Data Science Case Study
Who has not heard about data science lately? It has got numerous helpful applications in such fields as education, media, healthcare, banking, transportation, and retail. It is sound that students usually get the tasks of a data science case study in various industries. Studying this science has got one of the recent trends and it stands to reason. Its possibilities are huge: it enables to analyze financial frauds, do business in the area of e-commerce or personalize recommendations. A case study of data science is frequently reviewed as a way to challenge oneself or expand one’s knowledge in a certain field, change a common perspective, develop an individual approach, and contribute to the success of one’s company taking into account particular experiences of other organizations.
Could you answer the question: What is a case study? It is an investigation of a particular unit, group, community, or individual done systematically and intensively to analyze the information associated with a number of variables. The most convenient method of revealing the sense of this science and explaining a certain technology is through a case study.
How to Write Data Science Case Study
- Collect figures and information required for a study
First of all, you have to gain an insight into the problem. Only after you feel that you understand the core issues, you can start gathering details to make everything clear about the set needed. The process of searching for relevant information is a time-consuming process. You can use questionnaires, surveys, and other traditional techniques for collecting information, but it is also possible to analyze online platforms and find relevant datasets of perfect quality on the internet.
- Identify the key stages of research
A case study in data science implies taking a number of stages for processing of the gathered facts, such as specifying the problem statement, collecting data for problem solving, exploring them, pre-processing them, and analyzing them.
- Solve a data science case study
Addressing problem in a case study needs a systematic approach and well-thought organization of the process. Typically, handling a case study, a researcher takes three steps, presented below:
- Specifying the problem statement and required strategy
- Making assumptions on the basis of collecting and pre-processing of the gathered information
- Developing predictions and using evaluation metrics to confirm the solutions
Data Science Case Study Example
Only after you get engaged into an intensive process of addressing and analyzing the set problem statement, you can state that you know how to handle the task to solve a data science case study. Have a look at a bright example below to gain an understanding of how it is possible to apply the outputs of data science in different industries.
The internet of things (IoT) is one of the latest technologies in the consumer, infrastructure, and business sectors. Data that come from the IoT are beneficial both to the current and future generations. Urban mobility has been a major concern for many civilizations across the globe. Accordingly, the internet of things promises to solve this problem, especially in the nearest future. The need to develop smart cities has led to the development of various types of IoT, including Parkofon, data of which are important when it comes to smart mobility. Thus, the internet of things, particularly, the Parkofon technology, plays a significant role in modern society, particularly focusing on its benefits and high applicability in different smart cities.
The internet of things, which refers to a collection of network devices that ensure connectivity, has been on the rise in many parts of the world. Xia aver that it consists of both tangible and intangible system devices that enhance smart operations. The applicability of the internet of things is becoming increasingly predominant in the infrastructure sector. Both developed and developing states are developing smart cities that require the use of smart applications, such as Parkofon. Accordingly, it is worth exploring benefits associated with the use of the internet of things, particularly the Parkofon technology.
Given the rapid development of cities across the globe, it is important to examine how the internet of things has revolutionized urban mobility. Data collected from it are essential when it comes to coordinating operations of a smart city. For instance, Parkofon has the potential to enhance efficiency through the reduction of city congestion and the provision of convenient parking. Data collected from various metered devices and vehicles allow for the effective management of urban parking. It results in the real-time availability of parking space for vehicles that come to a smart city.
Despite the convenience that comes from the use of Parkofon in urban mobility, the app has substantial challenges associated with the adoption of new technologies. The major concern is privacy and security, considering the availability of one’s location and details in the system. The autonomy of the latter can attract unscrupulous players and manipulators. Besides, the storage of a large amount of data collected by Parkofon sensors may be a challenge. The internet of things requires large and secure storage capacity for various data types. Despite the adoption barriers, it is an efficient technology for smart urban mobility.
In summary, despite challenges, the IoT plays an important role in urban mobility, considering the growing need for smart cities. Parkofon is one of the IoT technologies that enhance urban mobility by easing urban congestion and providing convenience for smart parking. Although data collected from the internet of things help in the synchronization and coordination of infrastructural activities, many risks hinder the spread and adoption of the technology.
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