Introduction to Big Data Course Guidelines
37 Questions
0 Views

Choose a study mode

Play Quiz
Study Flashcards
Spaced Repetition
Chat to lesson

Podcast

Play an AI-generated podcast conversation about this lesson

Questions and Answers

What percentage of attendance is required to sit for the final exam?

  • 85%
  • 75%
  • 80% (correct)
  • 70%
  • What is the weight of the final exam in the overall course assessment?

  • 40%
  • 50% (correct)
  • 60%
  • 70%
  • Which of the following statements about Big Data is true?

  • Big Data is irrelevant to small businesses.
  • Big Data emerged when storage costs exceeded decision costs. (correct)
  • Big Data is manageable without any analytics.
  • Big Data cannot be analyzed without high-level technical skills.
  • What is the penalty for cheating and plagiarism in this course?

    <p>Zero marks for the affected assignment or exam.</p> Signup and view all the answers

    How should a student communicate if unable to meet a deadline?

    <p>Email the instructor BEFORE the deadline.</p> Signup and view all the answers

    What is the main consequence of the data deluge mentioned?

    <p>Every business eventually requires analytics.</p> Signup and view all the answers

    How many members are typically allowed in each project group?

    <p>2-3 members</p> Signup and view all the answers

    What is the commencement point for a student's grade in the class?

    <p>Zero points</p> Signup and view all the answers

    What term describes the situation when data exceeds an organization's storage or computation capacity?

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

    Which of the following factors does NOT relate to big data?

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

    What does data velocity primarily refer to?

    <p>The speed at which data is created and processed</p> Signup and view all the answers

    Which of the following best describes data complexity?

    <p>Challenges in merging data from various systems</p> Signup and view all the answers

    Data variability refers to which aspect of data management?

    <p>Changes in data flow and quality over time</p> Signup and view all the answers

    How does the variety of data impact its analysis?

    <p>Increases the potential for hidden insights</p> Signup and view all the answers

    What is the primary focus of big data analytics?

    <p>Utilizing data effectively for decision making</p> Signup and view all the answers

    What is the primary function of SAS software in the business intelligence market?

    <p>Providing integrated solutions for information management and analytics</p> Signup and view all the answers

    Which of the following statements accurately describes R?

    <p>R provides a wide variety of statistical and graphical techniques.</p> Signup and view all the answers

    What characterizes Hadoop in the big data ecosystem?

    <p>It allows computation ranging from a single server to a cluster of thousands of machines.</p> Signup and view all the answers

    In which area is Python frequently utilized?

    <p>Machine learning and artificial intelligence</p> Signup and view all the answers

    What is a primary feature of Tableau?

    <p>Visualizing data through interactive graphs and dashboards</p> Signup and view all the answers

    Which statement is true about SAS's industry focus?

    <p>SAS provides unmatched domain-specific industry-focused analytics solutions.</p> Signup and view all the answers

    How is R best described in terms of its extensibility?

    <p>R is highly extensible and adaptable.</p> Signup and view all the answers

    Which of the following is NOT a common application for Python?

    <p>Cloud computing management</p> Signup and view all the answers

    What is the primary purpose of analytics?

    <p>To transform data into insights for better decisions</p> Signup and view all the answers

    Which model helps in predicting future outcomes based on historical data?

    <p>Predictive model</p> Signup and view all the answers

    What is the role of machine learning in data science?

    <p>To enable machines to learn with minimal human intervention</p> Signup and view all the answers

    Which analytic method helps in understanding the relationships among variables?

    <p>Descriptive model</p> Signup and view all the answers

    Which of the following is NOT a factor driving the demand for big data solutions?

    <p>Access to static data only</p> Signup and view all the answers

    What type of model is used to recommend optimal decisions based on data analysis?

    <p>Prescriptive model</p> Signup and view all the answers

    What does data mining primarily focus on?

    <p>Finding meaningful patterns in data</p> Signup and view all the answers

    Which of the following tools is recognized as a market leader in analytics?

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

    What is one capability of deep learning in artificial intelligence?

    <p>Performing human-like tasks</p> Signup and view all the answers

    What defines prescriptive analytics?

    <p>It connects findings with actionable insights</p> Signup and view all the answers

    Which method is used for predicting numerical outcomes?

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

    What is a key characteristic of big data tools?

    <p>They enable processing of large datasets quickly</p> Signup and view all the answers

    What is the fundamental difference between diagnostic and prescriptive models?

    <p>Prescriptive models suggest actionable outcomes based on data</p> Signup and view all the answers

    Which factor contributes to increasing data velocity?

    <p>Increased use of social media</p> Signup and view all the answers

    Study Notes

    Class Rules

    • Students can do anything except make noises (chatting, singing).
    • Students can feel free to interrupt with questions.
    • Attendance is required, according to university policy.
    • 80% attendance is necessary to sit the final exam.

    Course Assessment

    • Final exam: 50%
    • Assignments: 20% (individual)
    • Project: 30% (2-3 person groups, requiring reports and presentations)
    • Cheating and plagiarism will result in no marks.
    • Course grade is based on points earned, not an accumulation of grades.
    • Students should communicate with instructor about issues or problems.
    • Students should email instructor if they cannot meet deadlines.

    What is Big Data?

    • Big data is when the volume, velocity, and variety of data exceed an organization's storage or computation capacity for accurate, timely decision-making.
    • Sources of Big Data include hospital patient registries, electronic point-of-sale data, telephone calls, website hits, bank transactions, catalog orders, remote sensing images, airline reservations, web comments, tax returns, credit card charges, and sensor data.

    Consequences of the Data Deluge

    • Every problem, eventually, generates data.
    • Every company and individual eventually needs analytics.

    Big Data

    • Big data is when the cost of storing information becomes less than the cost of making the decision to throw it away.

    Big Data: What is it?

    • Big data is the point where the volume, velocity, and variety of data exceed an organization's capacity to store and process the data in a timely manner for accurate decision-making.

    Factors associated with big data

    • Data volume
    • Data velocity
    • Data variety
    • Data variability
    • Data complexity

    Data Volume

    • Data volumes are increasing due to social media (Facebook, Twitter, Instagram) usage, machines talking to each other, improvements in manufacturing (quality control), automated tracking devices, and streaming data feeds.

    Data Velocity

    • Business processes are increasingly automated.
    • Mergers and acquisitions increase data velocity.
    • Social media usage increases data velocity.
    • Integration of self-service applications increases data velocity.

    Data Variety

    • Structured data, unstructured data, business applications, unstructured text documents (articles, blogs), emails, digital images, videos, audio clips, streaming data, stock ticker data, RFID tag data, and sensor data are all data sources.

    Data Variability

    • The flow of data changes over time (e.g., seasonality, peak response, social media trends).
    • Data values change over time.
    • Data values differ across data sources.
    • Data is stored in different formats.
    • Data standards change across time.

    Data Complexity

    • Data comes from a variety of systems and formats, making it difficult to merge, clean, and transform data uniformly.

    What is Analytics?

    • The importance of big data isn't the volume of data but how it is used.
    • Analytics is the scientific process of transforming data into insight to create better decisions, and opportunities for a competitive advantage.

    Levels of Analytics

    • Different levels of analytics, from descriptive to predictive to prescriptive.
    • Data science experience, advanced analytics, and software engineering support end-to-end analysis of large and diverse data sets.
    • Communication with stakeholders is key.

    Analytic Methods

    • Descriptive models help understand what happened.
    • Predictive models predict future outcomes based on historical data.
    • Prescriptive models suggest optimal decisions based on predictions.

    Glossary of Terms

    • Various data-related terms like Statistics, Data Mining, Machine Learning, Artificial Intelligence, Natural Language Processing, Computer Vision, Deep Learning.

    Reasons for the Big Data Explosion

    • Increasing data velocity due to streaming data feeds, point-of-sale systems, RFID tags, smart metering, increases in cheap data storage, social media, automated business processes, mergers, and online self-service applications.

    Factors Driving Demand for Big Data Solutions

    • Increasing data growth rates.
    • Availability of data from social media.
    • Demand for mobile business intelligence.
    • Increased need for real-time reporting.
    • Desire to analyze social media sentiment.

    Data Science

    • Data systems, business intelligence, machine learning, business acumen, math, or statistics are all part of data science.
    • A data scientist is deep in one or two areas.

    Big Data Tools

    • Hadoop, Storm, Spark, Hive, Tableau, R, Python, and SAS are example tools.

    R

    • R is a language and environment for statistical computing and graphics.

    Hadoop

    • Hadoop is a popular big data ecosystem designed for highly scalable computations, from a single server to a cluster of thousands of machines.

    Python

    • Python is a versatile, high-level programming language used in various fields like web development, game development, machine learning, data science, data visualization, web scraping, and more.

    Tableau

    • Tableau is a data visualization tool for business intelligence allowing creation of interactive graphs, charts, dashboards, and worksheets to gain insights.

    Studying That Suits You

    Use AI to generate personalized quizzes and flashcards to suit your learning preferences.

    Quiz Team

    Related Documents

    Description

    This quiz covers the essential rules and assessment criteria for a Big Data course. Students will familiarize themselves with class rules, attendance policy, project requirements, and the definition of Big Data. Ensure you understand these elements to succeed in the course.

    More Like This

    Data Science and Big Data Analytics Course
    10 questions
    Introduction to Big Data Overview
    30 questions
    Big Data Overview and Class Rules
    38 questions

    Big Data Overview and Class Rules

    ExceptionalConnemara8104 avatar
    ExceptionalConnemara8104
    Use Quizgecko on...
    Browser
    Browser