicating models</p> Signup and view all the answers

    What is the main purpose of sampling in data analysis?

    <p>To reduce processing time with a representative subset</p> Signup and view all the answers

    Which of the following best describes a model in the context of data science?

    <p>An abstract representation of data and relationships</p> Signup and view all the answers

    What is true about association analysis and clustering techniques?

    <p>They do not involve prediction and lack a test dataset</p> Signup and view all the answers

    What is the purpose of identifying outliers in a data set?

    <p>To ensure they are not caused by data entry errors.</p> Signup and view all the answers

    What is a significant risk of using sampling in data science?

    <p>Errors introduced that may affect the model's relevancy</p> Signup and view all the answers

    Which statement about feature selection is correct?

    <p>Feature selection helps identify useful attributes for predictions</p> Signup and view all the answers

    Which quartile divides the data set into two equal halves?

    <p>Q2</p> Signup and view all the answers

    How is the first quartile (Q1) calculated?

    <p>It is the median of the lower half of the data.</p> Signup and view all the answers

    What can outliers indicate in a dataset?

    <p>They can indicate unique or rare occurrences within data</p> Signup and view all the answers

    In the example data set 6, 47, 49, 15, 42, 41, 7, 39, 43, 40, 36, what is the value of Q3?

    <p>43</p> Signup and view all the answers

    When computing quartiles, what should be done if the median is also one of the data items?

    <p>Exclude it from any further computations.</p> Signup and view all the answers

    What is the first step to compute Q1, Q2, and Q3?

    <p>Order the given data set in ascending order.</p> Signup and view all the answers

    For the data set 39, 36, 7, 40, 41, 17, what is the value of Q2?

    <p>37.5</p> Signup and view all the answers

    What percentage of data items does Q1 cut off from the lowest end?

    <p>25%</p> Signup and view all the answers

    What is a primary objective of data exploration?

    <p>To identify and analyze anomalies in datasets.</p> Signup and view all the answers

    Which type of data exploration focuses on a single attribute at a time?

    <p>Univariate exploration</p> Signup and view all the answers

    Which of the following is NOT an attribute of the Iris dataset?

    <p>Flower height</p> Signup and view all the answers

    Scatterplots in data exploration are primarily used for which purpose?

    <p>To identify clusters in low-dimensional data.</p> Signup and view all the answers

    Which of the following correctly describes descriptive statistics?

    <p>It summarizes aggregate quantities of a dataset.</p> Signup and view all the answers

    What is one benefit of using histograms in data exploration?

    <p>They visualize data distribution and error rate estimation.</p> Signup and view all the answers

    The Iris dataset consists of how many observations for each species?

    <p>50</p> Signup and view all the answers

    Which type of exploration considers multiple attributes simultaneously?

    <p>Multivariate exploration</p> Signup and view all the answers

    What does the symbol r represent in statistics?

    <p>Sample correlation coefficient</p> Signup and view all the answers

    What is the range of values for the correlation coefficient r?

    <p>-1 to 1</p> Signup and view all the answers

    If two variables have a correlation coefficient r close to -1, what does this indicate?

    <p>A strong negative linear correlation</p> Signup and view all the answers

    Which of the following is NOT a motivation for using data visualization?

    <p>Elimination of numerical data</p> Signup and view all the answers

    What type of visualization focuses on the relationship between one attribute and another?

    <p>Multivariate visualization</p> Signup and view all the answers

    Which method is NOT a common approach for visualizing data relationships?

    <p>Using numerical data only</p> Signup and view all the answers

    What is the main benefit of visualizing data in a scatter plot?

    <p>To easily see patterns or correlations between two variables</p> Signup and view all the answers

    How does univariate visualization assist in data exploration?

    <p>By investigating the distribution of a single attribute</p> Signup and view all the answers

    Study Notes

    Introduction to Data Science

    • Dr. Amal Fouad is a Senior Data Scientist
    • Introduction to Data Science presentation

    Agenda

    • Why study Data Science?
    • How Does Data Science Impact Organizations?
    • Application and Competitive Advantage
    • Importance of Data Science
    • What Will Be Discussed

    DS vs AI vs ML vs DL?

    • Artificial Intelligence: Any technique that allows computers to mimic human behavior
    • Machine Learning: A subset of AI techniques that use statistical methods to enable machines to improve with experience
    • Deep Learning: A subset of Machine Learning that makes the computation of multi-layer neural networks feasible

    DS vs AI vs ML vs DL? (Timeline)

    • Early Artificial Intelligence (1950s)
    • Machine Learning Flourishes (1990s-2000s)
    • Deep Learning Breakthroughs (2010s)

    Why Data Science?

    • Information Created Worldwide: Expected to continuously accelerate, data is growing exponentially.
    • 2005 - 0.1 ZB of data
    • 2010 - 2 ZB of data (9%)
    • 2015 - 12 ZB of data (9%)
    • 2020 - 47 ZB of data (16%)
    • 2025 - 163 ZB of data (36%)
    • Data accumulating at 28% annual growth rate
    • Data Analysts in the workforce growing at 5.7% annual growth rate
    • Data Analyst shortage
    • Data is a huge field with lots of opportunities to study

    Components of Data Science

    • Artificial Intelligence (AI): This is a broad field encompassing many techniques to mimic human behaviour
    • Machine Learning (ML): Methods used in AI to enable machines to learn from data
    • Deep Learning (DL): A specific technique of ML that involves networks of multiple layers
    • Maths & Statistics: Statistical models used for data analysis
    • Visualization: Using charts,graphs to visually present the data for better understanding
    • EDA (Exploratory Data Analysis): Process of discovering patterns in the data

    Data Definition

    • Data is a collection of details, figures, symbols, descriptions
    • Raw data can be in various forms like letters, numbers, images, characters
    • Computer data is represented (in most cases) by a combination of 0's and 1's
    • Information is data to which meaning has been attached

    Databases

    • Oracle
    • MySQL
    • Microsoft SQL Server
    • MongoDB
    • PostgreSQL

    Data Definition (Databases aspect)

    • Databases are logical structures that rely on set theory, relationships between tables, and unique primary and foreign keys.
    • This keeps data integrity between tables
    • Data Science methods, processes and insight concerning structured or unstructured data

    Data Science Definition

    • Data science is an interdisciplinary field that employs scientific methods, processes, algorithms, and systems to obtain knowledge and insights from structured or unstructured data.
    • Data Science is applied in many areas

    Data Science Definition (Unified concepts)

    • Data science is a concept to unify statistics, data analysis, machine learning and their related methods.
    • Used for understanding and analyzing actual phenomena with the help of data
    • Drawn from mathematics, statistics, computer science, and information science

    Data Science Definition (Roles & skills)

    • Analyst - Business administration. Exploratory Data Analysis.
    • Data Scientist - Advanced algorithms. Machine learning. Data preparation. Data governance. Data product engineering. SQL.
    • Data Engineer - Builds data pipelines.
    • Process Owner - Project management. Management of stakeholder expectations. Maintaining a vision. Facilitating processes

    Data Scientist Starter Pack - Libraries

    • NumPy: A Python library for large, multi-dimensional arrays and matrices in order to use high-level mathematical numerical functions.
    • Scikit-learn: A free software library for classification, regression and clustering algorithms.
    • Pandas: A Python library for data manipulation and analysis of data structures.

    Data Scientist Starter Pack - Tools

    • Jupyter: An open-source web application to create and share documents containing live code, equations, diagrams and narrative text.
    • Colab: similar to Jupyter Notebook but provides sharing and collaboration features.

    Data Scientist Applications

    • E-commerce: Identifying customers, recommending products, analyzing reviews
    • Manufacturing: Predicting problems, monitoring systems, automating manufacturing units, maintenance scheduling, anomaly detection
    • Healthcare: Medical image analysis, drug discovery, bioinformatics, virtual assistants
    • Transport: Self-driving cars, enhanced driving experience, car monitoring system, enhancing passenger safety
    • Banking: Fraud detection. Credit risk modeling. Customer lifetime value
    • Finance: Customer segmentation, strategic decision making, algorithmic trading, risk analysis

    Data Science - Importance

    • Data science helps brands to deeply understand their customers
    • Data science provides powerful and engaging ways for brands to communicate their story
    • The field of Big Data is a continuously evolving field

    Data Science - Importance (Specific areas)

    • Data Science findings and results are applicable to sectors like travel, healthcare and education.
    • Data science is accessible to most sectors

    Data Science Roadmap

    • Includes different areas of expertise, math, probability, statistics, programming, machine learning, deep learning, NLP, database, visualization tools, and deployment.
    • Includes project on various topics including credit card fraud detection, movie recommendation, data science at Netflix, data science at Flipkart

    How YouTube uses AI

    • Automatically removes objectionable content
    • Recommends videos based on user choice
    • Organizes content

    How Facebook uses AI

    • DeepText
    • Machine Translation
    • Automatic Image Recognition
    • Chatbots
    • Deepfakes
    • Targeted ads

    How Google uses AI

    • DeepText
    • Gmail (smart replies, spam detection)
    • Google Search (suggestions based on past searches)
    • Google maps (ETA based on location, day of the week, trip time)
    • Google Translate
    • Speech Recognition

    Companies using data science

    • Facebook
    • Amazon
    • Google
    • LinkedIn
    • Netflix
    • YouTube
    • Microsoft

    Roles in Data Science

    • Data Scientist: Proves of disproves hypothesis. Gathering and wrangling data, develop and communicate

    • Data Engineer: Builds Data Driven Platforms, Operationalize algorithms, and handle data integration.

    • Data Analyst: Storytelling, build dashboards and other data visualizations. Providing insights through visuals

    • Process Owner Project management to maintain a vision for data that facilitates process

    Learning Data Science with Python (Libraries)

    • NumPy: numerical computing library
    • Scikit-learn: machine learning library
    • Pandas: data manipulation and analysis library

    Learning Data Science with Python (Tools)

    • Matplotlib: plotting library
    • TensorFlow: open-source library for dataflow programming. Used for machine learning, applications, and neural networks.
    • Keras: Neural network library that runs on TensorFlow
    • Jupyter: interactive computing environment
    • Colab: similar to Jupyter for collaborating and sharing

    Data Science - Applications (List)

    • Consumer Identification
    • Product Recommendation
    • Review Analysis
    • Potential Problem Prediction
    • Automated Systems in Manufacturing
    • Maintenance Scheduling
    • Anomaly Detection
    • Fraud Detection
    • Credit Risk Modeling
    • Customer Lifetime Value
    • Medical Image Analysis
    • Drug Discovery
    • Bioinformatics
    • Virtual Assistants
    • Self Driving Cars
    • Enhanced Driving Experience
    • Car Monitoring System
    • Improving Passenger Safety
    • Customer Segmentation
    • Strategic Decision Making
    • Algorithmic Trading
    • Risk Analysis

    Data Science - Additional key definitions

    • Data is a collection of details and facts.
    • Information is derived from processed data.
    • Analytics are methods to get insight from data
    • Data Science is an interdisciplinary field
    • AI is mimicking human behavior
    • Machine Learning enables machines to learn
    • Deep learning uses multiple layer neural networks

    Data Cleaning

    • Incomplete data
    • Noisy data
    • Inconsistent data

    Handling Missing Values

    • Replace with default values
    • Calculate mean/mode, and fill in missing numerical/categorical fields respectively
    • Generate random values from existing data distribution (as needed)

    Handling Outliers

    • Data Values that exceed expected values.
    • Potentially due to erroneous data processing errors
    • Remove or adjust the values
    • Check data for consistency

    Handling Noisy Data

    • Errors in Data collection, entry, transmission or technology limitations.
    • Data validation and correction process is used for fixing errors

    Handling Inconsistent Data

    • Data values that differ from expected values
    • Use data dependencies to detect and rectify inconsistencies

    Data Science Process

    • Understanding the problem
    • Prepare data samples
    • Develop a model
    • Apply model to dataset
    • Deploy, maintain models

    Prior Knowledge

    • Understand the objective of the problem being investigated
    • Understand subject matter
    • Understand the data context

    Data Preparation

    • Data exploration (to understand the data characteristics)
    • Handling data quality (validating or fixing erroneous data)
    • Handling missing data (managing or replacing missing values)
    • Data type conversion (converting between different data types, if necessary)
    • Handling data anomalies (handling outliers or highly correlated attributes)
    • Feature Selection (Selecting relevant/important features from large datasets)
    • Sampling (Representing a sample from a dataset)

    Data Modeling

    • Creating a model that provides representation of data and relationships within context
    • Making predictions based on a trained model

    Data Application

    • Assimilation of model results into the business process
    • Deployment and maintenance of the model

    Data Visualization

    • Visualizing data helps to identify trends & correlations in data
    • Univariate visualization (analyzing one attribute at a time)
    • Multivariate Visualization (analyzing multiple attributes together)
    • Charts can display trends and relationships.
    • Graphs can help in interpreting trends and patterns from the data effectively

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    Related Documents

    Introduction to Data Science PDF

    Description

    Test your knowledge on data validation rules, standardization methods, and techniques for managing missing values in datasets. This quiz covers key concepts and practices essential for ensuring data integrity and quality in data management processes.

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