Data Science in E-commerce Overview
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What is the primary focus of Data Engineers in an e-commerce data pipeline?

  • Designing and maintaining data storage (correct)
  • Building machine learning models
  • Identifying key business questions
  • Analyzing user behavior for patterns

    • Data Scientists primarily focus on bridging business needs with data-driven solutions.

    False

    What is the outcome of Data Scientists' work in an e-commerce context?

    Actionable insights and automated predictions

    Data science involves applying data-centric ______ and inferential thinking.

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

    Which activity is part of the process for Statisticians?

    <p>Refining a statistical model</p> Signup and view all the answers

    Match the roles with their primary focus:

    <p>Data Engineers = Ensuring smooth data flow Data Scientists = Implementing machine learning models Statisticians = Improving statistical models Business Analysts = Bridging business needs with data</p> Signup and view all the answers

    The outcome for Business Analysts is to create machine learning models.

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

    What is a real-life example of data science mentioned in the content?

    <p>E-Commerce Data Pipeline</p> Signup and view all the answers

    What does an algorithm represent?

    <p>A set of commands to complete a task</p> Signup and view all the answers

    A model is formed before an algorithm is established.

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

    What does the term 'Machine Learning Model' refer to?

    <p>Model Data + Prediction Algorithm</p> Signup and view all the answers

    An algorithm can be compared to a ______ for baking a cake.

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

    In the context of a GPS system, what is the model?

    <p>The journey represented on the map</p> Signup and view all the answers

    Match the following components of the Data Science Workflow:

    <p>Data Collection = Gathering information Experimentation and Prediction = Testing hypotheses Exploration and Visualization = Analyzing data Data Analysis = Making sense of collected data</p> Signup and view all the answers

    Which of the following is NOT an application of Data Science?

    <p>Social media browsing</p> Signup and view all the answers

    List one method for data collection within the Data Science Workflow.

    <p>Customer surveys, web traffic results, or financial transactions</p> Signup and view all the answers

    What are the two states that the algorithm can recognize from accelerometer data?

    <p>Running and walking</p> Signup and view all the answers

    Generative AI is primarily used to recognize existing patterns rather than generate new content.

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

    Name one application of Generative AI.

    <p>Image generation</p> Signup and view all the answers

    Generative AI is specifically designed to generate new content as its primary __________.

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

    Match the following Generative AI applications with their descriptions:

    <p>Image generation = Creating new images from a model Video synthesis = Creating new video content Language generation = Producing written text based on input Music composition = Composing new melodies or songs</p> Signup and view all the answers

    What is the primary goal of the fraud detection algorithm at XYZ Bank?

    <p>To assess the probability of a transaction being fraudulent</p> Signup and view all the answers

    Which of the following is NOT a benefit of Generative AI?

    <p>Require no data for training</p> Signup and view all the answers

    Deep learning requires significantly less training data compared to traditional algorithms.

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

    The algorithm used by XYZ Bank relies on a dataset that includes only valid transactions.

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

    What is one example of a technology that uses Generative AI?

    <p>Self-driving cars</p> Signup and view all the answers

    What technology underpins smart watches' ability to detect physical activities?

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

    The algorithm at XYZ Bank refines its predictive capabilities with each new __________.

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

    Match the following IoT gadgets with their functionalities:

    <p>Smart watches = Detect physical activities Internet-connected home security systems = Enhance home security Electronic toll collection systems = Automatic toll payments Building energy management systems = Optimize energy usage</p> Signup and view all the answers

    Which of the following defines a well-defined question for machine learning?

    <p>A clear inquiry needing a predictive answer</p> Signup and view all the answers

    The dataset used in training the fraud detection algorithm is created from outdated transaction data only.

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

    IoT devices can transmit data but are not standard __________.

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

    What is the primary purpose of Generative AI models like GANs?

    <p>To generate synthetic data that resembles the original</p> Signup and view all the answers

    Cloud Computing allows users to access applications and data only from local servers.

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

    Name one application that runs in the cloud.

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

    The type of cloud that allows all systems and services to be accessible to the general public is called a __________ cloud.

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

    Match the following types of cloud deployment models with their definitions:

    <p>Public Cloud = Accessible to the general public Private Cloud = Accessible within an organization Hybrid Cloud = Combination of public and private Community Cloud = Shared infrastructure for specific communities</p> Signup and view all the answers

    Which of the following models allows access to cloud services while maintaining some management?

    <p>Private Cloud</p> Signup and view all the answers

    The term 'Cloud' refers solely to private networks.

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

    What refers to manipulating and accessing applications online?

    <p>Cloud Computing</p> Signup and view all the answers

    Which cloud model is specifically designed to provide technology infrastructure on demand?

    <p>Infrastructure as a Service (IaaS)</p> Signup and view all the answers

    A Hybrid Cloud consists only of public cloud resources.

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

    Name one example of a Platform as a Service (PaaS) provider.

    <p>Google App Engine</p> Signup and view all the answers

    The ______ Cloud allows systems and services to be accessible by a group of organizations.

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

    What is a key feature of Software as a Service (SaaS)?

    <p>It allows users to access software applications remotely over the internet.</p> Signup and view all the answers

    Match the following service models with their primary focus:

    <p>IaaS = provides infrastructure resources PaaS = provides runtime environment for applications SaaS = delivers software applications as a service Hybrids Cloud = combines public and private cloud resources</p> Signup and view all the answers

    Infrastructure as a Service typically supports single-tenant environments.

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

    PaaS typically supports a ______ architecture that is highly scalable.

    <p>multi-tier</p> Signup and view all the answers

    Study Notes

    IT in Business - Session 10

    • Course focusing on IT in business
    • Instructor: Hafsa Naeem
    • Session 10 covers Data Science, Applications, and Cloud Computing

    Data Science

    • Data science applies computational and inferential thinking to understand and solve problems.
    • A quote from Joseph Gonzalez, Professor at UC Berkeley, is included, defining data science.

    Real-Life Example: E-Commerce Data Pipeline

    • Role: Data Engineers, Data Scientists, Statisticians, Business Analysts
    • Focus: Ensuring smooth data flow, extracting value from data & implementing models, improving a single model, bridging data and business needs
    • Example Scenario: Designing and maintaining e-commerce data pipelines, analyzing e-commerce data, focusing on refining statistical models, translating business needs into data-driven solutions.
    • Activities and Outcomes: Implementing ETL processes, setting up optimized data storage, building machine learning models, iterative improvement of statistical models, identifying key insights.

    Algorithms & Models

    • An algorithm is a set of commands for a computer to perform calculations or problem-solving operations.
    • A data model organizes data elements and standardizes how they relate.
    • A machine learning model is a combination of data and prediction algorithms.

    Algorithms & Models - Analogies

    • Cake Baking: Algorithm: recipe; Model: finished cake; Relationship: recipe guides the process, cake is the outcome.
    • Building a LEGO Set: Algorithm: instructions; Model: completed structure; Relationship: instructions lead to the creation of the model.
    • GPS Navigation: Algorithm: route calculation; Model: journey; Relationship: algorithm determines route.
    • Cooking a Dish: Algorithm: recipe; Model: prepared dish; Relationship: recipe yields desired dish.

    Data Science Workflow

    • 1. Data Collection: Collect data through customer surveys, web traffic, emails from sales teams & potential clients, and financial transactions.
    • 2. Experimentation and Prediction: Perform experiments and predict potential outcomes.
    • 3. Exploration and Visualization: Explore and visualize the data.

    Applications of Data Science

    • 1. Machine Learning (ML): A well-defined question, a set of example data, and a new set of data for algorithms to use.
    • 2. Internet of Things (IoT): Gadgets that are not computers that can transmit data.
    • 3. Deep Learning: Requires significant training data and used for complex problems, such as image classification, language learning/understanding, and self-driving cars.
    • 4. Generative AI: A technology that is as advanced as magic. Used for creating concise information, automating processes, designing products, and generating different types of content from text to images.

    Internet of Things (IoT)

    • Examples: Smart watches, Internet-connected home security systems, electronic toll collection systems, building energy management systems.

    Smart Watches

    • They detect physical activities like running and walking.
    • An accelerometer monitors motion in three dimensions.
    • Data is used to develop algorithms that can recognize running or walking.

    Cloud Computing

    • Cloud computing is a way to utilize applications as utilities over the Internet.
    • Users can configure and customize applications online, accessing database resources anytime.
    • No need to maintain or manage computing resources.

    What is Cloud?

    • The term Cloud refers to a network or the Internet.
    • Cloud computing provides services over public, private, WAN, LAN, or VPN networks.
    • Applications like email, web conferencing, and CRM run in the cloud.

    What is Cloud Computing?

    • Cloud computing allows online data storage, application infrastructure, and software delivery via a network.
    • Software and hardware resources are delivered as a network service.

    Cloud Computing Models

    • Deployment Models: Public, Private, Hybrid, Community
    • Service Models: Infrastructure as a Service (IaaS), Platform as a Service (PaaS), Software as a Service (SaaS)

    Infrastructure as a Service (IaaS)

    • Provides delivery of technology infrastructure as a scalable service.
    • Access to fundamental resources such as physical and virtual machines.
    • Usually billed based on usage.

    Infrastructure as a Service (IaaS) - Examples

    • Amazon Web Services (AWS)
    • Microsoft Azure
    • Google Cloud Platform (GCP)
    • IBM Cloud Infrastructure
    • DigitalOcean
    • OpenStack

    Platform as a Service (PaaS)

    • Provides a runtime environment for applications.
    • Includes development and deployment tools.
    • Supports the lifecycle of building and delivering web applications.

    Platform as a Service (PaaS) - Examples

    • Amazon Web Services (AWS)
    • Google App Engine
    • Microsoft Azure App Service
    • Red Hat OpenShift
    • IBM Cloud Foundry
    • Salesforce Lightning Platform

    Software as a Service (SaaS)

    • Allows use of applications as a service to end users.
    • Provides licensed multi-tenant access. Delivered remotely via web.
    • Usually bills users based on usage.

    Software as a Service (SaaS) - Examples

    • Salesforce
    • Google Workspace
    • Microsoft 365
    • Dropbox
    • Slack
    • Zoom
    • Shopify
    • Netflix
    • Adobe Creative Cloud
    • Salesforce Marketing Cloud

    Cloud Storage

    • Create an account.
    • Upload files/content.
    • Log in over WiFi to access content.
    • Content stays on the cloud.

    Advantages & Disadvantages of Cloud Storage

    • Advantages: Lower computer costs, improved performance, reduced software costs, instant software updates, improved document format compatibility, unlimited storage, increased data reliability, universal document access, easier group collaboration, device independence.
    • Disadvantages: Requires constant internet connection, doesn't work well with low-speed connections, features might be limited, can be slow, stored data can be lost, stored data might not be secure.

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    IT in Business Data Science Applications PDF

    Description

    This quiz explores the roles and responsibilities of data professionals in the e-commerce data pipeline, including Data Engineers, Data Scientists, Statisticians, and Business Analysts. Learn about the outcomes of their work and the core concepts of data science, machine learning, and algorithms. Test your knowledge on various aspects of the data science workflow and its applications.

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