Podcast
Questions and Answers
What percentage of attendance is required to sit for the final exam?
What percentage of attendance is required to sit for the final exam?
What is the weight of the final exam in the overall course assessment?
What is the weight of the final exam in the overall course assessment?
Which of the following statements about Big Data is true?
Which of the following statements about Big Data is true?
What is the penalty for cheating and plagiarism in this course?
What is the penalty for cheating and plagiarism in this course?
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How should a student communicate if unable to meet a deadline?
How should a student communicate if unable to meet a deadline?
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What is the main consequence of the data deluge mentioned?
What is the main consequence of the data deluge mentioned?
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How many members are typically allowed in each project group?
How many members are typically allowed in each project group?
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What is the commencement point for a student's grade in the class?
What is the commencement point for a student's grade in the class?
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What term describes the situation when data exceeds an organization's storage or computation capacity?
What term describes the situation when data exceeds an organization's storage or computation capacity?
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Which of the following factors does NOT relate to big data?
Which of the following factors does NOT relate to big data?
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What does data velocity primarily refer to?
What does data velocity primarily refer to?
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Which of the following best describes data complexity?
Which of the following best describes data complexity?
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Data variability refers to which aspect of data management?
Data variability refers to which aspect of data management?
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How does the variety of data impact its analysis?
How does the variety of data impact its analysis?
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What is the primary focus of big data analytics?
What is the primary focus of big data analytics?
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What is the primary function of SAS software in the business intelligence market?
What is the primary function of SAS software in the business intelligence market?
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Which of the following statements accurately describes R?
Which of the following statements accurately describes R?
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What characterizes Hadoop in the big data ecosystem?
What characterizes Hadoop in the big data ecosystem?
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In which area is Python frequently utilized?
In which area is Python frequently utilized?
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What is a primary feature of Tableau?
What is a primary feature of Tableau?
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Which statement is true about SAS's industry focus?
Which statement is true about SAS's industry focus?
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How is R best described in terms of its extensibility?
How is R best described in terms of its extensibility?
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Which of the following is NOT a common application for Python?
Which of the following is NOT a common application for Python?
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What is the primary purpose of analytics?
What is the primary purpose of analytics?
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Which model helps in predicting future outcomes based on historical data?
Which model helps in predicting future outcomes based on historical data?
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What is the role of machine learning in data science?
What is the role of machine learning in data science?
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Which analytic method helps in understanding the relationships among variables?
Which analytic method helps in understanding the relationships among variables?
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Which of the following is NOT a factor driving the demand for big data solutions?
Which of the following is NOT a factor driving the demand for big data solutions?
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What type of model is used to recommend optimal decisions based on data analysis?
What type of model is used to recommend optimal decisions based on data analysis?
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What does data mining primarily focus on?
What does data mining primarily focus on?
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Which of the following tools is recognized as a market leader in analytics?
Which of the following tools is recognized as a market leader in analytics?
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What is one capability of deep learning in artificial intelligence?
What is one capability of deep learning in artificial intelligence?
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What defines prescriptive analytics?
What defines prescriptive analytics?
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Which method is used for predicting numerical outcomes?
Which method is used for predicting numerical outcomes?
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What is a key characteristic of big data tools?
What is a key characteristic of big data tools?
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What is the fundamental difference between diagnostic and prescriptive models?
What is the fundamental difference between diagnostic and prescriptive models?
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Which factor contributes to increasing data velocity?
Which factor contributes to increasing data velocity?
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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.
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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.