MITS6002 Business Analytics Assignment 2 Research Study MITS6002 Assignment 2 Copyright © XXXXXXXXXXVIT, All Rights Reserved. 2 Learning Outcomes The following learning outcomes have been covered in...

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unit name is business analytics and also read the assignment requirements carefully and ask if u think need to ask. i have attached 2 files one belongs to assignment requirements and second one belongs to the format like in IEEE.


MITS6002 Business Analytics Assignment 2 Research Study MITS6002 Assignment 2 Copyright © 2015-2019 VIT, All Rights Reserved. 2 Learning Outcomes The following learning outcomes have been covered in this assessment: LO3. Conduct research on a collection of business cases and perform statistical analysis as also interpret these outcomes to recommend appropriate business directions. LO4. Critically analyse a variety of business domains and adopt business analytics models appropriate to the domain that requires quantitative techniques for decision making. LO5. Recommend appropriate analytic tools and techniques to resolve complex business analytics problems in various industry sectors. Objective(s) This assessment item relates to the unit learning outcomes as in the unit descriptor. This assessment is designed to improve student presentation skills and to give students experience in researching a topic and writing a report relevant to the Unit of Study subject matter. INSTRUCTIONS Perform a literature review on a known topic in business analytics. It can be any topic on tools, methodologies or applications. Some examples include, but not limited to: 1. Use of predictive analysis in healthcare industry 2. Comparison of BI tools 3. Techniques of predictive analysis 4. Methods of representing multi-dimensional data in visualisations 5. Analytics techniques to improve logistics management 6. Security of data and privacy concerns in analytics Please note that this is an individual project. Discuss with your lecturer before week 7 to decide on a topic. The topic needs to be chosen before week 7. Based on your review you need to submit a report in IEEE format; see the word file in the moodle. Submit your report in a word or pdf format. Your report should be limited to 1200-1500 words. MITS6002 Assignment 2 Copyright © 2015-2019 VIT, All Rights Reserved. 3 Submission Guidelines All submissions are to be submitted through turn-it-in. Drop-boxes linked to turn-it-in will be set up in the Unit of Study Moodle account. Assignments not submitted through these drop-boxes will not be considered. Submissions must be made by the due date and time (which will be in the session detailed above) and determined by your Unit coordinator. Submissions made after the due date and time will be penalized at the rate of 10% per day (including weekend days). The turn-it-in similarity score will be used in determining the level if any of plagiarism. Turn-it-in will check conference web-sites, Journal articles, the Web and your own class member submissions for plagiarism. You can see your turn-it-in similarity score when you submit your assignment to the appropriate drop-box. If this is a concern you will have a chance to change your assignment and re-submit. However, re-submission is only allowed prior to the submission due date and time. After the due date and time have elapsed you cannot make re-submissions and you will have to live with the similarity score as there will be no chance for changing. Thus, plan early and submit early to take advantage of this feature. You can make multiple submissions, but please remember we only see the last submission, and the date and time you submitted will be taken from that submission. Your report should be a single word or pdf document containing your report. 1 2 > FOR CONFERENCE-RELATED PAPERS, REPLACE THIS LINE WITH YOUR SESSION NUMBER, E.G., AB-02 (DOUBLE-CLICK HERE) < preparation="" of="" papers="" for="" ieee="" transactions="" on="" magnetics="" (revised="" may="" 2015)="" first="" a.="" author1,2,="" second="" b.="" author="" jr.2,="" senior="" member,="" ieee,="" and="" third="" author="" (第三著者)3,4,="" fellow,="" ieee="" 1national="" institute="" of="" standards="" and="" technology,="" boulder,="" co="" 80305,="" usa="" 2department="" of="" electrical,="" computer,="" and="" energy="" engineering,="" university="" of="" colorado,="" boulder,="" co="" 80309,="" usa="" 3department="" of="" metallurgical="" and="" materials="" engineering,="" colorado="" school="" of="" mines,="" golden,="" co="" 80401,="" usa="" 4national="" institute="" for="" materials="" science,="" tsukuba,="" ibaraki="" 305,="" japan="" these="" instructions="" give="" you="" guidelines="" for="" preparing="" papers="" for="" ieee="" transactions="" on="" magnetics.="" use="" this="" document="" as="" a="" template="" if="" you="" are="" using="" microsoft="" word.="" otherwise,="" use="" this="" document="" just="" for="" helpful="" information,="" and="" prepare="" your="" paper="" using="" any="" word="" processor="" or="" latex="" (the="" general="" ieee="" latex="" template="" is="" available="" at="" www.ieee.org/publications_standards/="" publications/authors/author_templates.html).="" this="" word="" template="" is="" useful="" for="" estimating="" the="" length="" of="" an="" article="" but="" it="" is="" optional.="" the="" electronic="" file="" of="" your="" paper="" will="" be="" formatted="" further="" at="" ieee.="" paper="" titles="" should="" be="" written="" in="" uppercase="" and="" lowercase="" letters.="" avoid="" writing="" long="" formulas="" with="" subscripts="" in="" the="" title;="" short="" formulas="" that="" identify="" the="" elements="" are="" fine="" (e.g.,="" “nd-fe-b”).="" do="" not="" begin="" a="" title="" with="" the="" word="" “on.”="" avoid="" starting="" a="" title="" with="" articles="" like="" “the.”="" full="" names="" of="" authors="" are="" much="" preferred="" but="" initials="" may="" be="" used="" instead.="" chinese,="" japanese,="" and="" korean="" authors="" are="" encouraged="" to="" include="" their="" names="" in="" native="" characters="" in="" parentheses="" after="" their="" romanized="" names.="" more="" information="" may="" be="" found="" at="" www.ieee.org/publications_standards/publications/authors/auth_names_="" native_lang.pdf.="" department="" names="" are="" optional="" in="" the="" affiliations.="" do="" not="" give="" street="" addresses="" in="" the="" affiliations="" (except="" for="" authors="" with="" no="" institutional="" affiliation).="" define="" all="" symbols="" used="" in="" the="" abstract,="" and="" again="" in="" the="" text.="" do="" not="" cite="" references="" in="" the="" abstract.="" index="" terms—about="" four="" key="" words="" or="" phrases="" in="" alphabetical="" order,="" separated="" by="" commas.="" i.="" introduction="" t="" his="" document="" is="" a="" microsoft="" word="" template="" for="" ieee="" transactions="" on="" magnetics.="" use="" of="" this="" document="" as="" a="" template="" is="" optional.="" if="" you="" are="" reading="" a="" paper="" version="" of="" this="" document,="" please="" download="" the="" electronic="" file,="" transmag2015.doc,="" from="" www.ieee.org/publications_="" standards/publications/authors/author_templates.html="" so="" you="" can="" use="" it="" to="" prepare="" your="" manuscript.="" if="" you="" would="" prefer="" to="" use="" latex,="" download="" ieee’s="" latex="" style="" and="" sample="" files="" from="" the="" same="" web="" page.="" use="" those="" latex="" files="" for="" formatting,="" but="" please="" follow="" the="" instructions="" in="" transmag2015.doc="" (or="" transmag2015.pdf).="" authors="" may="" prepare="" their="" papers="" for="" review="" using="" any="" word="" processor,="" one="" or="" two="" columns,="" single="" or="" double="" spaced.="" please="" follow="" the="" writing="" style="" specified="" in="" this="" document.="" when="" you="" open="" transmag2015.doc,="" select="" “print="" layout”="" from="" the="" “view”="" menu="" in="" the="" menu="" bar="" (view=""> Print Layout). 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Answered Same DayMay 08, 2021MITS6002

Answer To: MITS6002 Business Analytics Assignment 2 Research Study MITS6002 Assignment 2 Copyright ©...

Kuldeep answered on May 10 2021
127 Votes
Big Data
Big Data    May 10    2020    
    
Contents
Introduction    2
Data privacy concerns of big data analytics    2
Conclusion    6
References    7
INTRODUCTION
For many reasons, big data analytics is broadly used every day. These methods of application analysis can undoubtedly bring innovative or new improvements to the business like, retail companies have successfully used big data anal
ytics to easily predict popular commodities in each season as well as predict the geographic regions with the greatest need, to name a few. Big data analysis is so powerful that it creates many new privacy issues in addition to all positive business possibilities [1]. This study includes the literature review on topic analyzing the big data analytics data privacy concerns.
DATA PRIVACY CONCERNS O BIG DATA ANALYTICS
Big data is big business. Big data can help organizations collect large amounts of information for analysis. It can help companies provide better insights and make better decisions. It is defined as a large amount of structured, semi-structured and unstructured data with the potential to mine information. As the amount of data we collect, collect and process continues to grow, privacy issues will continue to be an issue. Enterprises see big data as a powerful tool for the business development and have a competitive advantage over competitors. On the other hand, hackers view big data as malicious gold. Taking advantage of the potential of such a huge data pool is an opportunity that hackers will continue to explore [2]. The focus on big data is not limited to hackers. People are increasingly paying attention to marketers, financial institutions, employers, and of course, how the government collects and holds a lot of detailed information about individuals. Almost all human activities are recorded and stored; where we go, with whom we communicate, communicate, read and write, dress, eat, and watch things, which are sometimes recorded without our consent. Information may expose us to many adversities and may affect all aspects of human life, from interpersonal relationships, work, and loans and even on airplanes. Big data analysis as a data review process that uses algorithms and data analysis tools to discover patterns and trends to make meaningful conclusions and decisions [3]. As new trends in technology continue to emerge, their usage has been rising. Big companies are embracing big data analysis and incorporating it into their operating procedures. It is considered to be a way to increase efficiency while maximizing output, and can bring greater profit margins. In the retail business, big data analysis is used to predict the most needed products and make better decisions thereafter. It is even possible for these companies to predict market areas that may benefit from them. When companies use data analytics, there are many other benefits. However, for such large data, there are challenges [4].
Today, brands increasingly rely on the big data. Sadly, they often have too much confidence in algorithms as well as data accuracy. The methods used by brands to collect the data from the beginning might be flawed, which means they might collect inaccurate data or information. Hackers, malware, disk damage, and other problems can also damage data. If brands create models all around imperfect data, it may cause serious trouble for all relevant users [5]. For instance, an insurance firm might create an inaccurate risk situation based on defective data, which might unfairly penalize every user in their group. From last few years, BD has changed the whole world in different ways, most of which have become better. On the other hand, it also brings few security risks. They must understand such risks moreover take appropriate protective measures. BD is term used for...
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