13 Matching Annotations
  1. Jan 2021
    1. The term that seemed to fit best was data scientist:  those who use both data and science to create something new.

      Even though data science might have a very fluid definition, it is know that data scientists strive to find what cannot be seen in raw data, putting in the work to see what the competition cannot is a very valuable asset

    2. that it’s a career path rather than a category of activities. In my conversations with people, it seems that people who consider themselves Data Scientists typically have eclectic career paths, that might in some ways seem not to make much sense.”

      Data science can be a tricky field because it can be used for so many different things such as sports, business, healthcare, etc. which makes it a very flexible option as well.

    3. They also seem to start by looking at what the data can tell them, and then picking interesting threads to follow, rather than the traditional scientist's approach of choosing the problem first and then finding data to shed light on it.”

      Data scientists are now much more comfortable using huge amounts of data and they look for anything useful they can use, as opposed to always finding an issue then looking for results in the data.

    4. I present the Data Science Venn Diagram… hacking skills, math and stats knowledge, and substantive expertise.

      An understanding of advanced statistics is a must as the methodologies get more complex and new methods are being created such as machine learning

    5. They can tackle all aspects of a problem, from initial data collection and data conditioning to drawing conclusions. They can think outside the box to come up with new ways to view the problem, or to work with very broadly defined problems: ‘here's a lot of data, what can you make from it?’"

      Data scientists are not just hired to mine and run the data, they are also making the decisions that the data has directed them to. They can do this by making data visuals to show their colleagues that will lead to the best decisions for the company.

    6. a new kind of professional has emerged, the data scientist, who combines the skills of software programmer, statistician and storyteller/artist to extract the nuggets of gold hidden under mountains of data.”

      A job like this did not even exist a few years ago, now data scientists are being tasked with business's most important decisions and are finding ways to use data that we have never seen in the years before. This is a good reference to finding "gold" which are the hidden actions within the data.

    7. The ability to take data—to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it—that’s going to be a hugely important skill in the next decades…

      A decade later, this is a very true statement, there are so many companies out there who need people to understand massive amount of data and be able to process it and find the best business decisions for the company.

    8. “Different from natural science and social science, Dataology and Data Science takes data in cyberspace as its research object. It is a new science.”

      Computer engineering and coding has been rapidly growing at this point and the internet is becoming more available to everyone which helps keep the innovation driving and is a large reason in the expansion of data science.

    9. If our goal as a field is to use data to solve problems, then we need to move away from exclusive dependence on data models and adopt a more diverse set of tools.”

      Very important step in the growth of data science, many people were getting stuck in their ways of using data how they have for years, this helps provoke innovation.

    10. Data mining is the application of specific algorithms for extracting patterns from data… the additional steps in the KDD process, such as data preparation, data selection, data cleaning, incorporation of appropriate prior knowledge, and proper interpretation of the results of mining

      There are lots of different names people think of when discussing data science, sentence does a good job of describing the different methodologies that are used within data science and there is a specific order in which everything is done.

    11. “Companies are collecting mountains of information about you, crunching it to predict how likely you are to buy a product, and using that knowledge to craft a marketing message precisely calibrated to get you to do so…

      This has been an issue since the 90s and it has recently been a hot topic of businesses having extensive information on its customers, this has raised ethical concerns on what information people are comfortable giving to companies.

    12. “[Data is] a representation of facts or ideas in a formalized manner capable of being communicated or manipulated by some process.

      This is the fundamental purpose of data analysis is to be able to represent your data visually which can help businesses or people make important decisions while having the most information at their disposal.

    13. How vital and how important… is the rise of the stored-program electronic computer?

      One of the most important inventions in history, has been growing exponentially, data storage, program integration, and data analysis is still growing today.