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Podcast
Podcast
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Questions and Answers
Questions and Answers
Which statement best describes data?
Which statement best describes data?
- Data is an unprocessed collection of observable and quantifiable facts. (correct)
- Data is a singular piece of information representing an opinion.
- Data can only be numerical values and cannot include words or descriptions.
- Data is valuable only when analyzed and interpreted.
What is the primary process involved in datafication?
What is the primary process involved in datafication?
- The conversion of various aspects of life into quantifiable data. (correct)
- Developing software to automate data storage.
- Transforming unstructured data into structured formats.
- Collecting data for storage without any specific purpose.
Data science can be defined as which of the following?
Data science can be defined as which of the following?
- Merely the use of data for statistical analysis.
- Purely a technical field involving programming skills.
- A process focused only on data collection methods.
- The art and science of acquiring knowledge from data. (correct)
Which of the following is NOT a benefit of data science?
Which of the following is NOT a benefit of data science?
How does data science contribute to creating new industries?
How does data science contribute to creating new industries?
What is a common application of data in the banking sector?
What is a common application of data in the banking sector?
Which of the following describes the role of a data scientist?
Which of the following describes the role of a data scientist?
In what way can social platforms like Facebook utilize data?
In what way can social platforms like Facebook utilize data?
What is the primary role of a data engineer within an analytics team?
What is the primary role of a data engineer within an analytics team?
Which of the following is NOT one of the five essential steps for performing data science?
Which of the following is NOT one of the five essential steps for performing data science?
During the exploratory data analysis (EDA) phase, which technique is used for identifying outliers?
During the exploratory data analysis (EDA) phase, which technique is used for identifying outliers?
What is the first step in the data science process?
What is the first step in the data science process?
Which of the following could be considered a source of data when attempting to answer a data science question?
Which of the following could be considered a source of data when attempting to answer a data science question?
What is the purpose of plotting distributions of all variables during EDA?
What is the purpose of plotting distributions of all variables during EDA?
Which action is primarily performed in the 'obtaining the data' step of the data science process?
Which action is primarily performed in the 'obtaining the data' step of the data science process?
In the context of data science, what is the significance of domain knowledge?
In the context of data science, what is the significance of domain knowledge?
What is a primary role of a data scientist?
What is a primary role of a data scientist?
Which of the following skills is essential for a data scientist?
Which of the following skills is essential for a data scientist?
What is one of the three basic areas essential for understanding data science?
What is one of the three basic areas essential for understanding data science?
Why is new vocabulary necessary in the field of data science?
Why is new vocabulary necessary in the field of data science?
What does the term 'domain knowledge' refer to in data science?
What does the term 'domain knowledge' refer to in data science?
What common issue do data scientists often face when handling data?
What common issue do data scientists often face when handling data?
Which area does NOT contribute to a data scientist's expertise?
Which area does NOT contribute to a data scientist's expertise?
What does a data scientist primarily use computer programming for?
What does a data scientist primarily use computer programming for?
What is the primary goal of exploratory data analysis (EDA)?
What is the primary goal of exploratory data analysis (EDA)?
In the context of data science, which step comes first in the process?
In the context of data science, which step comes first in the process?
Which of the following statements differentiates EDA from data visualization?
Which of the following statements differentiates EDA from data visualization?
What should be included in the modeling step of the data science process?
What should be included in the modeling step of the data science process?
What is an essential part of the communication and visualization step in data science?
What is an essential part of the communication and visualization step in data science?
In the example predicting neonatal infection, what is the first step of the workflow?
In the example predicting neonatal infection, what is the first step of the workflow?
Which decision-making process is recommended during the modeling stage of the data science workflow?
Which decision-making process is recommended during the modeling stage of the data science workflow?
What is a critical aspect to focus on during exploratory data analysis?
What is a critical aspect to focus on during exploratory data analysis?
What is the primary purpose of math and statistics in data science?
What is the primary purpose of math and statistics in data science?
Why is Python often chosen for data science tasks?
Why is Python often chosen for data science tasks?
What role does a data engineer fulfill in the data science process?
What role does a data engineer fulfill in the data science process?
Which of the following programming languages is NOT commonly associated with data science?
Which of the following programming languages is NOT commonly associated with data science?
In the data science process, what should be done if duplicates or outliers are found in the dataset?
In the data science process, what should be done if duplicates or outliers are found in the dataset?
What is domain knowledge, and why is it important in data science?
What is domain knowledge, and why is it important in data science?
What task is typically associated with the job of data engineers?
What task is typically associated with the job of data engineers?
What is a characteristic feature of Python that contributes to its popularity in data science?
What is a characteristic feature of Python that contributes to its popularity in data science?
Questions and Answers
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Flashcards
Flashcards
What is data?
What is data?
Data are individual units of information, describing a single quality or quantity of something. It can include numbers, words, measurements, or descriptions.
Data Science
Data Science
Using data to gain knowledge, make decisions, predict the future, understand the past, create new products, or understand the present.
Datafication
Datafication
A process of turning aspects of life into data to discover new purposes and values.
Data as the New Oil
Data as the New Oil
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Datum
Datum
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Data Science Process
Data Science Process
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Main areas of Data Science
Main areas of Data Science
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Examples of Datafication
Examples of Datafication
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Data Scientist
Data Scientist
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Data Analysis
Data Analysis
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Machine Learning
Machine Learning
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Data Volume
Data Volume
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Data Quality Issues
Data Quality Issues
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Data Science Areas
Data Science Areas
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Data Cleaning
Data Cleaning
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Why is Math important for data science?
Why is Math important for data science?
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What does 'formalize relationships' mean in data science?
What does 'formalize relationships' mean in data science?
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Why is Python a popular choice for data science?
Why is Python a popular choice for data science?
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What is a data engineer's role?
What is a data engineer's role?
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Data Science Process: What happens if data is bad?
Data Science Process: What happens if data is bad?
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What are some examples of data science applications?
What are some examples of data science applications?
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What is domain knowledge in data science?
What is domain knowledge in data science?
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Data Science Process: What happens after data collection?
Data Science Process: What happens after data collection?
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Data Pipeline
Data Pipeline
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Data Engineer
Data Engineer
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Data Profiling
Data Profiling
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Data Exploration
Data Exploration
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Open Data
Open Data
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EDA: Explanatory Data Analysis
EDA: Explanatory Data Analysis
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Transform Variables (Data Science)
Transform Variables (Data Science)
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EDA: What is it?
EDA: What is it?
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EDA vs. Data Visualization
EDA vs. Data Visualization
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Data Science Workflow: Define the Problem
Data Science Workflow: Define the Problem
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Data Science Workflow: Collect Data
Data Science Workflow: Collect Data
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Data Science Workflow: Explore and Prepare Data
Data Science Workflow: Explore and Prepare Data
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Data Science Workflow: Build and Evaluate Models
Data Science Workflow: Build and Evaluate Models
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Data Science Workflow: Communicate and Visualize Results
Data Science Workflow: Communicate and Visualize Results
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Predicting Neonatal Infection: Data Science Process
Predicting Neonatal Infection: Data Science Process
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Flashcards
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Study Notes
Study Notes
Introduction to Data Science
- Data science is the art and science of acquiring knowledge through data.
- Data science uses data to acquire knowledge for making decisions, predicting the future, and understanding the past/present, including creating new industries/products.
- Data is individual units of information that describe a single quality or quantity of an object.
- Data is a collection of facts such as numbers, words, measurements, observations, or descriptions of things.
- Data can be qualitative or quantitative, with qualitative data being descriptive like "great fun" and quantitative data being measurable like 5, 3.265...
Data All Around
- A vast amount of data is collected and warehoused.
- Data is collected from various sources, including web data, telecom, bank/credit transactions, online trading and purchasing, and social networks.
Data is the New Oil
- Data is valuable but needs refining to be usable.
- Data analysis is needed to utilize the value in the collected data.
Digging for Data: Datafication
- Datafication is the technological trend of turning many aspects of life into data.
- It's a process of taking all aspects of life and turning them into data.
- Datafication allows transforming the purpose of things and turning information into new forms of value.
Datafication Examples
- Social Platforms (Facebook): Collect and monitor data about actions and friendships to market products and services.
- Banking: Data such as income, gender, age, etc. can be used to determine the likelihood of a person paying back a loan.
- Life Insurance Industry: Data collected helps in calculating risk levels for life insurance plans.
Risk Prediction in Life Insurance Industry
- Various attributes (product information, age, height, weight, BMI, employment information, insurance history, family history, medical history, medical keywords) are used for risk prediction.
- Risk level is an ordinal measure with 8 levels.
Data Scientist Profile
- Data science involves expertise in data visualization, machine learning, mathematics, statistics, computer science, and domain expertise.
- No single person possesses all aspects of data science, thus, teamwork is needed.
Why Data Science
- In today's age, there's a surplus of data.
- The volume of data makes human parsing impossible.
- Data collection comes in various forms and from different sources.
- Data often comes disorganized, may be missing or incorrect, and may vary greatly in scale.
Main Areas of Data Science
- Math/Statistics: Using equations and formulas for analysis.
- Computer Programming: Using code to create outcomes.
- Domain Knowledge: Understanding the problem domain.
Data Science Venn Diagram
- The three areas (math/statistics, computer science/IT, and domain/business knowledge) intersect to form data science.
Data Science Process
- Step 1: Asking an Interesting Question: Framing the problem for a data science solution. This includes understanding the domain knowledge and refining the question.
- Step 2: Obtaining Data: Finding and collecting data that can answer the question. Data sources can be private or public.
- Step 3: Explore Data (EDA): Examining the data using plots, graphs, and summary statistics (Data profiling). The goal is understanding the data's shape, patterns, and potential errors.
- Step 4: Modeling: Using statistical or machine learning models or validating them through metrics.
- Step 5: Communicating and Visualizing Results: Reporting findings to stakeholders, presenting insights through visualizations, and communicating results.
Data Engineer
- Data engineers prepare data for analysis and operations.
- Data pipelines, integrating, cleansing, and structuring data is a typical data engineering task.
- Data engineers provide ready-to-use data for data scientists.
Example: Predicting Neonatal Infection
- Data science is applied to the problem of predicting neonatal infections in prematurely born children.
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