Certain assignments in this course will permit or even encourage the use of generative artificial intelligence (AI) tools, such as ChatGPT. When AI use is permissible, it will be clearly stated in the assignment prompt posted in Canvas. Otherwise, the default is that use of generative AI is disallowed. In assignments where generative AI tools are allowed, their use must be appropriately acknowledged and cited. For instance, if you generated the whole document through ChatGPT and edited it for accuracy, your submitted work would need to include a note such as “I generated this work through Chat GPT and edited the content for accuracy.” Paraphrasing or quoting smaller samples of AI generated content must be appropriately acknowledged and cited, following the guidelines established by the APA Style Guide. It is each student’s responsibility to assess the validity and applicability of any AI output that is submitted. You may not earn full credit if inaccurate on invalid information is found in your work. Deviations from the guidelines above will be considered violations of CMU’s academic integrity policy. Note that expectations for “plagiarism, cheating, and acceptable assistance” on student work may vary across your courses and instructors. Please email me if you have questions regarding what is permissible and not for a particular course or assignment.

You are welcome to use generative AI programs (ChatGPT, DALL-E, etc.) in this course.  These programs can be powerful tools for learning and other productive pursuits, including completing some assignments in less time, helping you generate new ideas, or serving as a personalized learning tool.

However, your ethical responsibilities as a student remain the same. You must follow  CMU’s academic integrity policy. Note that this policy applies to all uncited or improperly cited use of content, whether that work is created by human beings alone or in collaboration with a generative AI. If you use a generative AI tool to develop content for an assignment, you are required to cite the tool’s contribution to your work. In practice, cutting and pasting content from any source without citation is plagiarism. Likewise, paraphrasing content from a generative AI without citation is plagiarism. Similarly, using any generative AI tool without appropriate acknowledgement will be treated as plagiarism.

Cheating and plagiarism will not be tolerated. 

The emergence of generative AI tools (such as ChatGPT and DALL-E) has sparked large interest among many students and researchers. The use of these tools for brainstorming ideas, exploring possible responses to questions or problems, and creative engagement with the materials may be useful for you as you craft responses to class assignments. While there is no substitute for working directly with your instructor, the potential for generative AI tools to provide automatic feedback, assistive technology and language assistance is clearly developing. Course assignments may use Generative AI tools if indicated in the syllabus. AI-generated content can only be presented as your own work with the instructor’s written permission. Include an acknowledgment of how generative AI has been used after your reference or Works Cited page. TurnItIn or other methods may be used to detect the use of AI. Under UTD rules about due process, referrals may

be made to the Office of Community Standards and Conduct (OCSC). Inappropriate use of AI may result in penalties, including a 0 on an assignment.

• 1700s Agricultural Revolution 

• 1780 Industrial Revolution

• 1940 Information Revolution

• 1950s Digital Revolution

• Knowledge Revolution

• Data Revolution

• fundamental component in the process of analysis,
• where it serves as the raw input that can be processed to generate information and insights.

• structured and communicable
• can be interpreted and processed for various applications.

• reinterpretable
• can be reanalyzed or reused in different contexts to extract new information.

the basis for:

• modeling
• inference.

• Data is ever growing......

  • Moore's Law

  • Parkinson's Law

Moore's Law

Moore's Law

Moore's Law

Digital Revolution

Humankind's capacity for unidirectional information diffusion through broadcasting channels has experienced comparatively modest annual growth (6%). Telecommunication has been dominated by digital technologies since 1990 (99.9% in digital format in 2007), and the majority of our technological memory has been in digital format since 2000s.

Parkinson's Law of Data

“Data expands to fill the space available for storage.”

Quick note about size

Bits: 8 bits = 1 byte

Bytes: 1024 bytes = 1 KB (1 to 3 digits)

Kilobytes: 1024 KB = 1 MB (4 to 6 digits)

Megabytes: 1024 MB = 1 GB (7 to 9 digits)

Gigabytes: 1024 GB = 1 TB (10 to 12 digits)

Terabytes: 1024 TB = 1 PB (13 to 15 digits)

Petabytes: 1024 PB = 1 EB (16 to 18 digits)

Exabytes: 1024 EB = 1 ZB (19 to 21 digits)

Zettabytes: 1024 ZB = 1 YB (22 to 24 digits)

Yottabytes: more than enough... (25 to 27 digits)

A Taxonomy of Data

1. Numbers

2. Text

3. Images

4. Audio

5. Video

6. Signals

7. Data of data: Metadata and Paradata

    

1. Survey

2. Experiments

3. Qualitative Data

4. Text Data

5. Web Data

6. Complex Data

   1. Network Data

   2. Multiple-source linked Data

What is Big Data?

Big Data Research

CSE - Computer and Information Science and Engineering
ENG - Engineering
SBE - Social Behavioral and Economic Sciences
Mathematics and Physical Science

National Institutes of Health (NIH)

Office of Behavioral and Social Sciences (OBSSR)
The National Archive of Computerized Data on Aging (NACDA)
program advances research on aging by helping researchers to profit from the under-exploited potential of a broad range of
datasets. NACD preserves and makes available the largest library of electronic data on aging in the United States

Data Sharing for Demographic Research (DSDR) provides data archiving, preservation,  dissemination and other data infrastructure services. DSDR works toward a unified legal,
technical and substantive framework in which to share re
search data in the population sciences.

UNITED STATES GEOLOGICAL SURVEY (USGS)

The USGS John Wesley Powell Center for Analysis and Synthesis
just announced eight new research projects for transforming big data sets and big ideas about earth science theories into
scientific discoveries. At the Center, scientists collaborate to perform state of the art synthesis to leverage comprehensive, long-term data.

• Prediction-explanation gap

• Induction-deduction gap

• Bigness-representativeness gap

• Data access gap

1. Science of Data

2. Understand Data Scientifically

Data Science Keywords

• Data collection and production

  • Data mining

  • Web scraping

• Data Visualization

  • Interactive charts

  • Dashboards

• Data management

  • Database

  • SQL, NoSQL

• Data analytics

  • Machine learning 

  • Deep learning 

• CRMs

How Data are generated?

• Computers

• Web

• Mobile devices

• IoT (Internet of Things)

• Further extension of human users (e.g. AI, avatars)

How Data are generated?

How Data are generated?

"Data Lake" Ubiquitous

Massive raw data repository in its rawest form pending processing.

Social (Data) Scientist's mission

Social (Data) Scientist's mission

Social (Data) Scientist's mission

The story of Google Flu Trend

A Theory of Data: Understanding Data Generation

Administrative Data

Administrative data are defined as data which derive from the operation of administrative systems, typically by public sector agencies

- Connelly et al. 2016

1. Survey

2. Experiments

3. Qualitative Data

4. Text Data

5. Web Data

6. Machine Data

7. Complex Data

   1. Network Data

   2. Multiple-source linked Data

Statistical Modeling:
The Two Cultures 

Theory:
Data Generation Process

Theory:
Data Generation Process

Theory:
Data Generation Process

Theory:
Data Generation Process

Theory:
Data Generation Process

 Algorithmic Modeling

Data Science Roadmap

1. Introduction - Data theory

2. Data methods

3. Statistics

4. Programming

5. Data Visualization

6. Information Management

7. Data Curation

8. Spatial Models and Methods

9. Machine Learning

10. NLP/Text mining

Data Science Roadmap

1. Introduction - Data theory

   1. Fundamentals

      1. Data concepts

      2. Data Generation Process (DGP)

   2. Algorithm-based vs. Data-based approaches

   3. Taxonomy

Data Science Roadmap

2. Data methods

   1. ​Passive data

   2. Data at will

   3. Qualitative data

   4. Complex data

   5. Text data

Data Science Roadmap

3. Statistics

   1. Sample and Population

   2. Inference

   3. Size and power

   4. Representation

Data Science Roadmap

4. Programming

   1. R

   2. Python

   3. HTML

   4. Java script

Data Science Roadmap

5. Data Visualization

   1. Tableau

   2. ggplot2

   3. Shiny

   4. D3.js

   5. Animation

Data Science Roadmap

6. Information Management

   1. MapReduce

   2. Hadoop

   3. Cassandra

   4. MongoDB

   5. NoSQL

Data Science Roadmap

7. Data curation

   1. Google OpenRefine

   2. Sampling

   3. Missing value concepts and management

Data Science Roadmap

8. Spatial Models and Methods

   1. GIS

   2. R/Leaflet

   3. Python Map

   4. Remote Sensing

Data Science Roadmap

9. Machine Learning

   1. Supervised

   2. Unsupervised

   3. Regression methods

   4. Neural Networks

Data Science Roadmap

10. NLP/Text Mining

    1. Corpus

    2. Text Analysis

    3. Sentiment Analysis

    4. Natural Language Processing

Data Literacy

1. Data generating process
2. Graphic grammar
3. Statistical judgement

Data Literacy

Data Literacy

Data Literacy

1. Why we need numeric data?
2. History of data