Data Science Fundamentals Quiz

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Questions and Answers

What has contributed significantly to the enormous growth of data in both commercial and scientific databases?

The decline of traditional marketing methods

Advances in data generation and collection technologies (correct)

Decreased costs of data storage solutions

Increased consumer demand for data privacy

What is the primary expectation when data is gathered in the context of data science?

It will have value for some purpose, intended or not (correct)

It will be exclusively useful for business analytics

It will be used solely for its intended purpose

It will replace the need for human input in decision-making

Which of the following companies is known to handle millions of visits per day as part of e-commerce?

Facebook

Amazon (correct)

Twitter

Google

How have computers influenced the landscape of data science?

They have become cheaper and more powerful Signup and view all the answers

What is a key driver for companies to adopt data science practices?

To enhance customer relationships and services Signup and view all the answers

What is one of the key roles of data science in the scientific community?

Automating analysis of massive datasets Signup and view all the answers

Which of the following is considered a significant opportunity addressed by data science?

Finding alternative energy sources Signup and view all the answers

Which aspect distinguishes scientists from computer scientists in their approach to data?

Scientists are data-driven, while computer scientists are algorithm-driven Signup and view all the answers

What primary skill set is mentioned as part of data science?

Exploratory Data Analysis and Visualization Signup and view all the answers

Which of the following data types is not typically associated with the collection and storage at enormous speeds?

Sales transactional data Signup and view all the answers

What is one of the misconceptions of computer scientists regarding data?

Data is merely a resource for testing algorithms Signup and view all the answers

What characteristic of the natural world do scientists recognize that contrasts with computer science?

Data often contains errors and noise Signup and view all the answers

In what way do scientific simulations generate data?

Producing terabytes of data in short periods Signup and view all the answers

What is the main goal of market segmentation?

To distribute a marketing mix across different subsets of customers. Signup and view all the answers

Which approach is used in document clustering?

Identifying similarities through the frequency of important terms. Signup and view all the answers

In the context of deviation detection, which of the following applications could be used?

Credit card fraud detection. Signup and view all the answers

How is clustering quality measured in market segmentation?

By comparing buying patterns within and across clusters. Signup and view all the answers

What is a potential application of anomaly detection in sensor networks?

Monitoring and surveillance for unusual behavior. Signup and view all the answers

What is a key characteristic that distinguishes wisdom from genius in data science?

Wisdom comes from experience and humility. Signup and view all the answers

Which statement best describes how a good data scientist demonstrates curiosity?

They engage in discussions with others in their domain. Signup and view all the answers

What type of questions should data scientists prioritize when analyzing data sets?

Questions that encourage exploration and learning. Signup and view all the answers

What kind of analysis could be conducted using the Baseball-Reference.com dataset?

Determining how player salaries relate to future performance. Signup and view all the answers

Which question reflects a demographic inquiry using the provided datasets?

Do left-handed people have shorter lifespans than right-handers? Signup and view all the answers

In the context of Google Ngrams, which question investigates trends in language usage?

How often do new words emerge and remain in usage? Signup and view all the answers

What aspect of baseball data can be analyzed to assess players' skill performance?

The correlation between batting performance and a player's position. Signup and view all the answers

Which of the following is not typically encouraged for software developers, unlike data scientists?

Focusing solely on code efficiency. Signup and view all the answers

What is the primary goal of churn prediction for telephone customers?

To predict customer loyalty or disloyalty Signup and view all the answers

In the context of detecting fraudulent transactions, what is crucial for building the model?

Labeling transactions as fraud or fair Signup and view all the answers

Which of the following options describes the approach used in the Sky Survey Cataloging?

Segmenting images and measuring 40 attributes per object Signup and view all the answers

What does classification in the context of data analytics primarily focus on?

Grouping data into defined categories based on features Signup and view all the answers

What type of information is typically gathered to classify a customer as loyal or disloyal?

Frequency and timing of their calls Signup and view all the answers

What is a significant outcome of the Sky Survey Cataloging project?

Identification of new high red-shift quasars Signup and view all the answers

What is the purpose of regression in data analytics?

To predict values based on other variable values Signup and view all the answers

Which of the following best describes the classification of early-stage formation galaxies?

Marked by specific attributes like light wave characteristics Signup and view all the answers

What is a primary responsibility of data scientists within an organization?

To build models of verified and validated data sets Signup and view all the answers

Which industry is NOT commonly associated with the work of data scientists?

Fitness Trainers Signup and view all the answers

What challenge do data scientists face when dealing with data?

High Dimensionality in data analysis Signup and view all the answers

Which of the following is a type of organization where data scientists can work?

Governmental agencies Signup and view all the answers

In what context is a data model used by a data scientist in a hospital setting?

To predict the best treatment for a specific patient Signup and view all the answers

Study Notes

Data Science Overview

Data science involves the growth of commercial and scientific databases, driven by advancements in data generation and collection.
A key principle is gathering as much data as possible, whenever and wherever possible.
Expectations are that the gathered data will be valuable either for its initial intended purpose or for unforeseen future applications.

Why Data Science? (Commercial Viewpoint)

Vast amounts of data are being collected and warehoused, including web data (e.g., Google's Peta Bytes).
Companies like Facebook and Amazon handle massive volumes of user interactions and transactions.
Advances in computing power and reduced costs make data processing more accessible.
Competitive pressures encourage the utilization of data to provide better, customized services.

Why Data Science? (Scientific Viewpoint)

Data is collected and stored at enormous speeds, with examples including satellite data (NASA EOSDIS archives) and astronomical data (telescope sky surveys).
High-throughput biological data and scientific simulations generate vast quantities of data.
Data science provides the tools for analyzing massive datasets and forming new hypotheses.

Great Opportunities to Solve Society's Major Problems

Data science can improve healthcare and reduce costs.
Data science can aid in predicting the impacts of climate change.
Data science can assist in finding alternative/green energy sources.
Data science can be used to reduce hunger and poverty by boosting agricultural production.

What is Data Science?

Data science encompasses exploratory data analysis, visualization, machine learning, statistics, and high-performance computing for large-scale data.

Skill Sets for Data Science

Data science demands a blend of computer science, hacking skills, machine learning, mathematical and statistical knowledge, and subject matter expertise.

Appreciating Data

Computer scientists often view data as a neutral input for computational tasks rather than appreciating the inherent value.
Obtaining useful datasets requires more effort than simply using randomly generated data. Data sets represent a valuable but scarce resource requiring ingenuity and hard work.

Computer vs. Real Scientists

Real scientists aim to understand complex natural phenomena, unlike computer scientists, who create organized virtual worlds.
Scientific truths are not always absolute, while computer science often involves binary truths.

Computer vs. Real Scientists (continued)

Scientists are primarily data-driven, while computer scientists are algorithm-driven.
Scientists tend to emphasize the discovery of new knowledge, whereas computer scientists focus on creating new things.
Scientists often work with data containing inherent errors, whereas computer scientists typically idealize the absence of error.

Genius vs. Wisdom

Data scientists aim to develop insights rather than simply produce code, like software developers.
Wisdom, which is crucial for data science, entails recognizing and avoiding incorrect answers, while genius involves finding the right answers.

Developing Wisdom

Wisdom comes from experience, general knowledge, listening to others, and humility (recognizing one's errors).

Developing Curiosity

Data scientists cultivate a deep understanding and curiosity about the field/application.
Scientists talk to domain experts (people whose data they work with to gather information about the subject matter).
To get broader perspectives, they regularly read news media.

Asking Good Questions

Data scientists critically assess data sets by asking pertinent questions, such as potential insights, user needs, and relevant datasets for obtaining those insights.

Let's Practice Asking Questions (Datasets)

Examples of datasets for practice questions are: baseball-reference.com, Google ngrams, and NYC taxi cab records.

Baseball Questions

How can an individual player's skill, value, or performance be appropriately measured?
How can the appropriateness of trades between baseball teams be determined?
What is the typical trajectory of the performance of a baseball player as they age and mature?
Does batting performance correlate with the position played?

Demographic Questions

Do left-handed people have shorter lifespans compared to right-handed people?
How often do people return to their place of birth?
Do player salaries reflect current and future performance?
Are human heights and weights increasing in modern populations?

Google Ngrams

Google Ngrams is a resource providing an annual time series of words/phrases and their frequency in scanned books.
A word/phrase is considered "popular" if it appears more than 40 times.

Ngram Questions

How has the use of curse words changed over time?
What is the typical lifespan of fame and technological innovation?
How frequently do new words emerge, and do they remain commonly used?
How can a language model be constructed?

NYC Taxi Cab Data

Datasets include driver information, pickup/drop-off locations, and fare information for every taxi trip.
Data is obtained from the City of New York via Freedom of Information Act requests.

Taxicab Questions

How much do taxi drivers earn per night?
What distance do taxi drivers travel?
How does traffic impact travel times and fares, especially during rush hour?
Where do people generally travel to and from during different times of the day?
Do faster taxi drivers receive better tips?
Where should taxi drivers go to pick up their next fare?

Machine Learning Tasks

Data tasks include clustering (grouping similar objects), predictive modeling (making predictions based on data), and anomaly detection (identifying unusual patterns).
Data science is useful in diverse areas like analyzing milk or identifying diaper brands.

Predictive Modeling: Classification

Predictive modeling aims to model class attributes as a function of other attributes to predict values.

Examples of Classification Tasks

Classify credit card transactions (legitimate or fraudulent).
Classify land covers (e.g., water, urban).
Categorize news stories (e.g., finance, weather).
Identify intruders in the cyberspace.
Predict benign or malignant tumor cells.
Classify protein secondary structures

Classification: Application 1 (Fraud Detection)

Goal: Predict fraudulent credit card transactions.
Approach: Use credit card transaction data as attributes (e.g., purchase frequency, time of day) and label past transactions as fraudulent or non-fraudulent.

Classification: Application 2 (Churn Prediction)

Goal: Predict customer churn (likelihood of a customer switching to a competitor).
Approach: Analyze customer transaction data to determine relevant attributes such as call frequency, financial status, and loyalty.

Classification: Application 3 (Sky Survey Cataloging)

Goal: Classify sky objects (stars and galaxies).
Approach: Analyze image features from telescopes and assign classes based on gathered image information and characteristics.

Classifying Galaxies

Classify galaxies based on their formation stages using image features and characteristics of their emitted light waves.

Regression

Regression predicts a continuous variable based on other variables using a linear or non-linear model.
Examples include predicting sales amounts, wind velocities, and stock market indices.

Clustering

Clustering groups similar objects, minimizing intra-cluster distances and maximizing inter-cluster distances.

Applications of Cluster Analysis

Understand customer behavior and preferences (customer profiling).
Group related documents (using keywords from documents for clustering).
Group genes and proteins with similar functionality.
Group stocks with similar pricing trends.
Reduce the size of large datasets by summarizing.

Clustering: Application 1 (Market Segmentation)

Goal: Divide a market into subsets of similar customers to tailor marketing strategies.
Approach: Collect customer data (e.g., location, lifestyle) and identify clusters of similar customers. Measure buying patterns to refine cluster analysis.

Clustering: Application 2 (Document Clustering)

Goal: Group similar documents based on their important terms.
Approach: Analyze terms and their frequencies in documents; use similarity measure to form clusters.

Deviation/Anomaly/Change Detection

Recognize and respond to significant deviations from normal behavior.
Examples include credit card fraud detection, network intrusion identification, and detecting changes in global forest cover.

Motivating Challenges in Data Science

Data science faces scalability, high dimensionality, heterogeneous and complex data, ownership/distribution issues, and non-traditional analysis methods.

DS Career path

Graduates can pursue varied careers.
Data scientists can work in diverse fields.

Introduction (Data Science)

Graduates of data science programs typically work as data scientists in various organizations (private, governmental, non-profit).

Industries for Data Scientists

Data scientists can fill various roles across diverse industries, including finance, government, healthcare, online platforms, retail, and agriculture.

Data Scientist Responsibilities

Data scientists usually model validated data.
Models predict, recommend, and assess future business decisions.
Data scientists gather validated data and use historic data on similar instances to predict future ones.
Data scientists often model data to predict future trends and business outcomes.
Data scientists may need to collect, clean, normalize, and communicate the necessary data with relevant parties, often the department, team, or entity that's responsible for collecting the data.
Data compliance may play a critical role in ensuring lawful data usage.

Data Science More Opportunities

Data science graduates can pursue additional specializations as software development engineers and create dashboards and reports from data insights.

CIS Career path

Graduates of computer information systems programs can typically pursue careers in various fields, such as business analysis, software development, and system implementation.
CIS programs may have varied career options or job functions depending on the focus of the program.

Introduction (CIS)

Graduates of computer information systems programs typically work as business analysts, software developers, or system implementers.

Introduction (CIS continued)

CIS is interdisciplinary, incorporating technology and business disciplines.