Podcast
Questions and Answers
What has contributed significantly to the enormous growth of data in both commercial and scientific databases?
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?
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?
Which of the following companies is known to handle millions of visits per day as part of e-commerce?
- Amazon (correct)
How have computers influenced the landscape of data science?
How have computers influenced the landscape of data science?
What is a key driver for companies to adopt data science practices?
What is a key driver for companies to adopt data science practices?
What is one of the key roles of data science in the scientific community?
What is one of the key roles of data science in the scientific community?
Which of the following is considered a significant opportunity addressed by data science?
Which of the following is considered a significant opportunity addressed by data science?
Which aspect distinguishes scientists from computer scientists in their approach to data?
Which aspect distinguishes scientists from computer scientists in their approach to data?
What primary skill set is mentioned as part of data science?
What primary skill set is mentioned as part of data science?
Which of the following data types is not typically associated with the collection and storage at enormous speeds?
Which of the following data types is not typically associated with the collection and storage at enormous speeds?
What is one of the misconceptions of computer scientists regarding data?
What is one of the misconceptions of computer scientists regarding data?
What characteristic of the natural world do scientists recognize that contrasts with computer science?
What characteristic of the natural world do scientists recognize that contrasts with computer science?
In what way do scientific simulations generate data?
In what way do scientific simulations generate data?
What is the main goal of market segmentation?
What is the main goal of market segmentation?
Which approach is used in document clustering?
Which approach is used in document clustering?
In the context of deviation detection, which of the following applications could be used?
In the context of deviation detection, which of the following applications could be used?
How is clustering quality measured in market segmentation?
How is clustering quality measured in market segmentation?
What is a potential application of anomaly detection in sensor networks?
What is a potential application of anomaly detection in sensor networks?
What is a key characteristic that distinguishes wisdom from genius in data science?
What is a key characteristic that distinguishes wisdom from genius in data science?
Which statement best describes how a good data scientist demonstrates curiosity?
Which statement best describes how a good data scientist demonstrates curiosity?
What type of questions should data scientists prioritize when analyzing data sets?
What type of questions should data scientists prioritize when analyzing data sets?
What kind of analysis could be conducted using the Baseball-Reference.com dataset?
What kind of analysis could be conducted using the Baseball-Reference.com dataset?
Which question reflects a demographic inquiry using the provided datasets?
Which question reflects a demographic inquiry using the provided datasets?
In the context of Google Ngrams, which question investigates trends in language usage?
In the context of Google Ngrams, which question investigates trends in language usage?
What aspect of baseball data can be analyzed to assess players' skill performance?
What aspect of baseball data can be analyzed to assess players' skill performance?
Which of the following is not typically encouraged for software developers, unlike data scientists?
Which of the following is not typically encouraged for software developers, unlike data scientists?
What is the primary goal of churn prediction for telephone customers?
What is the primary goal of churn prediction for telephone customers?
In the context of detecting fraudulent transactions, what is crucial for building the model?
In the context of detecting fraudulent transactions, what is crucial for building the model?
Which of the following options describes the approach used in the Sky Survey Cataloging?
Which of the following options describes the approach used in the Sky Survey Cataloging?
What does classification in the context of data analytics primarily focus on?
What does classification in the context of data analytics primarily focus on?
What type of information is typically gathered to classify a customer as loyal or disloyal?
What type of information is typically gathered to classify a customer as loyal or disloyal?
What is a significant outcome of the Sky Survey Cataloging project?
What is a significant outcome of the Sky Survey Cataloging project?
What is the purpose of regression in data analytics?
What is the purpose of regression in data analytics?
Which of the following best describes the classification of early-stage formation galaxies?
Which of the following best describes the classification of early-stage formation galaxies?
What is a primary responsibility of data scientists within an organization?
What is a primary responsibility of data scientists within an organization?
Which industry is NOT commonly associated with the work of data scientists?
Which industry is NOT commonly associated with the work of data scientists?
What challenge do data scientists face when dealing with data?
What challenge do data scientists face when dealing with data?
Which of the following is a type of organization where data scientists can work?
Which of the following is a type of organization where data scientists can work?
In what context is a data model used by a data scientist in a hospital setting?
In what context is a data model used by a data scientist in a hospital setting?
Flashcards
Data Science
Data Science
The practice of extracting knowledge and insights from data through various techniques like statistical analysis, machine learning, and data visualization.
Large-scale Data
Large-scale Data
The massive growth in the amount of data collected and stored across businesses and research, driven by advancements in technology.
Gather Whatever Data You Can
Gather Whatever Data You Can
Gathering as much data as possible, regardless of immediate use, with the expectation that it will be valuable in the future.
Competitive Pressure
Competitive Pressure
The growing pressure for businesses to use data effectively to improve customer service, increase efficiency, and gain a competitive advantage.
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Customized Services
Customized Services
The use of data to provide personalized and customized services to customers based on their individual needs and preferences.
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Wisdom in Data Science
Wisdom in Data Science
The ability to make good judgments based on experience, knowledge, and understanding.
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Asking Good Questions
Asking Good Questions
Asking questions that uncover hidden insights and drive meaningful discoveries from data.
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Developing Curiosity
Developing Curiosity
Exploring the context and background knowledge related to the data you are working with.
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Google Ngrams
Google Ngrams
A resource that provides annual time series of the frequency of words/phrases in scanned books.
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Google Ngram Viewer
Google Ngram Viewer
A website that allows you to analyze the frequency of words and phrases over time.
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Baseball-Reference.com
Baseball-Reference.com
A website that provides comprehensive baseball statistics for players, teams, and games.
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Player Skill and Value
Player Skill and Value
Analyzing and interpreting data to understand the value, performance, and trends of individual players.
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Batting Performance and Position
Batting Performance and Position
Examining the relationship between player performance and the position they play.
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What does data science do for scientists?
What does data science do for scientists?
Data science uses algorithms to analyze massive datasets, helping scientists automate tasks and create new hypotheses.
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What are some sources of large-scale data in science?
What are some sources of large-scale data in science?
Data collected from satellites, telescopes, and high-throughput biological experiments is often in Petabytes or Terabytes, requiring specialized data storage and processing.
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How do scientists and computer scientists differ in their approaches to data?
How do scientists and computer scientists differ in their approaches to data?
Unlike computer scientists who focus on creating orderly virtual worlds, scientists deal with the messy and complex real world.
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What are the primary motivations for scientists and computer scientists?
What are the primary motivations for scientists and computer scientists?
Scientists are driven by discovering and understanding real-world phenomena, while computer scientists prioritize inventing and building algorithms.
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How do scientists and computer scientists approach data imperfections?
How do scientists and computer scientists approach data imperfections?
Scientists are comfortable with the idea that data can have errors, while computer scientists often expect perfectly clean and accurate data.
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What are the key elements of data science?
What are the key elements of data science?
Exploratory Data Analysis (EDA) and visualization are crucial for understanding the data, Machine Learning helps find patterns, and High-Performance Computing is needed for dealing with the massive scale of scientific data.
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How can data science contribute to solving societal problems?
How can data science contribute to solving societal problems?
Data science has the potential to solve significant societal issues, such as improving healthcare, predicting climate change, and increasing food production.
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How do real scientists and computer scientists value data differently?
How do real scientists and computer scientists value data differently?
Computer Scientists often treat data as an abstract concept, while real scientists value data as a precious resource that requires effort to acquire and understand.
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Clustering
Clustering
Organizing data points into groups based on similarities, like putting similar customers together for targeted marketing.
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Document Clustering
Document Clustering
Finding groups of documents with similar content by analyzing the terms they use, like grouping articles on the same topic.
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Anomaly Detection
Anomaly Detection
Identifying unusual patterns or behaviors that deviate from the norm, like spotting fraudulent credit card transactions.
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Change Detection
Change Detection
Using data to understand how things change over time, like tracking variations in forest cover.
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Credit Card Fraud Detection
Credit Card Fraud Detection
Analyzing credit card transactions to identify suspicious activity, like unusual spending patterns or transactions from unusual locations.
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Customer Churn Prediction
Customer Churn Prediction
A classification task where the goal is to predict whether a customer is likely to switch to a competitor, based on their usage patterns and other factors.
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Sky Survey Cataloging
Sky Survey Cataloging
A classification task where the goal is to categorize astronomical objects, like stars or galaxies, based on data from telescopic images.
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Continuous Value Prediction
Continuous Value Prediction
A regression task where the goal is to predict a continuous value (like temperature or price) based on other variables and their relationships.
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Model for Class Prediction
Model for Class Prediction
A technique used in classification tasks where the goal is to assign a class label (like 'fraud' or 'loyal') to a data point based on its features and learned patterns.
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Attributes
Attributes
Information used to describe characteristics or qualities of a data point. These features can be numerical or categorical and are often used for training machine learning models.
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Image Segmentation
Image Segmentation
The process of dividing images into individual regions or segments, each representing a distinct object or feature.
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Image Attributes
Image Attributes
Numerical or categorical values extracted from data, often representing a specific aspect of a data point. These measures provide insights into the data's characteristics.
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Data Scalability
Data Scalability
A situation where data is so huge that it becomes challenging to process and analyze efficiently.
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High Dimensionality
High Dimensionality
Data that has a large number of features or variables, making it difficult to analyze, visualize, and model effectively.
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Heterogeneous Data
Heterogeneous Data
Data that comes from multiple sources and formats, making it challenging to integrate and process.
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Data Distribution
Data Distribution
When data is spread across different locations or organizations, making it difficult to access and combine.
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Non-traditional Analysis
Non-traditional Analysis
Data Scientists applying their skills and knowledge to unconventional areas beyond traditional business applications like forecasting and analysis.
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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.
Introduction (CIS continued)
- CIS programs train graduates with a firm grasp of organizational practices and business functions and how technology can improve business operations or workflow.
- CIS graduates often encounter challenges in applying technology, including the difficulty in developing software to meet complex business needs, system design, data management, and adapting to existing business practices.
Example (CIS Program)
- During healthcare electronic health record (EHR) development, CIS graduates are prepared to interact with the functionality of the program and can effectively work within the healthcare information systems.
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