Learn the major milestones of data science history by discussing the origin of data science, a timeline of major breakthroughs in data science, and how data science is useful and found in every aspect of our lives.
- [Voiceover] The origin of data science coincides with the wide adoption of computers. The discipline of statistics existed well before computer science, but computers empowered statisticians to solve a wide variety of practical problems with real life implications, since heavy number crunching and massive storage of data became feasible due to the emergence of modern computing technologies.
The invention of database management systems in the 1960s, and relational database management systems in the 1970s, accelerated the pace of this marriage between statistics and computer science. In the late 1980s, terms such as knowledge discovery and data mining started being used widely. In the early 1990s, database industry practitioners started noticing the explosion of business data in the form of big data.
The official start of using the phrase big data can be traced back to an article published in the ACM Digital Library in 1997. In the late 1990s, the phrase data science first appeared to inspire researchers and professionals to harness the predictive power of data by effectively analyzing them and producing useful intelligence.
At about the same time, the word statistician began to be used interchangeably with the term data scientist. In the mid 2000s, the word analytics was adopted by data scientists to emphasize the fact that an increasing number of companies started to heavily rely on the statistical and quantitative analysis of data, as well as predictive modelling to make informed decisions so that they can compete better with other businesses.
As you can see, the history of data science is that of endless scientific and technological innovations to cope with newly emerging challenges, as we move into the era of information age.
Jungwoo Ryoo is a professor of information science and technology at Penn State. Here he reviews the history of data science and analytics, explores which markets are using big data the most, and reveals the five main skills areas: data mining, machine learning, natural language processing (NLP), statistics, and visualization. This leads to a discussion of the five biggest career opportunities, the four leading industry-recognized certifications available, and the most exciting emerging technologies. Along the way, Jungwoo discusses the importance of ethics and professional development, and provides pointers to online resources for learning more.
- A history of data science
- Why analytics is important
- How data science is used in social media, climate research, and more
- Data science skills
- Data science certifications
- The future of big data