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MMI223999 IT Professional Issues and Project Methods Assignment Sample

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MMI223999 IT Professional Issues and Project Methods Assignment

Overview on data science

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Data science is the various blends in the field of statistics, analysis of programming and cloud computing and so on. Which is mainly used to extract the values from the provided data? This is a vast field that is booming and this skill is being learned by all the people. And after learning this data science peoples are getting trained and gaining the knowledge of this particular domain. The skill that has been discussed above and the sub domains of this data science is generally known as the data scientist. One can be a skilled person in data science if he or she has gained all the knowledge about data science. Data engineer in software engineering and data analyst in data communication, and getting knowledge about math, statistics and algorithms is the main role of a data scientist. A data scientist should keep the knowledge in all the fields that have been listed above. If anyone has all the knowledge about this data science in the Venn, that means there are huge choices and opportunities to go forward in the IT industry. There are several kinds of data like structured data and unstructured data. Some brief analysis will be done about the structure of data in the next topics.

MMI223999 IT Professional Issues and Project Methods

Figure: Data scientist


Ethical problems in data science.

  1. Ethical issue is nothing but a situation in the business where the arrival of a moral conflict is done and this is addressed. In the other word this can be said that this is a kind of occasion where the moral standards are questioned. Actually ethical problems mean that, when the provided decision, activity and the scenarios created a constitution with the moral principle of society.
  2. In a disciplined way that actually deals with some many data aspects. In one way statics can be taken as the critical pillar that is being rapidly involved in the landscape of data science. There is a viral data role in increasing and especially in big data, or in several applications statics have taken a place in the field with an unparalleled challenge. Also it is creating some exciting opportunities (Pasillas, 2017). Statistics is playing a major role in data science and providing some help on assisting with the major use of data science like decision making and in the use of data in this uncertain face. Some research that made statistics and data science in the society and in the current generation of digital world has more impact. Being focused in that particular area of statistics will help in a better understanding of data science, and will be able to promote some more collaboration in the science and scientist domain.


  1. Determination of ethical issues or dilemmas. And if there are some conflict values or not or if there are some professional responsibilities available or not.
  2. The key values should be identified and the principles that are involved.
  3. Ethical principles and the values are needed to be ranked as per the professional judgment. The dilemmas that is relevant to the issue.
  4. An action plan is required to develop to consist of the ethical priority that has been selected or can be determined in the issue of dilemma.
  5. To illustrate the potentials on the ethical issues of the related problem, the solutions are discussed below.

In the law of enforcement there is some importance of facts. In order to investigate all the cases, some rely on the fact of the guard against cognitive biases and misinformation’s. Some investigation must be done on the surrounding dilemmas in order to ensure the right information. Rumors acting should be avoided and the gossip of verifying information and evidence are factual though. In the second case, determination of logical obligation and the duties. Users should be sure about the legal obligations and professionals are. Users will be allowed to decide the action of course in a very easy way and will be able to take the ethical dilemmas. However the obligations are not allowed for legal obligation and professionals all the time (Miller, 2020). Third case, that needs to be discussed about the participants and who are interested to be involved. This is important in order to know who has been impacted in this course of action to decide upon. Often the participants who are primary and there are easily identified. And the participants who are in the secondary phase are not considered often. These might include some family, friends, or might be some employees who are related to the primary participants in the ethics dilemma. After coming to know about the decision that has been made in the secondary participants it might be important to make the decision but the majority of it will not be considered. Last point that is very important is determination of the ethical values of the participants. When the options are being considered and some assistance is being done on ethical theories. Participant in the ethical dilemma having the most important and loyalty values. Or in some other cases this might happen that some other participants can have the most important value on the quality. When this is considered, all the loyalty values may or may not compare to the equality to depend upon in the ethical dilemma.

Social problems in data science.

  1. social problem or issue can be identified by society as a kind of problem which is being prevented in the current society for the function of an optimized level. It is an important topic to be understandable, like not all things that are occurring in the society and this has been raised to a social level problem. Mainly four types of social problems that may occur, these include the situation must be recognized by the public second point that comes in, a situation which is heavily against the society's general values. Recognizing the valid concerns in the population problems. And the last point that occurs is that the problem should be rectified by the citizens in the joint action.
  2. The craze of data science has just started to reshape the research of the current enterprise of the statistical field and in the biostatistician field. In the current research a list of challenged research areas has been picked for the interest of data scientists from the statistics of the research perspective and its vice versa. The NSF report has quoted that the selection of the area has taken the advantage of collective wisdom. But in some cases this also reflects about the personal research that has been experienced and the views of the researchers. The list is to organize the particular orders, the meaning of exhaustive, but this has been done to simulate the discussion. The certain experts have an important area of research to make the discussion according to the challenging problems to the data science applications.
  3. To make the potential solution to the problem in data science these have to be discussed. The change of climate and poverty. And also reducing the pollution of air.
  4. It is just an example of how data science can be helpful to implement some identified issues in some more detail, and how data science is used to create the products of organizations and entrepreneurs. Data science can also be identified in order to tackle the challenge of some global issues.

For instance this can be said that there are some other members to follow the schedule of a program that is mainly used in data science development. This can be implemented in some emergency cases like in the ambulance services and in order to ensure the vulnerable people to provide some medicines in a faster way (Maierhofer, 2017). Some other personalized invention has been analyzed and also with this some job recommendation has been applied in the long term and unemployed. Some external accounts have been taken about the desires. Mainly these are the desires that are individual and have some restrictions. Also they are having some socioetric context. 

So far data science has fairly explored the method of pressing issues of the World. Some more effective data analysis and some collations including this some more services and effective ways to solve the challenge has been acclimated and in poverty and in air pollution. There are some effective challenges to put off air pollution by the use of data science. This is the effort that has been used to develop some innovative ideas about the technology to explore imperial exploration. Also other institutes of academics are doing the same methodology and are trying to solve the world's huge problems. And finding the chance to make the solution of that problem by the use of data science.

Legal problems in data science.

  1. legal issues are nothing but the law of some legal questions of that foundation in case. This actually can be preferable to the point in which the evidence is proved is disputed. And the outcomes actually interpreted in the law of court.
  2. To be concerned about the big problems in data science is ownership of data and security. Privacy of the customers that can be a legal issue in the problem is data science. Contracts of third parties might be one of the problems and the points will be discussed in the below points briefly. And often the participants who are primary and there are easily identified. And the participants who are in the secondary phase are not considered often. These might include some family, friends, or might be some employees who are related to the primary participants in the ethics dilemma. After coming to know about the decision that has been made in the secondary participants it might be important to make the decision but the majority of it will not be considered.
  3. The potential solutions that are available in order to address the legal issue of data science are consent use, some legal basis, requirement of anonymization and the requirements on retention. These are the points that are completely related to the legal issues of data science over artificial intelligence. Also some more points that can be added in the legal policy of data science that is the security, agency, negligence, and transparency, and the next point that is the third parties.
  4. Let's discuss the point that has been pointed out in the above part. First of all consent use, this is mainly the process where the customers actually consent for the data that has been obtained to use. And the information is collected and also there are some ways for the customers to process and collect the opt-out data. Next point that has been pointed out is the legal basis; this is actually applicable regulation privacy that has been regulated in the data that are being adhered to in order to collect certain information. And all the associations with the rights of customers (Karpatne et al. 2017). Requirement of anonymization is another point that has been pointed out. This is the process to arrange the data by the customers and data anonymization is being generated. Retention that are required in the legal issue that is the consumer data that is being saved for how much time this is going to be stored.

Security issues that can be analyzed in the legal problems. As the system software of the consumer of the software system. And all the AI systems had fallen under the various standards of security and with this happening the law of reporting breach. An updated IT security process is required in the organization to apply in the AI systems (Igual and Seguí, 2017). And also by luck the basics has been luckily understood from the basics about IT security. There are also several options for information to be collected and for customers to process and collect opt-out data. The next point is the legal basis. This is the actual data protection regulation that is regulated by the data that is tracked to collect specific information. And all the association with the customer's rights. There was another point about the anonymization requirement. This is the process by which a customer orders data and anonymization of the data is generated. Retention required for the case, consumer data retained how long it will be retained.

Reference list


Guerzhoy, M., 2019, July. Introduction to Data Science as a Pathway to Further Study in Computing. In ICER (p. 303).

Igual, L. and Seguí, S., 2017. Introduction to data science. In Introduction to Data Science (pp. 1-4). Springer, Cham.

Maierhofer, T., 2017. Introduction to Data Science.

Miller, J.A., 2020. Introduction to Data Science Using ScalaTion Release 2.

Pasillas, M.C.O., 2017. Toward Critical Data-Scientific Literacy: An Intersectional Analysis of the Development of Student Identities in an Introduction to Data Science Course. University of California, Los Angeles.

Karpatne, A., Atluri, G., Faghmous, J.H., Steinbach, M., Banerjee, A., Ganguly, A., Shekhar, S., Samatova, N. and Kumar, V., 2017. Theory-guided data science: A new paradigm for scientific discovery from data. IEEE Transactions on knowledge and data engineering29(10), pp.2318-2331.


Basu Roy, S., 2020. CS 301-001: Introduction to Data Science.

So, R.J., 2021. Introduction: Contemporary Culture After Data Science. Journal of Cultural Analytics1(1), p.22335.

Dawson-Elli, N., Lee, S.B., Pathak, M., Mitra, K. and Subramanian, V.R., 2018. Data science approaches for electrochemical engineers: an introduction through surrogate model development for lithium-ion batteries. Journal of The Electrochemical Society165(2), p.A1.

Miller, J.A., 2020. Introduction to computational data science using scalation.

Xie, T., Jun, Y.U. and Zeng, T., 2020. Econometric methods and data Science techniques: A review of two strands of literature and an introduction to hybrid methods.

Kolda, T.G., 2019. Introduction to SIAM Journal on Mathematics of Data Science (SIMODS). SIAM Journal on Mathematics of Data Science1(1), pp.1-7.

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