What's Data Science?

Disciplines on the cutting edge of technology seldom have concise or consistent definitions. If we say that data science is what data scientists do, you might accuse us of being whimsical. Nonetheless, that definition has merit. Suffice it to say, data science is a branch of information technology that deals with massive data sets also known as big data. Data scientists develop algorithms for sorting, organizing, making inferences from, visualizing, and working with amounts of data that would otherwise be too large to make sense of.

 

Central to this question is the role of big data and the data scientist. As a profession, data science takes a cross-disciplinary approach that involves computer science and programming. Perhaps more importantly, it involves the role of the mathematical discipline of statistics.

 

In order to make inferences or draw conclusions about the massive sets of data we call big data, statistics have to play a vital role. Consider a very basic question. Should an email that’s been sent to you go to your main mailbox or the spam folder? Let’s assume that the sorting algorithm doesn’t know anything about the sender. You get an email from an individual who is claiming that they are a Nigerian prince who has access to a vast fortune that he, for whatever reason, cannot access. How can an algorithm tell that this message, or the messages for cheap effective Cialis, or a Russian woman who is looking for the man of her dreams are all likely spam?

 

Data Science Makes Predictions

 

One of the key features that make data scientists among the most sought-after professionals in today’s business climate, is that they can train computers to make predictions based on the data that is provided them. In this case, the massive amount of data doesn’t confuse the issue. It actually makes the predictive models more accurate.

 

For instance, if a Nigerian Prince email happens to find its way into your main inbox, you can mark that email as spam. The system records this information, and those that receive a similar email will have it sent to their spam-box instead of their inbox. In other words, it predicts that a message that one person has marked as spam is more likely than not going to be marked by another person as spam.

 

On the other hand, it often happens that emails that should have made it into your inbox are sent to your spam-box. If you want this email, you can send it to your inbox and your email client will know next time that emails received from that address do not constitute spam.

 

Data Science is Primarily Used by Businesses

 

The main arena in which data science has garnered the most attention is business. In order to remain successful, businesses need to be one step ahead of their competitors. For instance, Ford is renowned for making the best trucks on the market today. But Ford is also a smaller company, much smaller than Volkswagen, Toyota, and GMC. This means that Ford, which offers a full lineup of SUVs, trucks, and sedans, needs to make sure that they aren’t overproducing the consumer demand for certain vehicles.

 

For instance, midsize and compact trucks were not huge sellers in the US market for the past 5 years. Ford was able to recognize that there was a decline in sales and discontinue their midsize model for the US market.

 

The past couple of years, however, have shown that more people are buying smaller trucks as an alternative to family sedans and SUVs. Ford recognized this and decided to bring back their midsize truck for the US market.

 

In the process, they saved themselves millions of dollars by not producing a truck they predicted would not sell well. They will likely make millions more when they introduce the Ford Ranger into the US market again. This has to do with being able to predict market signals.

 

Marketing to the Customer

 

Another way in which businesses use data science is by determining who their customer base is. Consider firstly that the internet is kind of an impersonal place. A customer goes to a website to purchase an object that they value, but the retailer doesn’t really see that person face to face. However, the website may be able to make certain inferences about the person based on their browsing history.

 

In fact, one of the most important developments over the past few decades is targeted consumer advertising that is based on your interests.

For instance, let’s say that you really enjoy bowling. Big data has a gleaned a great deal of information about individuals and their likes. As it turns out, individuals that like bowling have a tendency to like golf as well. In fact, many bowlers bowl in the winter and golf throughout the summer.

 

Websites that make the majority of their money from advertising may deduce from your recent purchase of a bowling ball that you might also be interested in deals on golf clubs. This helps retailers reach out to their potential customers while keeping customers in the loop about what deals that they would be interested in.

 

Data Science in Other Fields

 

Businesses aren’t the only ones who have benefited from data science. In fact, data science has something to offer almost every industry you can imagine. Today, it’s being applied in areas like science, economics, social sciences, and the law.

 

Legal and Data Science

 

Consider legal research on a complex topic such as jury verdicts in medical malpractice lawsuits over the past five years. While searchable databases provide enough information to draw inferences, the amount of time it would take and the expense of taking it might not always be practical to either lawyers or clients. BriefMine, which is a San Francisco-based company, is applying data science to the legal profession to make the process more efficient.

 

Social Sciences and Data Science

 

Social scientists now have access to more information than they’ve ever had before thanks to social media sites like Instagram, Facebook, and Twitter. With the help of data science, it’s much easier to read trends from these sites that would take decades of time to collect otherwise. Consider all the tweets that are tweeted in a single day. That information can be used to draw conclusions from.

 

Economics and Data Science

 

The example of Ford considering market trends is an example of a company that uses data to make predictions. But we have access to much more information than that. Economics is one of the key areas that is being revolutionized through the use of data science. Information like how inflation affects products in specific industries is suddenly accessible to us. The speed at which we can draw conclusions about that information is nearly instantaneous.

 

Data Science Chaos

 

They don’t call this the information age without a good reason. In fact, most of the information that we call ”big data” just sits in storage waiting for someone to do something with it. Data scientists have a difficult job ahead of them. Not only are they responsible for organizing and sorting this information, they also have to come up with ways to interpret and draw conclusions from it.

 

The science of big data now stands at the forefront of an information revolution. Data scientists are no longer merely responsible for developing ways in which to organize, classify, interpret, and make predictions given a massive data set. They are also responsible for training computers how to do this themselves.

 

New information can be processed and made use of as soon as it becomes available.The predictive models become more accurate as they gain more information. No longer will humans have to rebuild theories from the ground up as we attempt to incorporate new information. No longer will humans be overwhelmed by quantity in data sets. Data science frees up human minds to do what they do best: innovate.

Search R-Algo Engineering Big Data’s blog for data science articles.

R-ALGO Engineering Big Data provides articles on Artificial Intelligence, Algorithms, Big Data, Data Science, and Machine Learning. Also, R-ALGO Engineering Big Data provides R Tutorials on how to implement Machine Learning Algorithms with provided datasets.

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