The Future of Data Science

In summary, the future of data science will involve an increasing amount of data being collected and shared, with the challenge being to find useful information among the vast amount of noise. Privacy concerns will continue to be a major factor, but controlling them will be complicated and may take a long time to see significant results. The potential benefits of data science in areas such as healthcare are promising, but there is also the risk of data being used for profit and political gain. As data continues to grow, the need for effective data mining techniques and regulations will become even more crucial.
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What do you think the future of data science would be, given the increasing privacy concern of collecting and sharing users data?
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There is no such thing as privacy anymore.
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  • #3
What do you mean? Are people no longer entitled to have privacy in their lives, or it is because of the lack of legislations to control the collection and use of data by corporations?
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Future of data science?

My guess is, that the amount of data available will just doubled periodically, while the amount of useful data will remain ~ constant. The future will be about finding the letter among the endless noise. This will introduce a new kind of privacy (privacy of the nobody, as I call it - even if everything about us will be available somewhere, it would be just noise in the big picture). Also, different kind of certificates and online libraries will be invented to keep record of the real and noise data, but some time later it'll be just a new addition to the noise... The method to handle them on multiple levels will be the new booming area of the 'data science'.

At the end the so called 'data science' will be reduced to an endless fight against the monster we invented and brought onto ourselves.
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  • #5
EngWiPy said:
What do you think the future of data science would be, given the increasing privacy concern of collecting and sharing users data?

Privacy concerns about collecting and sharing users data is one parameter of the impact and implications of the job done through data science and also for determining its future. It is an extremely important one but it can by no means be totally controlled by data science itself. Data are stored and transferred in huge amounts over the Internet, so there is a multitude of potential risks based on some sort of malicious behavior that lie outside data science itself. Then, there is the human factor inside data science and whatever this (potentially) implies. On top of that, the term "data science" is not only somewhat vague by itself regarding what exactly encompasses but it also has become fashionable, so outside academia and research i.e. at business level, has taken various forms that do not absolutely meet the purposes of data science at best. So, in my opinion, there cannot be some very fast and drastic way to control and solve the issues of privacy. Targeted laws and regulations can diminish the risks for privacy but the problem is quite complicated and it may take a long time to exhibit some important results.

On the other hand, the really useful things that are and will be accomplished through data science is also a very important parameter that determines its future. One very important example is health. Another one is the progress of science itself by using not only scientific data but also data for science.

So, I think that the future will hold a lot of useful accomplishments for data science regarding human life. Any negative implications at various levels is something that we, humans, have to take care of.
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  • #6
EngWiPy said:
What do you mean? Are people no longer entitled to have privacy in their lives, or it is because of the lack of legislations to control the collection and use of data by corporations?
The stories that you see on the news only scratch the surface of what's been going on for years and the value of that data exceeds undefined 'privacy' concerns.

First, you have to clearly define what is meant by privacy. Is it just information that can specifically identify you? Should anonymised data be included as being entitled to privacy? If so, say goodbye to traffic information on Google maps. But if anonymised data is OK, then you have the data aggregators who can re-assemble who you are from it. How do you legislate the hundreds of variables that go into just one data aggregation algorithm and then apply that to the dozens of companies that build these types of products?

And, if that isn't fun enough, your privacy decisions can affect the privacy of others. For example, the notorious Golden State Killer was caught using DNA provided to a public database by a distant relative. How does someone protect theirself from that kind of privacy invasion?
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  • #7
The are some areas where data can obviously help, like in medical health care. In businesses, data is used to multiply profit. The promise of Data Science makes businesses collect more and more data, which, as we saw in the Cambridge Analytica case with Facebook, may stir a political reaction that could limit the benefit and use of data science at the end.
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A I will need to become better at mining the large amount of data to give the end user the correct result. The amount of data will become staggering. More staggering then now, that is.

1. What is the role of data science in the future?

Data science will continue to play a crucial role in shaping the future of various industries. It will be used to forecast trends, make data-driven decisions, and identify patterns and insights that can lead to innovation and growth.

2. What are the emerging technologies in data science?

Some emerging technologies in data science include artificial intelligence, machine learning, natural language processing, and blockchain. These technologies are constantly evolving and have the potential to revolutionize the way we collect, analyze, and use data.

3. How will data privacy and security be addressed in the future of data science?

Data privacy and security will remain a top priority in the future of data science. With the increasing amount of data being collected, organizations will need to implement stricter protocols and regulations to protect sensitive information. This may include using advanced encryption methods and implementing data anonymization techniques.

4. What are the challenges of implementing data science in businesses?

Some challenges of implementing data science in businesses include finding skilled data scientists, managing and processing vast amounts of data, and integrating data science into existing systems and processes. Additionally, there may be ethical and legal considerations to take into account when using data science in decision-making.

5. How can data science be used to address global issues?

Data science can be used to address global issues such as climate change, poverty, and healthcare. By analyzing large datasets, patterns and trends can be identified, which can help inform policies and decision-making to address these issues. Additionally, data science can be used to optimize resource allocation and improve efficiency in addressing these global issues.

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