Introduction to Data Science Concepts
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Questions and Answers

What is a primary focus of data science?

  • Restricting data access
  • Physical data storage
  • Data collection and preparation (correct)
  • Hardware development

    • Which of the following statements best represents the concept of data science?

  • Data science focuses solely on sociological data.
  • Data science is exclusively a computational discipline.
  • Data science involves the integration of various fields. (correct)
  • Data science is limited to statistics only.

    • Which of the following components is NOT part of the data science formula?

  • Statistics
  • Hardware engineering (correct)
  • Informatics
  • Communication

    • What does the phrase 'data science is the science of data' imply?

    <p>Data science involves theoretical understanding of data.</p> Signup and view all the answers

    Which of the following elements contributes to the management aspect of data science?

    <p>Data processing protocols</p> Signup and view all the answers

    What type of applications does data science pertain to?

    <p>Translational and inter-disciplinary applications</p> Signup and view all the answers

    In data science, what is meant by 'heterogeneous data'?

    <p>Data that varies in type and structure</p> Signup and view all the answers

    Which of the following best describes data visualization in the context of data science?

    <p>A way to communicate findings through graphical representations</p> Signup and view all the answers

    What components are included in the formula for data science?

    <p>statistics, informatics, computing, communication, sociology, management</p> Signup and view all the answers

    Which tool is primarily used for managing versioning and sharing code?

    <p>Git and GitHub</p> Signup and view all the answers

    What is the primary purpose of the Global Biodiversity Information Facility (GBIF)?

    <p>To give open access to data about all types of life on Earth</p> Signup and view all the answers

    Which of the following is NOT a component of a data science workflow?

    <p>Analyze market trends</p> Signup and view all the answers

    In which environment is the code typically developed or adapted?

    <p>Jupyter Notebook/Colab</p> Signup and view all the answers

    What best practice is recommended for data science project management?

    <p>Version control using Git and GitHub</p> Signup and view all the answers

    Which of the following is a common misconception about the data science formula?

    <p>Data science can be simplified to just statistics</p> Signup and view all the answers

    Which environment is primarily associated with file management in the data science workflow?

    <p>Bash</p> Signup and view all the answers

    What is the primary purpose of machine learning?

    <p>To allow a computer to learn from data</p> Signup and view all the answers

    How does deep learning differ from traditional machine learning?

    <p>It is inspired by the structure of the human brain</p> Signup and view all the answers

    What sets machine learning apart from conventional programming methods?

    <p>It constructs models based on data training</p> Signup and view all the answers

    Which aspect of machine learning is primarily focused on making classifications or predictions?

    <p>The statistical methods employed</p> Signup and view all the answers

    Which statement accurately reflects the relationship between machine learning and deep learning?

    <p>All deep learning techniques are part of machine learning</p> Signup and view all the answers

    What is one of the main causes of inefficiencies noted in the agro-environment data science?

    <p>Inadequate monitoring</p> Signup and view all the answers

    Which technology is used for low-power local connectivity to enhance traceability?

    <p>Zigbee</p> Signup and view all the answers

    What is a significant advantage of traceability in the food supply chain?

    <p>Determines carbon footprint</p> Signup and view all the answers

    Which factor is NOT listed as part of the carbon footprint in the traceability context?

    <p>Engine emissions</p> Signup and view all the answers

    What is a challenge in implementing traceability across the food supply chain?

    <p>Multiple countries involved</p> Signup and view all the answers

    Which of the following tools is primarily used for data visualization in data science?

    <p>Matplotlib</p> Signup and view all the answers

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

    <p>Python</p> Signup and view all the answers

    What type of data preparation is crucial for optimizing decision support systems in the food chain?

    <p>Data transformation and organization</p> Signup and view all the answers

    Which connectivity type offers global coverage in the context of IoT solutions for traceability?

    <p>Long-range low-power IoT</p> Signup and view all the answers

    Which library is primarily associated with machine learning in Python?

    <p>Scikit-learn</p> Signup and view all the answers

    What distinguishes open data from other types of data?

    <p>It can be used, modified, and shared without restrictions.</p> Signup and view all the answers

    Which statement best describes the concept of '5 Star Open Data'?

    <p>It requires linking data to external datasets to enhance context.</p> Signup and view all the answers

    What is the purpose of using open standards in open data?

    <p>To ensure data can be easily accessed and used by different systems.</p> Signup and view all the answers

    Which of the following is NOT a feature of open data?

    <p>Requires special licensing for educational use only.</p> Signup and view all the answers

    Which Creative Commons (CC) license allows for both commercial use and modification without restriction?

    <p>Attribution (BY)</p> Signup and view all the answers

    What role does community feedback and verification play in open data?

    <p>It ensures data accuracy and enhances trustworthiness.</p> Signup and view all the answers

    In what way are costs associated with open data typically characterized?

    <p>They are negligible and often relate to reproduction costs.</p> Signup and view all the answers

    What is a crucial characteristic of data to qualify as open data?

    <p>It should be structured and machine-readable.</p> Signup and view all the answers

    What is the primary function of Generative AI?

    <p>To create novel content that mimics human creations</p> Signup and view all the answers

    Which type of datasets do Predictive AI models typically use?

    <p>Smaller, more targeted datasets</p> Signup and view all the answers

    Which algorithm is commonly used in Generative AI?

    <p>Generative adversarial networks (GANs)</p> Signup and view all the answers

    What is the purpose of Predictive AI?

    <p>To forecast future events and outcomes</p> Signup and view all the answers

    In which application area is Generative AI frequently used?

    <p>Customer service</p> Signup and view all the answers

    Which statement is true regarding the output of Generative AI models?

    <p>They create original content</p> Signup and view all the answers

    What distinguishes Predictive AI from Generative AI?

    <p>Predictive AI focuses on prediction based on historical data while Generative AI creates new content</p> Signup and view all the answers

    Which of the following represents a common use case for Predictive AI?

    <p>Fraud detection</p> Signup and view all the answers

    What type of analysis does Generative AI primarily involve?

    <p>Statistical analysis blended with machine learning to create new content</p> Signup and view all the answers

    Which type of machine learning is aimed at mimicking human intelligence or behavior?

    <p>Generative AI</p> Signup and view all the answers

    Study Notes

    Agro-Environment Data Science Course - Lesson 01

    • This course introduces the fundamentals of agro-environmental data science.
    • The first lesson covers what data science is.
    • The course overview includes data science, methodology, tools, resources, and culture.

    Assessment

    • Assignments account for 40% or the final exam.
    • Two short assignments focus on operational knowledge.
    • A project (40%), done in groups, requires identifying and defining a problem solved through data science.
    • The project report should include the problem description.
    • Participation earns 20%, through weekly exercises.

    Project - Work Group

    • The goal is to create and design a data science project on natural resources, food, or the environment.
    • Components of the project include identifying and justifying an unanswered question, identifying skills and responsibilities of team members, identifying data sources, challenges and strategies to pre-process data, identifying modeling approaches, and outlining the implementation path to deliver the solution.
    • The deliverables are a written report, a presentation, and, if applicable, a mockup of the product (dashboard or web application).

    Welcome Kit

    • The welcome kit includes Python, Jupyter Notebooks, VPN, Google for Education account, Google Collaboratory, Git, Github, text editor, MariaDB, Dbeaver, and Discord.
    • A link to the kit is provided.

    What is Data Science?

    • An activity involving defining Data Science in your own words.
    • Key questions for the activity include: What is Data Science?, What do Data Scientists do?, What tools are used by Data Scientists?, and What is particular about Data Science applied to Natural Resources and Environment?.
    • A Jamboard activity using a link is suggested.

    Definition of Data Science

    • Data science is the science that deals with big amounts of data.
    • It involves managing data to do many tasks, including predict something.
    • Data science requires using mathematical models, and statistics management for more efficient ways to deal with large data sets through software.
    • Data visualization and analysis are essential tools for data science.

    What do Data Scientists do?

    • Data scientists study and organize data to make it useful.
    • They use the right tools to manage data and make it simple for people to understand.
    • They create dense data easier to understand and work with.
    • They use specific tools to deal with big data and data analysis.
    • 90% of their time is spent cleaning data

    What tools do Data Scientists use?

    • Data scientists use various tools including SQL code, visualization/editing software.
    • Databases are a critical tool (e.g. Python, Excel, power, query/dax, SQL).
    • Programming tools, databases, and scientific expertise are important
    • Collaborative tools (e.g., python, R, and visual tools like Power BI) are often used.

    What is specific to NatRec and Env?

    • Domain knowledge or usage of IoT is important for natural resources/environment research.
    • The complex life cycle and behavior of study subjects are challenges.
    • Factors such as the area of study, variables that are difficult to control by humans, unstable, and unpredictable variables, or high usage of IoT can be noteworthy in these fields.
    • Data sources, modeling resources, characteristics of the fields, and spatial dimensions also play important roles in NatRec and Env data science.

    What is Data Science? - Definition

    • Data science is an emerging field encompassing data collection, preparation, analysis, visualization, management, and preservation of information.
    • It involves methods for data discovery, and practice involving vast data associated with diverse scientific applications.

    What is data science? - definition (cont)

    • Data science is a process involving data analysis, computer power, revealing new knowledge in organizations, specific problem/questions, curiosity, data needs (structured and unstructured), and techniques for exploring patterns, modelling, communicating results through visualization and storytelling.

    What is data science? - definition (cont)

    • Data science relies on data as a critical component of decision-making crucial for organizational functions.
    • Data quality depends on its accessibility, correctness, and completeness from various sources.
    • Data collection, storage, and processing incur costs; therefore, data integration and efficient use in organizations are crucial.

    ### What is data science? - skills

    • A data scientist must have superior statistical knowledge.
    • They should excel at software engineering.
    • The core skill involves finding solutions to data problems, communicating findings to relevant stakeholders.
    • Skills include being curious, characterizing problems, having a taste for technologies, liking teamwork, and having mathematical/statistical knowledge.

    What is data science? - skills (cont)

    • Technical skills are relevant, including programming, statistics, data management systems, data extraction, machine learning, processing large datasets, visualization, model deployment, and cloud computing.
    • Soft skills are equally important — expertise, data intuition, communication, and teamwork.

    What is data science? - application examples

    • Identifying the veraison process of colored wine grapes is achieved via deep learning/image analysis, with a test accuracy of over 91% for three varieties.
    • Pest detection, through CNNs, demonstrates 97.55% mean average precision in grain detection.

    What is data science? - environment

    • The Data Science Environment considers tools like Linux, MacOS, Windows, Python, SQL, Visual Studio Code, Notepad++, MariaDb, MySQL, Git, GitHub, Google Cloud, and IBM Cloud;
    • The methodology includes Obtain, Scrub, Explore, Model, and Interpret (OSEMN).

    Additional reading materials provide resources on data science fundamentals, including overviews, the history of data science, and detailed explanations. For the specified lessons, resources might be valuable for more in-depth knowledge.

    Data Science Methodology (Methods - KDD, CRISP, SEMMA, OSEMN)

    • KDD: Knowledge Discovery in Databases
    • CRISP-DM: Cross-industry standard process for Data Mining
    • SEMMA: Sample, Explore, Modify, Model, Assess methodology
    • OSEMN: Obtain, Scrub, Explore, Model, Interpret methodology

    Data Management Plan - DMP

    A formal document that outlines how data is managed within the scope of an activity. The DMP includes several questions, including those about data type, format, privacy, access, and archiving.

    FAIR data principles

    • A set of principles that enhances data understanding, discoverability, and reuse to maximize its value.
    • The principles include findability, accessibility, interoperability, and reusability
    • PIDs: Unique identifiers used to reference data enabling proper tracking.

    Persistent Identifiers (PIDs)

    • They offer a persistent method for consistently linking to the target item.
    • PIDs are crucial for traceability, ensuring that items can be definitively linked to the data source.
    • PIDs, given their unique nature, are less likely to become irrelevant in the case of context changes.

    ### FAIR Data (Accessibility)

    • Data must be retrievable using a standardized protocol;
    • Open, free, and readily available, enabling universal implementation.
    • Allows authentication and authorization measures where needed.
    • Metadata remains accessible, even after the data is no longer directly accessible.

    FAIR Data (Interoperability)

    • Data uses a standardized, formal, shareable, and widely applicable language.
    • Data utilizes vocabularies that conform to FAIR principles;
    • Data includes qualified references to other data.

    FAIR Data (Reusability)

    • Rich, detailed, and accurate data descriptions are provided with appropriate attributes.
    • Clear and accessible data usage licenses are required.
    • A clear, accessible data provenance record should be associated.
    • Data aligns with domain-relevant community standards.

    Data Science Tools (Lesson 05/06/07-8)

    • This section will cover specific data science tools.
    • The topics of interest are specific programming tools, such as Python and SQL.
    • Related Libraries or IDE environments for data analysis were also covered.
    • APIs, and web scraping procedures will help process data or provide additional tools and resources.

    Data Science - Tools for Specific Purposes

    • programming: open-source & commercial (visual)
    • data management, extraction, web-scraping, transformations and visualization
    • cloud computing

    Data for Data Science

    • Data sets are structured collections of data that can be tabular (table-like), or hierarchical; or network-based data.
    • Metadata are essential for data understanding in the context of relevant analyses.
    • Data ownership and access are divided into two categories: Private and Open.
    • Private data encompasses private or personal information or commercially sensitive data.
    • Open data is often available through publicly accessible sources such as scientific institutions, governments, organizations, and corporations.

    Data Spectrum

    • Data sets span a spectrum of access types.
    • Different access types exist for specific data usage and ownership (closed, shared, and open).
    • Factors like personnel contact, contract specifics, authentication, and licenses impact data access.

    Motivations for Open Data Adoption

    • Significant benefits are observed from Open Data adoption, such as the prevention of road fatalities.
    • Reduced congestion costs, along with considerable savings in terms of time and resources are also important factors.
    • Encouraging better decision-making practices is another significant motivator.

    Open Data - Wrap Up

    • Open data is accessible, reusable, and sharable to anyone, including commercial users.
    • There can be costs associated with creating, maintaining and publishing usable data sets.
    • Data quality should be considered and assessed by examining its value based on the use, and not its source.
    • Open data formats and machine-readable standards are essential for data value.

    Creative Commons (CC) Licenses

    • These licenses govern how data and other Creative Commons work can be reused and distributed.
    • Creative Commons licenses grant users specific permissions and restrictions.
    • Detailed explanations exist regarding how their restrictions and permissions apply to different uses of data.

    5-Star Open Data

    • Several criteria apply, including available licenses, re-usable formats, use of identifiers for reference, and linking to other data sets to provide context.
    • The presented data, with context and access, should ensure that all elements needed for data exploration or use are easily achievable with access to diverse data sets.

    Open Data and Data Quality

    • Open data should be subject to practices for transparency, community feedback mechanisms, open standards, and correct citation/usage to ensure data quality.

    Tools for Data Science - Data Sources

    • Several sources of data are accessible for data science purposes, including those provided by the United Nations, the FAO, Copernicus, European data, and the U.S. Open Data.
    • Data is available through online portals, communities, and database searches such as Kaggle and Google Data Search.

    Tools for Data Science - API

    • Application Programming Interfaces (APIs) define how various computer components interact to share data.
    • APIs use HTTP protocol and JSON for transferring data across the internet (structured formats).

    Tools for Data Science - Web Scraping

    • This approach automatically gathers information from external websites given the web page's structure.
    • Tools used in this context often involve combining Python modules such as requests and BeautifulSoup to execute web scraping operations.

    Overview of Modeling Approaches in Data Science (Lessons 9-13)

    • These lessons provide an overview of modeling techniques in data science. The course focuses on unsupervised, supervised, semi-supervised, and reinforcement learning techniques including;
    • clustering, dimensionality reduction, regression, classification, decision trees, random forests, support vectors machines, etc

    Unsupervised Learning (Lesson 11-12)

    • Data is unlabeled in unsupervised learning.
    • Categories and clusters of data can be identified through methods like clustering (e.g., k-means, hierarchical) or dimensionality reduction (e.g., PCA).
    • The techniques of K-means and Hierarchical Clustering methodologies are studied and reviewed.

    Semi-Supervised Learning (Lesson 12)

    • A hybrid of supervised and unsupervised learning is used with a small dataset of labeled examples.
    • This helps to label a large set of unlabeled data to train more effective models or algorithms.
    • Two techniques exist: transductive and inductive learning—both discussed and reviewed, and their application examples showcased in crop pest detection contexts.

    Reinforcement Learning (Lesson 12)

    • Reinforcement algorithms learn through trial and error by acting upon an environment.
    • In such models, algorithms learn based on feedback systems either giving rewards or punishment to adjust actions based on successful experiences.
    • Examples including various applications from data center cooling to other areas, such as autonomous vehicles.

    Communicating Results (Lesson 13)

    • Data visualization tools help to communicate complex data in a clear, concise manner to the audience.
    • Visualizations and report structures are presented in detail including data processing, information modeling, and report structure techniques
    • Key concepts include narrative visualizations, storytelling process, and data visualization workflow methods.

    Data Science Ethics (Lesson 14)

    • Ethical considerations surrounding data obtainment/use of data or data-analysis procedures are reviewed.
    • Issues regarding privacy, implicit bias or fairness, and reproducibility are discussed, along with the importance of informed consent and the challenges in properly applying and managing data.
    • Ethical guidelines for data science or research ethics, frameworks, and checklists for ethical frameworks in data science were introduced and reviewed.
    • Common issues such as data bias, lack of representation in data sets, or inappropriate use of data were discussed as well as the application of data ethics to various algorithms or models.
    • Ethical issues associated with data analysis or data-driven processes are also reviewed, including data collection ethics, privacy, informed consent, and implications of using data for targeted marketing campaigns. 

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