Q This research acts as a contributing factor in predictive analysis through data science, and determine an individual Home, - DRAFT A RESEARCH PLAN BASED ON A GENERAL OPERATIONAL MODEL Need for the study “Data science is usually a mix of three things: quantitative analysis (necessary so as to understand your data), programming (so you can process and analyze your data effectively), and also storytelling (this is to help others understand what the data means and interpret it appropriately).” ?Edwin Chen, Data Scientist Over the last three to four decades the world has undergone rapid changes in scientific methods, algorithms, processes and systems that are used to extract knowledge or information from data. In today’s world, we take plenty of activities online and rely on gadgets either phones, tablets or laptops to make our life much easier. When we accomplish the task, we forget to be thankful to the technology or the device that helped us (Swan, 2013). We also forget to praise the incredible science that is behind this – . Healthcare is one of the world’s largest and most important sector. A lot of health organizations have started using data science to solve this problem to enable them diagnose, track and cure diseases. Research problem Health care data always give health professionals knowledge. The information is processed and validated by data scientists?to diagnose, track and cure flu type of diseases. The future needs to find ways that will increase the ability to track and cure the disease and?not just diagnose the disease. The main aspect of this research proposal is to state how data science has come to our rescue in our day to day lives and to understand how data science can be used to know when and how an individual might get sick, find out how to prevent it and also reduce threats associated with it. In the health sector, machines are used to get information about specific patients diagnosed with a certain disease, analyzing in factors like gender, geography, economic or lifestyle data and more to determine patients who are going towards a diagnosis of a certain deadly disease, those who are misdiagnosed and how efficiently patients diagnosed with a certain disease are easily tracked by the doctors. Research questions • What are the ways in which data science contributes to day to day living? • What are the ways in which data science helps in diagnosing, tracking world’s deadliest and widespread diseases like influenza? • What are the ways in which data science helps in data mining to identify individuals at higher risk factor and contribute to their treatment? Hypothesis Null Hypothesis Data science has no significant relationship with day to day living. Alternative Hypothesis Data science has a positive impact on day to day living Hypothesis Null Hypothesis Data science has no significant relationship with diagnosing, tracking and curing some of the world’s deadliest and widespread diseases. Alternative Hypothesis Data science has a positive impact on diagnosing, tracking and curing some of the world’s deadliest and widespread diseases. Research Plan Data collection process can be considered as a set of samples and the total procedure in this research comprised of calculated approach. In the research, 30 Individuals participated out of which 20 were women and 10 were men. The data collected for the month f February and March in the year 2013 (Centellegher et al., 2016). The time was appropriate as in the duration of the year, symptoms associated with flu and has been observed frequently. The provided graph and associated analysis has acted as a contributing factor in gathering the required knowledge for conducting the study and coming up with the required research outcome. Thus, it is with the help of the research, authentic data could be gathered and the methodology helped in coming up with results according to the demand. Results This research acts as a contributing factor in predictive analysis through data science, and determine if an individual is at higher risk of getting infected and sick or not, by gathering an understanding of the individual’s mobility patterns. It can be noticed that there is a possibility that if the person shall predict to be ill in near future, in such case, mobility tracker in the mobile would display a lower rate of mobility. When the illness occurs, it is observed that there are increased chances of seeking rest. The results also helps in considering limitations which is associated with responsibility of chores that make individual’s take to mobility rather than illness, often in the case of parents. Social media updates provide with indication regarding the choice that is related with the mobility prior illness. It has been noticed that there exists a correlation between place choices before the occurrence of the illness. This is because; symptoms associated with cold result in opting to choose warmer places refuge instead of outdoor activities. This can be considered as a major indicator. Conclusion/Discussion In present research an effort has been made to use mobility patterns for predicting the risk of diseases like flu- in the near future. With the help of tracking mechanism the data related to the individual’s mobility has been taken into consideration. There are many aspects associated with the impact of the study, which are the opportunity for organizations to formulate enhanced mobile applications for tracking mobility and ascertain that the risk for communication of symptoms associated with influenza like is low. The reduced mobility is an indication of these conditions such as flu may attack the individual and may help in resolving the risk that is related with contagious disease. Data science can be used to predict someone’s health history and acts on the data accordingly so as to provide ways, in which someone can improve his overall health, reduce the intensity of diseases and diminish risks of contracting a disease over the whole of patient’s life.