Social and Political Data Science: Introduction

Karl Ho

School of Economic, Political and Policy Sciences

University of Texas at Dallas

Artificial Intelligence:
Global Opportunities

Presentation prepared for Vidya Academy of Science and Technology, Kerala, India,  October 20, 2020

Introduction

  • Artificial Intelligence: Why AI and Data Science

  • What is AI?

    • Predictive Analytics

    • Data Science

  • What is Data Science?

  • Global Opportunities: Next Generations of Engineers in the age of Data Revolution

Overview

With some Data Science knowledge at your fingertips, you and your colleagues will begin asking the right questions instead of assuming the wrong answers.

- Luke Posey 2019

Engineering + Data Science: The Missing Duo. 
​https://towardsdatascience.com/engineering-data-science-the-ultimate-yet-somehow-missing-duo-597eb21dda98

Why Engineering and Data Science?

Engineers can avoid wasting the massive data sets piling up in manufacturing plants, processing plants, and other data heavy areas when they find the data scientists among themselves.

Two solutions:

  1. Hire data scientists to help engineer with mounting complex data
  2. Train engineers data science to deal with data themselves

Which one is more expensive?

Again, wrong question!

What is the question?

Next generation of engineers should equip themselves with data science skills, just like typing.

Darkest hour: Churchill and typist

Data Literacy

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

 

Data Literacy

  1. Data generating process
    1. ​How data are generated
    2. Distribution
    3. Missing values
    4. Wrong data

 

Data Literacy

  1. Graphic grammar
    1. Bad charts deliver incorrect message
    2. Poor design
    3. Color
    4. Label
    5. Scale

Data Literacy

  1. Statistical understanding
    1. Size does (not) matter
    2. Representativeness does
    3. Forecast/prediction minded
    4. Explanation

Data Literacy

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

Precision

Precision

Precision

Prediction

Prediction

Prediction

Engineers

Data Scientists

X

"..digital revolution transforms our views about values and priorities, good behaviour, and what sort of innovation is not only sustainable but socially preferable – and governing all this has now become the fundamental issue."

- Luciano Floridi 2018

Soft ethics, the governance of the digital and the General Data Protection Regulation. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2133), p.20180081.

Data science is the process of using algorithms, methods, and systems to extract knowledge and insights from structured and unstructured data. It uses analytics and machine learning to help users make predictions, enhance optimization, and improve operations and decision making.

- IBM 2020

https://www.ibm.com/analytics/data-science 

Conway, Jennifer. 2010. Artificial Intelligence and Machine Learning: Current Applications in Real Estate

https://dspace.mit.edu/bitstream/handle/1721.1/120609/1088413444-MIT.pdf

Conway, Jennifer. 2010. Artificial Intelligence and Machine Learning: Current Applications in Real Estate

https://dspace.mit.edu/bitstream/handle/1721.1/120609/1088413444-MIT.pdf

McKinsey Global Institute. 2016. THE AGE OF ANALYTICS: COMPETING IN A DATA-DRIVEN WORLD https://www.mckinsey.com/~/media/mckinsey/industries/public%20and%20social%20sector/our%20insights/the%20age%20of%20analytics%20competing%20in%20a%20data%20driven%20world/mgi-the-age-of-analytics-full-report.pdf609/1088413444-MIT.pdf

As data ecosystems evolve, value will accrue to providers of analytics, but some data generators and aggregators will have unique value

McKinsey Global Institute. 2016. THE AGE OF ANALYTICS: COMPETING IN A DATA-DRIVEN WORLD https://www.mckinsey.com/~/media/mckinsey/industries/public%20and%20social%20sector/our%20insights/the%20age%20of%20analytics%20competing%20in%20a%20data%20driven%20world/mgi-the-age-of-analytics-full-report.pdf609/1088413444-MIT.pdf

As data ecosystems evolve, value will accrue to providers of analytics, but some data generators and aggregators will have unique value

McKinsey Global Institute. 2016. THE AGE OF ANALYTICS: COMPETING IN A DATA-DRIVEN WORLD https://www.mckinsey.com/~/media/mckinsey/industries/public%20and%20social%20sector/our%20insights/the%20age%20of%20analytics%20competing%20in%20a%20data%20driven%20world/mgi-the-age-of-analytics-full-report.pdf609/1088413444-MIT.pdf

As data ecosystems evolve, value will accrue to providers of analytics, but some data generators and aggregators will have unique value

  1. Data Collection

  2. Data Management

  3. Data Visualization

  4. Data Modeling 

Artificial Intelligence

Machine Learning

Deep Learning

Prediction

Data Science Training

Data scientists are the no. 1 most promising job in America, according to LinkedIn.

Data scientists saw a 56% increase in job openings (4,000+) in the US in 2018.

Median Base Salary: $130,000

IBM Predicts Demand For Data Scientists Will Soar 28% By 2020

 Tools that don’t qualify on their own:

  • Excel

  • PowerPoint

  • Adobe Analytics

  • Google Analytics

  • Tableau

- Mike Driscoll

“The Three Sexy Skills of Data Geeks”: “…with the Age of Data upon us, those who can model, munge, and visually communicate data...

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

  1. Data methods

    1. ​Small data and big data

    2. Qualitative data

    3. Complex data

    4. Spatial data

    5. Text data

    6. Web data/Social media data

Data Science Roadmap

  1. Statistics

    1. Sample and Population

    2. Size and power

    3. Causal Inference

Data Science Roadmap

  1. Programming

    1. R

    2. Python

    3. HTML

    4. JavaScript

    5. Julia

    6. Cassandra

    7. Go

    8. C++

Data Science Roadmap

  1. Data Visualization

    1. R

      1. ggplot2

      2. Shiny

    2. Python

      1. Dash

      2. Django

    3. JavaScript

      1. D3.js

    4. Animation

Data Science Roadmap

  1. Information Management

    1. SQL

    2. MapReduce

    3. Hadoop

    4. NoSQL

    5. Cassandra

    6. MongoDB

    7. Neo4j

Data Science Roadmap

  1. Data curation

    1. Google OpenRefine

    2. Sampling

    3. Missing value management

    4. R Tidyverse

    5. Python Panda

Data Science Roadmap

  1. Spatial Models and Methods

    1. GIS

    2. R/Leaflet

    3. Python Map

    4. Remote Sensing

Data Science Roadmap

  1. Machine Learning

    1. Supervised

    2. Unsupervised

    3. Deep Learning

    4. Neural Networks

Data Science Roadmap

  1. NLP/Text Mining

    1. Corpus

    2. Text Analysis

    3. Sentiment Analysis

    4. Natural Language Processing

Millions of open data-related jobs are begging not just business, computer science and mathematics programs but also Social Science seeking candidates who can deal with complicated big data and statistical models.  

 

Jobs, jobs and jobs

McKinsey Global Institute. 2016. THE AGE OF ANALYTICS: COMPETING IN A DATA-DRIVEN WORLD https://www.mckinsey.com/~/media/mckinsey/industries/public%20and%20social%20sector/our%20insights/the%20age%20of%20analytics%20competing%20in%20a%20data%20driven%20world/mgi-the-age-of-analytics-full-report.pdf609/1088413444-MIT.pdf

McKinsey Global Institute. 2016. THE AGE OF ANALYTICS: COMPETING IN A DATA-DRIVEN WORLD https://www.mckinsey.com/~/media/mckinsey/industries/public%20and%20social%20sector/our%20insights/the%20age%20of%20analytics%20competing%20in%20a%20data%20driven%20world/mgi-the-age-of-analytics-full-report.pdf609/1088413444-MIT.pdf

McKinsey Global Institute. 2016. THE AGE OF ANALYTICS: COMPETING IN A DATA-DRIVEN WORLD https://www.mckinsey.com/~/media/mckinsey/industries/public%20and%20social%20sector/our%20insights/the%20age%20of%20analytics%20competing%20in%20a%20data%20driven%20world/mgi-the-age-of-analytics-full-report.pdf609/1088413444-MIT.pdf

To students in India, your comparative and absolute advantages:

  • Global Value Chain Restructuring

  • India's new role in the Indo-Pacific

  • "Democracy" club membership

  • Global partnership with Taiwan, Singapore, US and Europe

 

Java: D3 Library

Sentiment Analysis

Thank you!

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Artificial Intelligence: Global Opportunities

By Karl Ho

Artificial Intelligence: Global Opportunities

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