Introduction to Data Science
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Questions and Answers

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?

  • 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?

  • 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?

<p>Preserving data indefinitely. (A)</p> Signup and view all the answers

How does data science contribute to creating new industries?

<p>Through the application of acquired knowledge in innovative ways. (D)</p> Signup and view all the answers

What is a common application of data in the banking sector?

<p>Determining the likelihood of loan repayment based on individual data. (C)</p> Signup and view all the answers

Which of the following describes the role of a data scientist?

<p>Analyzes data to extract insights and informs strategic decisions. (B)</p> Signup and view all the answers

In what way can social platforms like Facebook utilize data?

<p>To target marketing based on actions and friendships. (A)</p> Signup and view all the answers

What is the primary role of a data engineer within an analytics team?

<p>To provide data in a ready-to-use form. (C)</p> Signup and view all the answers

Which of the following is NOT one of the five essential steps for performing data science?

<p>Writing code for data manipulation (A)</p> Signup and view all the answers

During the exploratory data analysis (EDA) phase, which technique is used for identifying outliers?

<p>Box plots (C)</p> Signup and view all the answers

What is the first step in the data science process?

<p>Asking an interesting question (C)</p> Signup and view all the answers

Which of the following could be considered a source of data when attempting to answer a data science question?

<p>Open Data (D)</p> Signup and view all the answers

What is the purpose of plotting distributions of all variables during EDA?

<p>To systematically understand data characteristics. (A)</p> Signup and view all the answers

Which action is primarily performed in the 'obtaining the data' step of the data science process?

<p>Data mining from available sources (B)</p> Signup and view all the answers

In the context of data science, what is the significance of domain knowledge?

<p>It combines with technical knowledge to solve problems. (C)</p> Signup and view all the answers

What is a primary role of a data scientist?

<p>To make predictions and answer key questions using data (A)</p> Signup and view all the answers

Which of the following skills is essential for a data scientist?

<p>Understanding domain-specific knowledge (A)</p> Signup and view all the answers

What is one of the three basic areas essential for understanding data science?

<p>Mathematics/statistics (C)</p> Signup and view all the answers

Why is new vocabulary necessary in the field of data science?

<p>To describe the complexities of modern data challenges (A)</p> Signup and view all the answers

What does the term 'domain knowledge' refer to in data science?

<p>Understanding the problem area related to data (C)</p> Signup and view all the answers

What common issue do data scientists often face when handling data?

<p>Data can be incomplete, missing, or incorrect (D)</p> Signup and view all the answers

Which area does NOT contribute to a data scientist's expertise?

<p>Psychology (A)</p> Signup and view all the answers

What does a data scientist primarily use computer programming for?

<p>To access, manipulate data, and develop models (D)</p> Signup and view all the answers

What is the primary goal of exploratory data analysis (EDA)?

<p>To understand the data and its generating process (A)</p> Signup and view all the answers

In the context of data science, which step comes first in the process?

<p>Define the problem (A)</p> Signup and view all the answers

Which of the following statements differentiates EDA from data visualization?

<p>EDA occurs at the start of analysis while visualization communicates findings. (A)</p> Signup and view all the answers

What should be included in the modeling step of the data science process?

<p>Fitting and choosing models (D)</p> Signup and view all the answers

What is an essential part of the communication and visualization step in data science?

<p>Ensuring quick understanding of trends and relationships (D)</p> Signup and view all the answers

In the example predicting neonatal infection, what is the first step of the workflow?

<p>Define the problem/question (B)</p> Signup and view all the answers

Which decision-making process is recommended during the modeling stage of the data science workflow?

<p>Compare multiple models for effectiveness (B)</p> Signup and view all the answers

What is a critical aspect to focus on during exploratory data analysis?

<p>Understanding clusters and patterns within the data (B)</p> Signup and view all the answers

What is the primary purpose of math and statistics in data science?

<p>To theorize relationships between variables (B)</p> Signup and view all the answers

Why is Python often chosen for data science tasks?

<p>It has a vast and friendly online community (B)</p> Signup and view all the answers

What role does a data engineer fulfill in the data science process?

<p>They prepare data for analytical uses (B)</p> Signup and view all the answers

Which of the following programming languages is NOT commonly associated with data science?

<p>C# (A)</p> Signup and view all the answers

In the data science process, what should be done if duplicates or outliers are found in the dataset?

<p>Collect more data or clean the dataset (A)</p> Signup and view all the answers

What is domain knowledge, and why is it important in data science?

<p>It involves understanding the specific industry relevant to the analysis (D)</p> Signup and view all the answers

What task is typically associated with the job of data engineers?

<p>Cleaning and preprocessing data (A)</p> Signup and view all the answers

What is a characteristic feature of Python that contributes to its popularity in data science?

<p>It supports a wide range of data science libraries (B)</p> Signup and view all the answers

Flashcards

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

Using data to gain knowledge, make decisions, predict the future, understand the past, create new products, or understand the present.

Datafication

A process of turning aspects of life into data to discover new purposes and values.

Data as the New Oil

Data is valuable but needs processing to be useful, like oil that needs refining.

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Datum

A single piece of information or data.

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Data Science Process

The stages involved in using data to gain knowledge, make decisions, or predict the future.

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Main areas of Data Science

Various fields where Data Science is applied (not fully specified in the text).

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Examples of Datafication

Collecting and analyzing data about various aspects of life like social media or banking (not fully specified).

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Data Scientist

A specialist who uses statistical methods, machine learning models, and data analysis techniques to extract knowledge from data.

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Data Analysis

Examining data to find patterns, trends, and insights.

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Machine Learning

A type of artificial intelligence where computers learn from data without explicit programming.

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Data Volume

The sheer amount of data available.

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Data Quality Issues

Imperfections in data, like missing values or errors.

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Data Science Areas

Consists of math/statistics, programming, and domain knowledge.

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Data Cleaning

The process of fixing or removing errors and inconsistencies from data.

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Why is Math important for data science?

Math helps understand how algorithms work, evaluate their performance, and adapt them to fit specific situations.

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What does 'formalize relationships' mean in data science?

Using math and statistics to establish connections between different variables (like sales & advertising spending), making them clear and quantifiable.

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Why is Python a popular choice for data science?

Python is simple to learn, widely used in both industry and academia, has a large supportive community, and offers ready-made data science tools.

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What is a data engineer's role?

Data engineers prepare data for analysis or operational uses, like cleaning, transforming, and organizing it.

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Data Science Process: What happens if data is bad?

If your data has duplicates, missing values, or outliers, you might need to collect more data or spend more time cleaning the dataset.

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What are some examples of data science applications?

Spam classifiers, search rankings, and recommendation systems are all examples of data science in action.

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What is domain knowledge in data science?

Understanding the specific topics of a project. Examples: medicine, marketing, or banking.

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Data Science Process: What happens after data collection?

After data collection, data is cleaned, preprocessed, and then analyzed using algorithms. Sometimes it's necessary to return to collecting more data based upon the analysis.

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Data Pipeline

A sequence of steps used to process and analyze data, often involving extracting data from multiple sources, cleaning it, and structuring it for specific applications. It's like an assembly line for your data.

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Data Engineer

A professional who builds and manages data pipelines, ensuring the smooth flow and quality of data for analysis. They are the data pipeline architects.

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Data Profiling

The process of examining the characteristics and quality of data, often through visualizations and statistics. It's like taking a detailed inventory of your data.

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Data Exploration

The process of understanding data patterns and relationships through visualizations, statistical summaries, and interactive analysis. You are trying to uncover hidden stories in the data.

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Open Data

Data that is freely available for anyone to use, often provided by governments, organizations, or research institutions.

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EDA: Explanatory Data Analysis

A key part of data exploration, using graphs, plots, and summaries to discover patterns, relationships, and insights within the data.

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Transform Variables (Data Science)

Modifying data for analysis by changing its representation. This can include one-hot encoding categorical variables or scaling numerical variables and more.

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EDA: What is it?

Exploratory Data Analysis (EDA) is the initial stage of analysis where you examine data to gain a general understanding of its shape and structure. It involves discovering patterns, identifying outliers, and understanding the data's characteristics.

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EDA vs. Data Visualization

EDA is done at the beginning of analysis to understand the data, while data visualization is done at the end to communicate findings. EDA focuses on discovery, while data visualization emphasizes communication.

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Data Science Workflow: Define the Problem

This is the first step in the Data Science workflow where you clearly define the problem you want to solve using data. It involves identifying the specific question you want to answer or the task you wish to accomplish.

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Data Science Workflow: Collect Data

This step involves gathering and assembling all the relevant data needed for your analysis. It requires identifying appropriate sources and gathering data in a systematic manner.

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Data Science Workflow: Explore and Prepare Data

This is where you clean, transform, and prepare the collected data for analysis. It involves handling missing values, correcting inconsistencies, and organizing the data to make it suitable for modeling.

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Data Science Workflow: Build and Evaluate Models

This step involves selecting and building statistical or machine learning models to answer your research question. Different models are compared based on their performance and accuracy.

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Data Science Workflow: Communicate and Visualize Results

This is the final step where you present your findings to stakeholders through reports, presentations, or visualizations. It involves effectively communicating the insights derived from data analysis.

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Predicting Neonatal Infection: Data Science Process

This is a real-world example where data science is used to predict neonatal infection. The entire data science workflow is applied to achieve this goal, from defining the problem to collecting, preparing, modeling, and visualizing the results.

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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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Introduction to Data Science PDF

Description

Explore the fundamental concepts of data science, its importance, and how data is collected and utilized in various sectors. This quiz covers key definitions, types of data, and the value of data in decision-making and industry innovation.

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