Question

Choose 1 of this 6 subjects (examples) in the bottom write ethically please complete it all

This is a quantitative analysis focus these are the chapters we covered in class Chapter 1 Introduction to Quantitative Analysis

Chapter 2 Probability Concepts and Applications

Chapter 3 Decision Analysis

Chapter 4 Regression Models

Chapter 5 Forecasting

Chapter 6 Inventory Control Models

Chapter 7 Linear Programming Models: Graphical and Computer Methods

Chapter 8 Linear Programming Applications

Chapter 9 Transportation, Assignment, and Network Models

Chapter 10 Integer Programming, Goal Programming, and Nonlinear Programming

Chapter 11 Project Management

Chapter 12 Waiting Lines and Queuing Theory Models

Chapter 13 Simulation Modeling

Chapter 14 Markov Analysis

Chapter 15 Statistical Quality Control.

EXAMPLES:

1. WRITE A REPORT ABOUT A CONCEPT COVERED IN CLASS, OR ONE YOU WOULD LIKE TO LEARN MORE ABOUT THAT WE DID NOT COVER.

2. DEVELOP A PPT SLIDESHOW (PERHAPS 12 TO 15 CHARTS SHOULD SUFFICE WITH VOICE OVER THAT LASTS ABOUT 10 MINUTES OR SO) WHERE YOU CREATE VOICE-OVER AND PROVIDE A LECTURE ON A TOPIC WE LEARNED ABOUT, OR ABOUT ANOTHER QUANTITATIVE TOPIC YOU'D LIKE TO KNOW MORE ABOUT.

3. WRITE A REPORT DISCUSSING HOW YOUR COMPANY USES QUANTITATIVE TECHNIQUES TO MAKE DECISIONS, OR WHERE THEY COULD IMPLEMENT QUANTITATIVE TECHNIQUES TO BETTER MAKE BUSINESS DECISIONS.

4. WRITE A REPORT ABOUT THE CONCEPT OF BIG DATA. IT'S A NEW BUZZ-WORD THAT HAS TAKEN OVER LATELY IN BUSINESSES. PERHAPS, YOU COULD DISCUSS WHAT BIG DATA IS, WHY IT IS IMPORTANT, HOW IT RELATES TO STATISTICS, ETC. , ETC.

5. INTERVIEW SOMEONE WORKING IN A QUANTITATIVE JOB AND DISCUSS THE RESULTS. YOU CAN COME UP WITH THE QUESTIONS, BUT PROBABLY 8 TO 15 IS A GOOD RANGE. SOME GOOD QUESTIONS MIGHT BE....WHAT SKILLSETS DO YOU NEED TO BE GOOD AT IN YOUR JOB? HOW DO YOU SEE THE FIELD OF DATA SCIENCE CHANGING? WHAT SOFTWARE AND PC SKILLS DOES ONE NEED TO BE FAMILIAR WITH? ETC.

6. ANYTHING ELSE THAT IS RELEVANT TO THIS COURSE.

REGARDLESS OF THE OPTION YOU CHOOSE, IF YOU WRITE A REPORT, I THINK 5 TO 7 PAGES IS SUFFICIENT (DOUBLE-SPACED). YOU CAN IGNORE THE 2,000 WORD COUNT RULE. ALSO, I AM NOT TOO PICKY ON APA, ETC. - JUST MAKE YOUR PAPER HAVE A NICE AND PROFESSIONAL APPEARANCE. I HOPE THESE OTHER OPTIONS PROVIDE A LITTLE MORE VARIETY THAN JUST HAVING 1 definite CHOICE.

Please answer completely

Answer 1

Introduction
As per the standard definition accepted in wikipedia, Big data is data sets that are so voluminous and complex that traditional data processing application software are inadequate to deal with them. Big data challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating and information privacy. There are three dimensions to big data known as Volume, Variety and Velocity.
Big data usually includes data sets with sizes beyond the ability of commonly used software tools to capture, curate, manage, and process data within a tolerable elapsed time. Big Data philosophy encompasses unstructured, semi-structured and structured data, however the main focus is on unstructured data. Big data "size" is a constantly moving target, as of 2012 ranging from a few dozen terabytes to many petabytes of data. Big data requires a set of techniques and technologies with new forms of integration to reveal insights from datasets that are diverse, complex, and of a massive scale.

Significance of Big Data
Recent years have seen a stamped increment in our ability to gather, store and offer information. As per IBM, 90 % of the information on the planet was produced over the most recent two years (International Business Machines Corporation, 2011). These information emerge from the Internet (looks, interpersonal organizations, websites, pictures), cell phones, logical examinations (genomics, cerebrum imaging, the study of disease transmission, ecological research), organizations (client records, exchanges, budgetary markers), governments (populace, social insurance, climate, programmed sensors) and different sources.
The key significance of Big Data lies not on the amount but rather on the potential employments. For example, the portrayal of complex illnesses at the atomic level joined with therapeutic and treatment history, demonstrative or imaging tests offers remarkable open doors for customized medication. The Large Hadron Collider records information 40 million times each second to test hypotheses in material science. Sites make a large number of suggestions consistently and examine new items and their costs. Information can help oversee urban areas or regular assets, think about environmental change or help creating locales. Postings in websites and informal organizations are utilized to devise political systems and concentrate how thoughts spread. Because of this broad potential, Big Data has been grasped by media, the scholarly community and organizations in an energetic, some of the time even dramatist, way. Terms, for example, information downpour or wave are normal. The 2012 World Economic Forum pronounced information as another class of financial resource, similar to money or gold (World Economic Forum, 2012). Information related callings reliably top generally rankings. I advance once more from the buildup and survey both examples of overcoming adversity and restrictions, calling attention to apparent lessons and pending difficulties. While Big Data requires a multidisciplinary approach, I receive a measurable perspective. Insights is the field entirely committed to gathering, dissecting and translating information. That is, to conveying us from inquiries to information, from information to data, and from data to learning and choices. It may appear to be shocking then that analysts have been moderately wary in holding onto Big Data as a god-like power. I trust that the clarification is straightforward. Experience has instructed analysts that information can be deluding and, much more dreadful, wrongly give the similarity of objectivity. Alongside control, Big Data likewise brings the open door for abundant misinterpretations. Due to the assortment in applications (Big Data is frequently characterized with 3V's: Volume, Velocity and Variety) an exhaustive survey is miserable; henceforth I restrict talk to a portion of the primary issues and illustrations.

Role of statistics in big data
Much in the way that pioneers, for example, Ronald Fisher, William Gosset or Harold Jeffreys set the reason for information based science, business and open strategy, the Big Data worldview is powered by methodological commitments. The Pagerank calculation utilized by Google's motor depends on Markov Chains. Netflix film proposals utilize a model that midpoints 107 expectations. Choice hypothesis can help survey the advantages of complex calculations even with vulnerability and contending objectives, e.g. consumer loyalty may likewise rely upon proposal assorted variety.
We have just talked about the need to inquire about new techniques to unravel motion from commotion, catch dynamic procedures, outline tries and incorporate heterogeneous information. Computational techniques that consolidate handling power with sharp systems to take care of complex issues are another focal issue, as animal power approaches are probably not going to succeed. Additionally challenges incorporate information recovery and outline. Programmed techniques to output and organization unstructured information (e.g. pictures, websites) may dispose of data or actuate predispositions. Likewise, our current producing of a greater number of information than we can store (Hilbert, 2012) forces the need to condense information. Rundowns suggest a potential for loss of data. For instance, we as of late detailed that the present technique to compress RNA-sequencing information disposes of so much data that one can't take in specific highlights, even as the measure of information develops to interminability (Rossell et al., 2014). A related issue is that of testing. Putting away a sufficient example acquired from all information can profit speed and cost for an immaterial misfortune in exactness. See Fan et al. (2014) and Jordan (2013) for audits on measurable and calculation issues for Big Data.
As a special mix of logical thinking, likelihood hypothesis and arithmetic, measurements is an important segment for the Big Data insurgency to achieve maximum capacity. In any case, measurements can't exist in separation, but instead in joint effort, with subject-of-matter aptitude, software engineering and related orders. As a last idea, the primary snag to overcome may well be the absence of experts with a satisfactory mix of aptitudes. Enrollment and preparing of youthful personalities willing to connect with this energizing endeavor ought to be a best need.

Benefits and Risks of Big Data
Organizations have been breaking down information from their own client collaborations on a littler scale for some a long time, yet the time of enormous information is still in its infancy. Therefore, mining huge informational indexes to discover helpful, nonobvious designs is a generally new yet developing practice in promoting, extortion aversion, HR, what's more, an assortment of different fields. Organizations are as yet figuring out how to manage enormous information and open its potential while keeping away from unintended or unanticipated consequences. Properly utilizing huge information calculations on information of adequate quality can give various open doors for upgrades in the public eye. Notwithstanding the all inclusive advantages of all the more proficiently coordinating items and administrations to shoppers, huge information can make open doors for low-wage and underserved groups.
The benefits are namely
1) Increase educational attainment for individual students.
2) Provide access to credit using non-traditional methods.
3) Provide healthcare tailored to individual patients’ characteristics.
4)Provide specialized healthcare to underserved communities.
5)Increase equal access to employment.

The the risks are as follows:  
1)Result in more individuals mistakenly being denied opportunities based on the actions of others.
2)Create or reinforce existing disparities.
3)Expose sensitive information.
4)Assist in the targeting of vulnerable consumers for fraud.
5)Create new justifications for exclusion.
6)Result in higher-priced goods and services for lower income communities.
7)Weaken the effectiveness of consumer choice.

Conclusion
Enormous information will keep on growing in significance, and it is without a doubt enhancing the lives of underserved groups in regions, for example, training, wellbeing, neighborhood and state administrations, and work. Our aggregate test is to ensure that huge information examination keep on providing advantages and chances to customers while clinging to center shopper security esteems and standards. As far as it matters for its, the Commission will keep on monitoring zones where enormous information practices could abuse existing laws, including the FTC Act, the FCRA, and ECOA, and will bring implementation activities where suitable. Moreover, the Commission will proceed to look at and bring issues to light about enormous information rehearses that could detrimentally affect low-wage and underserved populaces and advance the utilization of huge information that positively affects such populaces. Given that enormous information investigation can have huge results, it is basic that we cooperate—government, scholastics, customer supporters, and industry—to help guarantee that we boost huge information's ability for good while distinguishing and limiting the dangers it presents.