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?

Increasing global population Signup and view all the answers

What process allows machines to learn with minimal human intervention?

ML Signup and view all the answers

Which tool is identified as a market leader in analytics?

SAS Signup and view all the answers

Which analytic technique is used primarily for classification tasks?

Decision Trees Signup and view all the answers

What does prescriptive analytics provide for decision-making?

Optimal decisions based on predictions Signup and view all the answers

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

Regression Signup and view all the answers

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

Business acumen and analytics knowledge Signup and view all the answers

What does 'Big Data' refer to?

Data storage costs less than decision-making Signup and view all the answers

What feature makes R particularly valuable in statistical computing?

It is highly extensible and offers a variety of statistical techniques. Signup and view all the answers

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

It allows scalability from a single server to a vast cluster. Signup and view all the answers

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

It focuses on creating interactive graphs and dashboards. Signup and view all the answers

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

Python Signup and view all the answers

What type of applications can R effectively support?

Statistical analysis and graphical representation. Signup and view all the answers

Which statement best describes the capabilities of SAS?

It offers an integrated platform for various business solutions. Signup and view all the answers

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

It supports a wide range of applications including data science and AI. Signup and view all the answers

What distinguishes the analytics solutions offered by SAS?

They are unmatched and tailored for specific industries. Signup and view all the answers

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

R Signup and view all the answers

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

The speed at which data is generated and processed. Signup and view all the answers

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

Data obsolescence Signup and view all the answers

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

Digital images and audio clips. Signup and view all the answers

How does data variability affect data management strategies?

It complicates the ability to use historical data effectively. Signup and view all the answers

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

Using IoT devices to track customer behaviors. Signup and view all the answers

What challenge does data complexity introduce in data analytics?

The difficulty in merging and cleaning data from diverse sources. Signup and view all the answers

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

The methods applied to leverage the data. Signup and view all the answers

Which scenario exemplifies data velocity in a business context?

Instantaneous transaction processing in e-commerce. Signup and view all the answers

What must organizations consider in relation to data variability?

Data values and formats can change over time. Signup and view all the answers

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

Potential challenges with processing and analyzing the data. Signup and view all the answers

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

Studying That Suits You

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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.