Fundamentals of Data Science: History and Future of Data Science


Capability increased due to decreasing cost of data storage, cpu, and bandwidth.

Demand increased due to large amount of data being generated.


  • Demand for talent
    • “The future is so bright, Ada would need shades” – Joseph Burton
  • Emerging subdisciplines
    • Machine Learning Engineer
    • Data Visualization Engineer
    • Data Journalist
    • Big Data Engineer
  • Continued reduction in technical learning curve
    • Automation around machine learning and data wrangling
  • Ethics
    • risk of discrimination in “Black Box” models
    • machine learning can be used for bad as well as for good

Published by

Ednalyn C. De Dios

I’ve always been enamored with code and I love data science because of its inherent power to solve real problems. Having grown up in the Philippines, served in the United States Navy, and worked in the nonprofit sector, I am driven to make the world a better place. I have started and participated in numerous campaigns that aim to reduce domestic violence and child abuse in the community.

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