Introduction to Big Data Overview
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Questions and Answers

What is the main purpose of analytics?

  • To collect large amounts of data
  • To transform data into actionable insights (correct)
  • To replace human decision-making
  • To automate data processing

Which analytic method is used to predict future outcomes based on historical data?

  • Descriptive model
  • Prescriptive model
  • Diagnostic model
  • Predictive model (correct)

What type of analytics helps understand what has happened in the past?

  • Diagnostic analytics
  • Descriptive analytics (correct)
  • Predictive analytics
  • Prescriptive analytics

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

<p>Increasing global population (C)</p> Signup and view all the answers

What process allows machines to learn with minimal human intervention?

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

Which tool is identified as a market leader in analytics?

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

Which analytic technique is used primarily for classification tasks?

<p>Decision Trees (A)</p> Signup and view all the answers

What does prescriptive analytics provide for decision-making?

<p>Optimal decisions based on predictions (C)</p> Signup and view all the answers

Which of the following is not a method used in descriptive analytics?

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

Which component is essential for a data science team to function effectively?

<p>Business acumen and analytics knowledge (A)</p> Signup and view all the answers

What does 'Big Data' refer to?

<p>Data storage costs less than decision-making (C)</p> Signup and view all the answers

What feature makes R particularly valuable in statistical computing?

<p>It is highly extensible and offers a variety of statistical techniques. (D)</p> Signup and view all the answers

Which characteristic is unique to Hadoop compared to traditional database systems?

<p>It allows scalability from a single server to a vast cluster. (D)</p> Signup and view all the answers

What is a primary function of Tableau in the business intelligence sector?

<p>It focuses on creating interactive graphs and dashboards. (A)</p> Signup and view all the answers

Which programming language is considered the most popular for recent developments in data science?

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

What type of applications can R effectively support?

<p>Statistical analysis and graphical representation. (D)</p> Signup and view all the answers

Which statement best describes the capabilities of SAS?

<p>It offers an integrated platform for various business solutions. (C)</p> Signup and view all the answers

What is one of the main advantages of using Python in data applications?

<p>It supports a wide range of applications including data science and AI. (D)</p> Signup and view all the answers

What distinguishes the analytics solutions offered by SAS?

<p>They are unmatched and tailored for specific industries. (B)</p> Signup and view all the answers

Which language and environment is specifically tailored for statistical computing and graphics?

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

What does the term 'data velocity' refer to in the context of big data?

<p>The speed at which data is generated and processed. (A)</p> Signup and view all the answers

Which of the following factors is NOT typically associated with big data?

<p>Data obsolescence (A)</p> Signup and view all the answers

What is an example of 'data variety' in big data?

<p>Digital images and audio clips. (D)</p> Signup and view all the answers

How does data variability affect data management strategies?

<p>It complicates the ability to use historical data effectively. (B)</p> Signup and view all the answers

Which of the following activities is a core example of data volume increases?

<p>Using IoT devices to track customer behaviors. (C)</p> Signup and view all the answers

What challenge does data complexity introduce in data analytics?

<p>The difficulty in merging and cleaning data from diverse sources. (D)</p> Signup and view all the answers

In big data analytics, what is emphasized over the sheer amount of data?

<p>The methods applied to leverage the data. (D)</p> Signup and view all the answers

Which scenario exemplifies data velocity in a business context?

<p>Instantaneous transaction processing in e-commerce. (A)</p> Signup and view all the answers

What must organizations consider in relation to data variability?

<p>Data values and formats can change over time. (A)</p> Signup and view all the answers

Which of the following is an implication of increased data volume?

<p>Potential challenges with processing and analyzing the data. (B)</p> Signup and view all the answers

Flashcards

Class Rule: Noises

Making excessive noise, like chatting or singing, is prohibited.

Course Assessment: Final Exam

The final exam contributes 50% to the overall grade.

Course Assessment: Assignment

An individual assignment worth 20% of the final grade.

Course Assessment: Project

A group project with a report and presentation, worth 30% of the final grade.

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Academic Integrity

Plagiarism or cheating will result in zero marks.

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Data Deluge

An abundance of data, often overwhelming in volume and complexity.

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Data-Driven Solutions

Every problem can be solved with data, making analytics crucial for businesses and individuals.

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Big Data Definition

The cost of storing data is lower than the cost of decision-making without data.

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Big Data Analytics

Analyze large datasets to extract meaningful insights.

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Importance of Analytics

The ability to analyze data is becoming increasingly important for everyone.

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Big Data Threshold

The point at which the volume, velocity, and variety of data exceed an organization's ability to store or process it efficiently for timely and accurate decision-making.

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Data Volume

The sheer volume of data generated and collected. This is constantly increasing due to sources like social media, machine-to-machine communication, and automated tracking.

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Data Velocity

The speed at which data is generated and needs to be processed. This is influenced by automated processes, social media activity, and real-time applications.

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Data Variety

The different types of data encountered, including structured data from databases and unstructured data like text, images, and videos.

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Data Variability

The variability of data over time and across different sources. This includes factors like seasonality, data quality changes, and different storage formats.

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Data Complexity

The complexity of data arising from its diverse sources, formats, and structures. This makes merging, cleansing, and transforming data challenging.

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What is Analytics?

The key to utilizing big data isn't just having a lot of it, but understanding and extracting meaningful insights from it to make informed decisions.

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Analytics

The scientific process of transforming data into insights to make better decisions and gain a competitive edge.

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Descriptive Model

A type of analysis that helps you understand what happened, like identifying patterns and relationships in data.

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Predictive Model

A model that uses past data to predict future outcomes, like identifying trends or predicting customer behavior.

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Prescriptive Model

A model that goes beyond predicting outcomes and suggests the best actions to take based on predicted future scenarios.

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Data Mining

A broad field that involves the use of data, algorithms, and machine learning to extract knowledge from large datasets.

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Machine Learning

The ability of machines to learn from data and improve their performance over time without explicit programming.

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Artificial Intelligence (AI)

The branch of computer science that deals with creating intelligent machines that can perform tasks that typically require human intelligence.

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Deep Learning

A subfield of AI that uses complex algorithms to learn from large datasets and make predictions or decisions.

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Big Data Explosion

The increasing volume, velocity, and variety of data being generated and collected.

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Factors Driving Big Data Solutions

Factors driving the demand for big data solutions, such as increasing data growth rates, demand for mobile business intelligence, and real-time reporting.

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SAS

A leading software suite providing comprehensive tools for business intelligence, analytics, and reporting across various industries.

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R

A programming language widely used for statistical computing, data visualization, and machine learning.

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Hadoop

A powerful open-source framework designed for distributed storage and processing of large datasets.

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Python

A versatile general-purpose programming language popular for various applications including web development, data science, and machine learning.

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Tableau

A data visualization tool that allows users to create interactive dashboards and charts for business insights.

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Study Notes

Class Rules

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

Course Assessment

  • Final exam: 50%
  • Assignments: 20% (individual)
  • Project: 30% (2-3 members, report & presentation)
  • Cheating and plagiarism will result in zero marks

A Few Suggestions

  • Final grade is based on points, not accumulation of grades
  • Start with zero points, earn points during the course
  • Communicate with instructor for issues or problems
  • Email before deadlines for missed quizzes or assignments

What is Big Data?

  • Data volume, velocity, and variety exceed an organization's storage or computation capacity

Data Deluge

  • Data sources include data from hospitals' patient registries, electronics, POS, stock trades, phone calls, website hits, bank transactions, product catalog orders, remote sensing, airline reservations, web comments, tax returns, credit card charges, and sensor data.

Consequences of the Data Deluge

  • Every problem generates data
  • Every company needs analytics eventually
  • Everyone needs analytics eventually

Big Data: What is it?

  • The point at which the volume, velocity, and variety of data exceed an organization's storage or computation capacity needed for accurate & timely decision-making

Factors associated with big data

  • Volume
  • Velocity
  • Variety
  • Variability
  • Complexity

Data Volume

  • Increasing due to social media use (Facebook, Twitter, Instagram), machines communicating with other machines, improvements in manufacturing processes (quality control), automated devices, & streaming data feeds

Data Velocity

  • Business processes are more automated
  • Mergers and acquisitions
  • Increased social media use
  • Use of self-service applications
  • Integration of business applications

Data Variety

  • Structured data, unstructured data, unstructured text documents (articles, blogs, etc.), emails, digital images, video/audio clips, streaming data, stock ticker data, RFID tag data, & sensor data

Data Variability

  • Data changes over time (seasonality, peak response, social media trends)
  • Data values change over time, and vary across data sources and formats

Data Complexity

  • Data comes from various systems with different formats, making it difficult to merge, cleanse, & uniformly transform data

What is Analytics?

  • The importance of big data isn't the amount, but what's done with it
  • Analytics is the scientific process of transforming data into insights for better decision-making & a competitive advantage

Levels of Analytics

  • Descriptive: Understanding what happened
  • Diagnostic: Understanding why something happened
  • Predictive: What is likely to happen next?
  • Prescriptive: How can we improve future outcomes, what actions can we take?

Analytic Methods

  • Descriptive: Understanding what happened
  • Predictive: Identifying future outcomes
  • Prescriptive: Optimal decisions, future scenarios

Glossary of Terms

  • Includes terms like Statistics, Data Mining, Machine Learning, Artificial Intelligence, Natural Language Processing, Computer Vision, Deep Learning, Predictive Analysis, Prescriptive Analysis, & Optimization

Reasons for the Big Data Explosion

  • Increasing data velocity due to streaming data, POS systems, RFID tags, smart metering, improved business processes, mergers/acquisitions, & more online self-service applications

Factors Driving Demand for Big Data Solutions

  • Data availability from social media
  • Demand for mobile business intelligence
  • Real-time reporting requirements
  • Social media sentiment analysis

Data Science

  • Data Scientist roles, depth in one or two areas
  • Includes Data Systems, Business Intelligence, Machine Learning, Data Science, Business Acumen, Math, or Statistics
  • Data Science Teams cover all areas in depth

Big Data Tools

  • Hadoop, Apache Storm, Apache Spark, Hive, Tableau, R, Python, and SAS

R

  • Programming language & environment for statistical computing & graphics
  • Highly extensible with a wide variety of statistical and graphical techniques

Hadoop

  • Popular big data ecosystem design for highly scalable computations from single server to large clusters

Python

  • Interpreted, high-level, general-purpose programming language
  • Widely used for web development, game development, machine learning/AI, data science, data visualization, web scraping, business applications, and more

Tableau

  • Data visualization tool for business intelligence, creating interactive graphs & charts in dashboards & worksheets for insights

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Description

This quiz covers the foundational rules and assessment criteria for the Big Data course. Understand the importance of attendance and academic integrity while exploring the concept of data deluge and its implications. Ideal for students looking to grasp the course essentials and the scope of big data.

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