Data Science in E-commerce Overview
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Questions and Answers

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 (B)

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

A model is formed before an algorithm is established.

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

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

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

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

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

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

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

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

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

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

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

The term 'Cloud' refers solely to private networks.

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

A Hybrid Cloud consists only of public cloud resources.

<p>False (B)</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. (D)</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 (B)</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

Flashcards

What is data science?

Data science is the process of applying computational and inferential thinking to data to understand the world and solve problems.

Data Engineer Role

Data engineers design and maintain data pipelines, ensuring smooth data flow for analyzing and extracting value from data.

Data Scientist Role

Data Scientists analyze data for insights and predictions, building machine learning models based on patterns in data.

Statistician Role

Statisticians focus on refining statistical models to improve accuracy and fit the data.

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Business Analyst Role

Business analysts translate business needs into data-driven solutions, ensuring insights align with business objectives.

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What is a data pipeline?

Data engineers ensure the smooth flow of data through the pipeline, enabling analysis and value extraction.

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What is ETL in data science?

ETL processes involve extracting data from sources, transforming it into a useful format, and loading it into the pipeline for analysis.

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How do data scientists use machine learning?

Data scientists use machine learning algorithms to create models that can predict or classify data based on patterns in past data.

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Well-defined question in Machine Learning

A problem or question that can be addressed through data analysis and prediction.

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Example data in Fraud Detection

A collection of examples labeled as 'fraudulent' or 'valid' that are used to train a Machine Learning model.

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New data in Machine Learning

New data that is used to test the performance of a Machine Learning model.

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Algorithm

A set of step-by-step instructions that a computer follows to solve a problem or perform a calculation.

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Internet of Things (IoT) Gadgets

Devices that are not standard computers but can collect and transmit data.

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Model

A well-defined computation formed as a result of an algorithm. It takes input values and generates output values.

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Machine Learning

A type of Machine Learning that involves training algorithms on data to identify patterns and make predictions.

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Data Model

A process of organizing and standardizing data elements, defining their relationships with each other.

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Fraud Detection using Machine Learning

Using Machine Learning to analyze data and predict the likelihood of fraudulent transactions.

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Adaptive Learning

The ability of a Machine Learning model to adjust its predictions based on new data.

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Machine Learning Model

The core of a machine learning model, combining data and a prediction algorithm.

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Data Collection

The initial step in data science, involving gathering relevant data from various sources.

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Smart Watches and Machine Learning

Devices that can monitor and analyze physical activity, generating data that can be used for Machine Learning.

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Experimentation and Prediction

The stage in data science where experiments are conducted and models are used to make predictions.

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Exploration and Visualization

The analytical phase in data science where collected data is explored, visualized, and analyzed to uncover insights and patterns.

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Machine Learning (ML)

A field of computer science that involves the development of algorithms and models that enable computers to learn from data and make predictions.

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Generative AI

A type of AI that creates new content, such as images, videos, text, and music.

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Generative Model

A machine learning model that uses a large amount of data to learn patterns and then generate new data that resembles the original.

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Data Augmentation

Generating synthetic data that looks like real data from a specific dataset.

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Missing Data Imputation

Using generative models to fill in missing data points based on patterns learned from existing data.

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Anomaly Detection

Identifying unusual or unexpected events in a dataset using generative models.

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Recommendation Systems

Using generative models to predict what a user might want to see or buy based on past behavior.

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Deep Learning

Deep learning involves training artificial neural networks with many layers to learn complex patterns from large datasets.

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Arthur C. Clarke's quote on technology

Any sufficiently advanced technology is indistinguishable from magic. It means technology can seem like magic when it's very complex.

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What is Community Cloud?

The Community Cloud enables a group of organizations to share and access systems and services.

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What is Hybrid Cloud?

The Hybrid Cloud combines public and private clouds, using public cloud for non-critical activities and private cloud for critical tasks.

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What is IaaS?

IaaS provides access to fundamental resources like physical machines, virtual machines, and storage on demand.

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What is PaaS?

PaaS offers a platform for developing and deploying applications, including tools and runtime environments.

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What is SaaS?

SaaS delivers software applications as a service to end users, allowing them to access and use the software remotely.

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What are Cloud Computing Service Models?

The service models, like IaaS, PaaS, and SaaS, are the foundation of Cloud Computing.

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Who are some key IaaS providers?

AWS, Azure, GCP, IBM Cloud, and DigitalOcean are prominent examples of IaaS providers.

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Who are some key PaaS providers?

AWS, Google App Engine, Azure App Service, OpenShift, Salesforce, and IBM Cloud Foundry are examples of PaaS providers.

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What is Cloud Computing?

Cloud Computing utilizes the internet to provide on-demand access to computing resources like software, data storage, and infrastructure.

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What are Cloud Deployment Models?

The cloud can be accessed in various ways: public, private, hybrid, and community clouds—each with different levels of accessibility and security.

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What is a Public Cloud?

Public clouds are accessible to anyone and are often used for services like emails, with the trade-off of potential security concerns.

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What is a Private Cloud?

Private clouds are designed for specific organizations and are often seen as more secure since access is restricted.

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How can Generative AI be used in Exploratory Data Analysis?

Generative AI models can create synthetic data that closely resembles real data, useful for exploring and understanding data patterns.

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What is the purpose of Cloud Computing Models?

Cloud Computing models provide access to applications and services via the Internet, offering flexibility and scalability.

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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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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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