What Are The Advantages And Disadvantages Of Data Science

Data analytics includes the process of the analysis after collecting, of data to determine patterns as well as all types of information. Businesses profit from this data analysis and how it has been classified.

My degree in Mathematics and Statistics has helped give me an idea of how data is important and vital to businesses. During my course I have completed modules such as operational research, business simulation and data and information retrieval. These modules have given a great understanding of the importance of data within business and has inspired me to pursue a postgraduate degree in Business Intelligence. One programming language that I have studied is SQL. It was interesting to learn about SQL and how it works. I learnt how to retrieve data from a database and how to perform other tasks such as updating data and selecting the data I wanted. This helped me to understand a lot about SQL and how databases worked. The modules I have completed have giving me an insight into data analytics and have made me want to work in this field.

Data exists everywhere around us. From your heart beats per minute, the current room temperature, or the number of tweets being posted about Miley Cyrus, nearly everything can be considered a data point. It is now being collected at an alarming rate and there are now opportunities to use that data in ways that were previously unimaginable. As technology advances data will only continue to grow, regardless of field of study, occupation or geography. As a user and firm believer of Data Science, I am excited by the possibilities of using that data and the trajectory of the field as a whole. After discovering the power of data I have developed a passion and appreciation for it, and realize that there is a lot left for me learn. Pursuing a graduate degree in Data Science is the stepping stone I need to take my career to the next level and compete with the best in the exciting, rapidly evolving and growing field.

Data analysis is a procedure of inspecting, cleaning, transforming, and modelling data with the goal of highlighting useful information, suggesting conclusions, and supporting decision making. There are multiple facts and approaches, encompassing diverse techniques under a variety of names, in different business, science, and social science domains in data analysis. For data analysis we have to mine the data first for our purpose such that the data we can handle easily. Basically for data analysis our first thing to do our planning, how we are going to collect the data, our going data going to make sense or not, actually data will be meaningful for our object, after

Data are raw facts that have no meaning until they are processed and organized to identify patterns and relationships between the data elements.

The word "data" may seem to be facts or numbers collected for future references and analysis of a subject that is being carefully examined, but data are pretty much used in all aspects of our lives. With these important facts and statistics, we can help an individual or a company reduce a significant amount of cost. These data can help companies such as Apple realized future growth potentials and where it can better maximize its income.

(Cárdenas-Navia, Fitzgerald pg. 27) The authors say that this new field has paved the way for a new kind of liberal arts major who is equipped to thrive in the increasingly technological world. (Cárdenas-Navia, Fitzgerald pg. 28) The authors also highlight that data scientist programs are predominantly located in the computer science departments and the schools of engineering. (Cárdenas-Navia, Fitzgerald pg. 28) This limits the field to only STEM students, who already have experience and are familiar with physics, mathematics, computer science, and basic engineering principles. (Cárdenas-Navia, Fitzgerald pg. 28) As a result, this field has a reduced diversity of data-enabled professionals. (Cárdenas-Navia, Fitzgerald pg. 28) The authors mention in this article that BHEF has been working with different colleges and organizations to introduce liberal arts majors to data science, and have also challenged undergraduate institutions to consider including data science and analytic coursework along with hands-on experience to their liberal arts curriculum in the process. (Cárdenas-Navia, Fitzgerald pg.

Foster Provost and Tom Fawcett. Big Data, Data Science and its Relationship to Big Data and Data-Driven Decision making,” http://online.liebertpub.com/doi/full/10.1089/big.2013.1508

Big data technology is used to help produce useful information out of the massive amount of data collected from retail stores. According to Dylan Love,

Data mining is a very important component in today’s big data [22, 23]. Data mining is essential for everyone from large businesses to government organizations. It helps to identify trends, patterns and make predictions by exploring, comparing, researching and analyzing data.

The promise of data-driven decision-making is now being recognized broadly, and there is growing enthusiasm for the notion of ``Big Data.’’ While the promise of Big Data is real -- for example, it is estimated that Google alone contributed 54 billion dollars to the US economy in 2009 -- there is currently a wide gap between its potential and its realization.

For analysis, advanced statistical tools are used and the experimenter can draw necessary conclusions and inferences. In business sectors, they have a huge amount of scattered data in terms of profits, loss, demand, supply, sales and production. The industrial, insurance, agricultural, banking, information technology, food industry, telecommunication, retail, utilities, travel, pharmacy and many more have challenges to manage their data. As the coin always has two sides, there are both advantages and a few disadvantages of data analysis. The key advantages of data analysis are- The organizations can immediately come across errors, the service provided after optimizing the system using data analysis reduces the chances of failure, saves time and leads to advancement. It is also used to compare strategies between two companies so as to reduce the prices and gaining attention of target customers, ultimately leading to maximization of profit and minimization of cost(as done in Game Theory). However, big data analysis sometimes becomes more tedious and disadvantageous because it uses software Hadoop which requires special provisions in the computers. For now use of Hadoop for real-time analysis is not available. The manner in which the data is collected and the decision making view can vary from one person to another. Here, the quality of data gets affected and leaves the data insufficient or inefficient. In order to tackle this problem, the researcher must be professional, well experienced and should have deep knowledge about the characteristic under study. Also, we need to update data from time to time so as to avoid the changes in trend caused by the past data especially, for the rapidly growing

Currently the Office of Institutional Effectiveness with the help of the Office of Instruction and Student Services, has launched a campaign to increase the understanding and use of data in the decision making process. To become a culture that relies on data as an integral part of the decision making as well as measuring the effectiveness of our policies and practices, we must first understand the various types of data and how they can be used by everyone.

As coined in an article in the St. Louis Post-Dispatch by Aisha Sultan, “Data is the new world currency.” Data mining is the process of analyzing data from different perspectives and then summarizing it into useful information. In essence is it applying all different types of what if scenarios on large swaths of data to get possible results to aid in better decision making. This sort of decision making isn’t something new, it’s the technology aiding the decision making that is new. This has reduced the amount of time it takes in the decision making process and given the

Data, Data everywhere. It is a precious thing that will last longer than the systems. In this challenging world, there is a high demand to work efficiently without risk of losing any tiny information which might be very important in future. Hence there is need to create large volumes of data which needs to be stored and explored for future analysis. I am always fascinated to know how this large amount of data is handled, stored in databases and manipulated to extract useful information. A raw data is like an unpolished diamond, its value is known only after it is polished. Similarly, the value of data is understood only after a proper meaning is brought out of it, this is known as Data Mining.