What business isn’t looking to cut down on costs? Heck, what person in this world isn’t looking to cut down on costs? There is nothing that anyone or any business loves more than to save money. Data science is a process that is used to analyze a large amount of data for a company or business in order to find patterns within that data. Once those patterns have been discovered a business can use those numbers to cut back on costs or find a more efficient way of doing something. Data science is beneficial for a wide variety of businesses and companies, from banking to insurance, to manufacturing and media, and even air traffic control. Data science can help to not only improve efficiency but also to create safer work environments and safer environments for everyone. If you are curious about how you can save your business some money, or are wondering how to improve your business’ efficiency, or seeking ways to make your work environment safer for your employees, look no further than Mosaic Data Science.
Mosaic Data Science has more than a decade of data science consulting experience and many of their data scientists have more than three decades of experience in this field. The highly experienced team at Mosaic Data Science loves nothing more than a challenge and have taken on some of the most complex and difficult data pools that can be found. Mosaic Data Science’s unique approach to data science consulting and analysis seeks to improve the operational efficiency and safety of a wide variety of businesses, companies, and organizations. One of their highest achievements has been through working with commercial, civilian, and military air control to optimize safety and efficiency in order to save the industry billions of dollars every year. In addition to working toward air traffic safety and efficiency, Mosaic Data Science also has a lot of experience working with industries such as banking, manufacturing, insurance, medicine, media, and telecommunications. Their dedicated team of data scientists has many years of experience as well as credentials from the top rated schools for science and technology.
Mosaic Data Science’s consulting and analyzing projects have resulted in millions of dollars in annual cost reductions for many of their clients. Their approach to data science analysis is to look at the problem or challenge from a variety of different angles. They start by meeting with their client to figure out what it is that the client hopes for the data science project to achieve and then they will look at data sets from a number of different stand points in order to figure out what really is going on. They consider statistics, software engineering, business intelligence, cognitive intelligence, human factors, organizational behavior, operations research, and machine learning, among other things in order to ensure that no relevant information will slip through the cracks. After factoring in all of these things Mosaic Data will choose an appropriate method to determine the most reliable and comprehensive solution that is possible.
My brother is entering his last year of college and it seems like he is on a great path to success. Many students have no idea what they want to do when they enter college, and then there are others like my brother who have a plan and see it through to the end. Needless to say I was one of those unsure students, so it amazes me even more when I see people know exactly what they want in life and they pursue it until they do. My brother has always been interested in math and science; he excelled in AP physics and calculus even in high school. When he was accepted into a reputable science college we were all impressed but certainly not surprised.
As he is entering his last year, my brother has explained to the family that he will be pursuing a career in data science. None of us had a clue what he was talking about but we supported him regardless. Since that announcement I have tried my best to understand what data scientists do and how they do it, in simpler terms of course. What I have found is that these types of scientists pool their knowledge of mathematics, statistics, software engineering, civil engineering, and other similar sub-fields into one discipline that mainly consults other companies on what they can and should do with their data.
Over time any one company can collect and compile a lot of data. And I mean, I don’t think you and I could understand just how much data can build up so quickly. This is why companies hire data scientists; because they can’t handle all of the data themselves. As I understand it, some companies can’t even store the data they are collecting so they need someone to help with that. And for those that have adequate data storage but have no idea what to with it from there, data scientists can also help.
A lot can be learned from large data sets if you know what to look for. Hypothetically, my brother will some day tackles these large chunks of information and turn them into something organized and something that tells a story about that company. A good example of this type of data manipulation and learning is Google searches. When Google started out they had no idea what people would be searching for so they just left it up to the masses. But over time Google began reviewing and analyzing the data that they collected on searches, and now the searches that we do are customized and we don’t even realize it. In the beginning, when you searched for “watch repair” on Google you might find some newspaper ads, articles, or eventually businesses with that phrase in their service. Now, before you even finish typing watch repair in the search bar it is filled in for you. That is what data science management does.
Data scientists are everywhere, in every sector, so hopefully my baby brother finds his dream job soon!
What is the difference between statistics and data science? In a keynote address delivered during the 2013 Joint Statistical Meetings of the American Statistical Association, Nate Silver stated that the term data scientist is merely a “more sexy” term for statistician considering that statistics is a branch of science. Thus, it comes as no surprise then that those in academic circles see no distinction between the two.
And yet, for those who practice data science consulting, there is in a fact some degree of difference that makes the two separate disciplines. First, let us look at the data that each field deals with. Statistics is generally the method used in analyzing and interpreting data obtained in research studies through surveys or experiments. In statistics, the data is obtained through a sample of a population. For example, if you want to know if the people are satisfied with President Obama’s policies, you don’t have to go out and interview everyone in the U.S. You simply need to get a certain percentage of the population and apply equations of formulas.
Data science, on the other hand, has a different approach. It often uses figures obtained through data mining. Unlike the statistical approach, data mining often uses selective information to prove a point. In addition, it uses big data in order to find patterns and then utilizes said patterns to its advantages. Further, through this process, you will be able to find not just different trends but even associations as well.
For example, a grocery store finds out that prior to the Superbowl, men who bought beer also bought ribs for barbeque. Using statistics, you can conclude that during the Superbowl, as the sale of beer increases so does the sale of ribs. Data science goes one step further. By recognizing this pattern and association, grocery stores across the country will know that when the Superbowl is around the corner, they need to restock not just the beer but the ribs as well. Further, they can exploit this association by putting the beers near the ribs.
From this simple example, we can see that while statistics can help us expand our knowledge through research, data science can in fact help us become more active in applying the information obtained. Does this mean then that statistics no longer has a use when it comes to large amounts of data? The answer is obviously no. Statistics, being a science in itself, will always be a part of the larger picture. What this implies is that while a statistician can increase his expertise in his or her field, the statistician can be a significant member of any data science consulting team. This is because unlike statistics, data science is in reality a multi-disciplinary field.
Thus while the two may refer to the same thing in some cases, it is just as important to be able to know the difference as well. Regardless of where you belong, be it statistics or data science, what is important is that both fields endeavour to make data more meaningful.
Whenever you learn anything, it is important that you retain the information, process it, and place it in its rightful place in order to help you continue to process and understand information of a similar nature. It does no one any good to learn about something and then completely forget the ins and outs of it, or worse, to forget how to process that data and have to relearn the entire process. Indeed, placing information in a framework that is easy to understand and to retain is critical for moving forward with information in any way. It makes no sense to learn something and then immediately forget how to understand or use the information. Why learn something only to leave it behind instead of utilizing that information to its fullest capacity? And how is one to understand massive amounts of data without having this framework for understanding it? Having that basic mechanism for processing data is especially important when one is dealing with large data sets or with information that has so many aspects that it becomes difficult to understand it without a framework or way to process it. Indeed, some information becomes useless without this framework, and it is especially important to use said framework as effectively as possible to understand the information at hand.
All this being said, the field of data science is one which seeks to identify these frameworks and help others to utilize them to the fullest capacity in order to understand and utilize information to the fullest. As we have discussed, in many cases data is all but indecipherable without these mechanisms for understanding them. There is so much information available these days, making sure that you have a way to understand and to process that data efficiently and effectively makes a huge difference in setting you as a business person ahead of the curve. This is why it is important to have an analytics consulting firm by your side when you begin to work through the massive amounts of data that is available to you so that you can use it in the way that makes the most sense for you.
One aspect of data science that is particularly important and useful is data mining. While this term is often used out of context to simply describe large scale attempts to understand and process massive amounts of information, it really is much more specific and therefore much more powerful than that. Data mining is just one way that data science can be used to manufacture a framework through which to process information. It is the analytics step in the process of “Knowledge Discovery in Databases” or KDD, which is an interdisciplinary subfield in computer science. Which such massive data sets at hand, being able to process these varying data sets and to glean information from their intersecting areas is invaluable, and such information can allow an individual or a business to come up with important information not only in the present but also as a framework for understanding future information.
While there may be a very small portion of the population that fully understand data science, it doesn’t have to be intimidating or confusing. The word science is in there because it is in fact classified as a science, it is the science of understanding data. Or of getting good information from data. That is really all that data science is about. There is definitely an art to getting knowledge or information from data, and so those people who are trained and educated in doing that work are called data scientists. Many different industries and companies are using data science as a new method to get as much information as they can from their data. The world is becoming much more data driven, and it is important to be able to not only capture data and store it, but also to analyze it and get as much practical and useful information from that data for decision making and other important business uses and business cases. The new and expanding field of data science includes things like probability models and pattern recognition, but it also incorporates much more specific scientific fields of study such as machine learning and uncertainty modeling. Data science uses those scientists who are trained experts in maths and sciences as well as complex calculus. While big data uses data scientists in order to extrapolate as much information as possible from large pools of data, it is also becoming much more common for smaller data sets to also use this method and these principles to gain helpful and monetized information from their data sets as well.
Because there are so many different areas of the sciences that are incorporated in data science, it is important to have a larger team of scientists who are working on the data problems, and they must function as a well versed and cohesive unit and team, rather than just doing the work on their own. Data scientists come from all science and math backgrounds, and those who are experts in their fields are the likely candidates for forming a team of data scientists. This is why data science isn’t necessarily practiced at every company and corporation, because it can be difficult to get the qualified scientists to work together. This is generally something that a company will do a contract with a consulting firm on, so that they are able to out source the actual data science work, and then they are able to utilize the results as they are explained to them. So the practice doesn’t have to be complicated and confusing, but it also cannot be done by the general practitioner, it must be completed by the scientists who is fully experienced and trained. The idea behind it can be fully understood by all however, which is the result of simply understanding that data can be used for our benefit, and we can look at the data and find patterns and other information that will help us inmaking better policies and laws, based on the data.