|Exam Name||:||IBM Content Analytics Theory Technical Mastery Test v1|
|Questions and Answers||:||49 Q & A|
|Updated On||:||April 19, 2019|
|PDF Download Mirror||:||Pass4sure 000-M90 Dump|
|Get Full Version||:||Pass4sure 000-M90 Full Version|
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000-M90 exam Dumps Source : IBM Content Analytics Theory Technical Mastery Test v1
Test Code : 000-M90
Test Name : IBM Content Analytics Theory Technical Mastery Test v1
Vendor Name : IBM
Q&A : 49 Real Questions
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during the past 6 years on my own, IBM has discharged over 20,000 U.S. personnel who have been at least forty years historic.
Written by means of The associated Press
technology big IBM centered “gray hairs” and “historic heads” for bad performance stories so it may oust them from the company, because it fashioned a “Millennial Corps” and focused on hiring “early professionals,” a new age-discrimination lawsuit claims.
“during the past six years by myself, IBM has discharged over 20,000 U.S. personnel who have been as a minimum 40 years ancient in pursuit of a corporation-wide observe of the use of compelled neighborhood terminations, referred to as ‘resource movements,' to achieve its aim of removing older personnel from its labor force,” observed the lawsuit filed Wednesday with the aid of 4 former IBM personnel. Three of them labored on the enterprise for greater than three a long time, and one for greater than 10 years, the swimsuit spoke of. All have been over 55 once they have been sacked in may 2016, according to the swimsuit.
The swimsuit alleged that IBM required employees to submit claims of age discrimination to binding arbitration, however additionally banned them from collective arbitration over such claims.
IBM referred to in an emailed remark that the plaintiffs' theories had been rejected via courts together with the U.S. Supreme court. “we are confident that our arbitration clauses are criminal and appropriate,” the company observed. The company delivered that a body of Supreme court circumstances upholds arbitration agreements.
The purported purge all started in 2014, with the firm conducting a plan to repair its “seniority combine” by imposing an “aggressive performance management posture,” the suit filed in federal court docket in long island alleged. One in-house presentation confirmed that this posture meant doubling the percentage of people receiving terrible performance opinions, so three,000 employees can be laid off and replaced with “early specialists,” the suit claimed.
“In 2015 and 2016, IBM doubled down on its efforts to substitute its lengthy-tenured, older personnel with the more youthful Millennials it sought to recruit,” the go well with alleged. “IBM made presentations to its senior executives calling for IBM to evaluate its lengthy-term employees extra harshly, to use those negative opinions to justify selecting long-time period personnel for lay-off, and to replace these personnel with ‘EPs' — IBM administration short-hand for ‘early experts.'”
A 2016 presentation regarding one element of the business “especially referred to as for managers to exempt all ‘early professional hires' from layoff, despite efficiency,” the suit claimed. “The lengthy-serving, older employees had been supplied no such exemption.”
the previous employees suing IBM — Steven Estle, Margaret Ahlders, Lance Salonia and Cheryl Witmer — alleged the enterprise in 2014 begun downgrading their annual efficiency scores, so they all started receiving worse evaluations than in outdated years. when they had been fired in 2016, IBM falsely characterized their departures as retirements, the go well with claimed.
additionally beginning in 2014, IBM begun traumatic that laid-off worker's waive their appropriate to collective motion, the go well with alleged. personnel had been offered severance value a month's revenue, carrying on with health and lifestyles assurance insurance for a duration reckoning on time with the company, free profession counseling and as much as $2,500 for advantage training, the suit mentioned.
however workers would not receive any of those advantages if they did not sign an contract now not to deliver age-discrimination claims jointly, even in arbitration, the go well with claimed. by way of this arrangement, IBM sought to deprive employees of the economies and merits of pursuing legal motion together, and “as an alternative to burden them with the limitations and costs of bringing particular person movements challenging the identical discriminatory practices in secret arbitrations break away each other,” the swimsuit alleged.
“With misgivings, but dealing with the chance of a difficult job search and economic hassle, each and every Plaintiff reluctantly signed the waiver,” the swimsuit said.
The go well with took intention at a 2006 IBM inner file on employee demographics that purportedly known as older employees “gray hairs” and “old heads,” and concluded that younger people had been “frequently a lot greater innovative and receptive to know-how than baby boomers.”
In 2014, IBM launched a blog referred to as “The Millennial event,” the swimsuit pointed out. The firm also created “Millennial Corps,” a network of young workers to be consulted through senior leadership about business choices, in line with the go well with.
The swimsuit stated a presentation given at a 2014 IBM event, wherein slides shown allegedly indicated that Millennials exhibited attractive work traits comparable to trusting facts and making choices through collaboration, whereas laborers over 50 had undesirable attributes akin to being “greater dubious” of analytics, putting “much less inventory in records” and being less stimulated to consult colleagues.
The swimsuit draws closely from outcomes of a ProPublica investigation published in September that talked about IBM seemed to be winding down the Millennial Corps, because it was stated in a number of prison actions as proof the company was biased in opposition t younger laborers.
The plaintiffs are seeking a court invalidating IBM's waiver in opposition t collective action, a ruling that the be counted go to collective arbitration, and unspecified economic relief.
©2019 The Mercury information (San Jose, Calif.)
visit The Mercury news (San Jose, Calif.) at www.mercurynews.com
dispensed through Tribune content material company, LLC.
(MENAFN - PRLog) Potentia AnalyticsTM, Inc. the resourceful utility company commonplace for patient move Optimization, intelligent issuer Scheduling, and Predictive Analytics for strategic decisions has introduced the appointment of Tracy Schweitzer as an Account government. She might be responsible for all direct and channel revenue efforts all through the company with a focus on increasing the healthcare software solutions suite. The options suite offers a unique abilities to the healthcare sector through using synthetic intelligence, data mining, computing device learning and video game theory as main-edge options that drive resolution making, efficiency and profitability for his or her shoppers.
Tracy is an executive revenue expert with a confirmed song record of supplying profits increase. Her journey spans over 30 years within the company world at American categorical and IBM. At American specific she led teams to power incremental revenue and customer pride, through American specific' fiscal solutions, within Fortune 1000 agencies. ahead of joining American express in 2011, Tracy had a 22+ yr profession with IBM in revenue (international and home) and sales administration promoting IBM's whole software portfolio of options to small, medium and commercial enterprise groups.
"The greatest asset I deliver to the table is building and conserving amazing a success relationships with both shoppers and internally. I have a proven skill to take note customers' strategic company targets, establish needs and manipulate alternatives to closure. right through my career, my success has come from my effective will to win. I seem forward to continuing this could to win with Potentia AnalyticsTM!"
all over her career, Tracy has offered to many industries including healthcare. She earned a Bachelor of Science diploma on the tuition of South Dakota. Reflecting on her excitement for the opportunity, Tracy brought up, "i am delighted to join Potentia AnalyticsTM and be part of such an exhilarating crew! i am eager for working with every and every grownup and aid develop the enterprise."
"we are extremely excited to have Tracy be a part of our crew. We believe that her background, knowledge and skill will help our corporation attain our desires and raise us to new heights. As our company grows, we can proceed so as to add proficient people to our increasing team. Tracy has an in depth sales history, a aggressive character and a need to win. We seem forward to the partnerships she will support us create." Bonnie Kucharski, VP of Operations
Potentia's growing business attain includes hospitals, urgent care amenities, emergency administration groups, healthcare programs, lean consulting businesses/groups, outsourced issuer administration businesses and nurse staffing companies among others.
Tavia Vasicek, 1.618.505.2001, ext. 105
Potentia Analytics Inc. | issuer Scheduling, patient circulate Optimization & Predictive Analytics ( https://www.potentiaco.com/ )
IBM Corp. (IBM - Get document) is the most beneficial-performing component of the Dow Jones Industrial common up to now in 2019 and has the highest dividend yield of four.70%. This member of the 2019 "dogs of the Dow" closed last week at $139.forty six, up 22.7% 12 months to this point and in bull market territory 31.6% above its Dec. 26 low of $105.94. The stock is low-cost with a P/E ratio of 10.09, in accordance with Macrotrends.
My call to purchase IBM is supported with the aid of its every day chart, which indicates that a formation of a "golden pass" appears tremendously possible this week. A "golden move" happens when the 50-day simple moving average rises above its 200-day elementary relocating general to point out that larger prices lie ahead. The weekly chart is effective however overbought and the inventory appears poised to upward thrust to its 200-week fundamental moving average at $149.34, which I consider a return to its "reversion to the imply."
The "reversion to the imply" is where a stock should still be trading given all primary counsel about the company. As a market technician i agree with this to be the 200-week primary relocating usual.
IBM may still be considered a play on the theme that "everything ancient is new again." The enterprise has a string of 17 consecutive quarters of beating analysts' salary-per-share estimates. during the fourth-quarter endure market, IBM gapped lower on susceptible income tips on Oct. sixteen. Then on Jan. 21, the stock gapped higher on an improved-than-anticipated outlook.
once I graduated from Georgia Tech in 1966 as an Industrial Engineer, my first job turned into at Grumman in Bethpage, new york. whereas getting my grasp of Science degree at Brooklyn Poly, I taught myself how to application in Fortran. I took over a software used in manufacturing engineering and had entry to IBM 360 mainframe computers both at work and at school. The concept of the historical mainframes is now new again as IBM's cloud computing is on mainframes now not a farm of servers. I say that here is a extra secure platform. This platform could be superior as the business integrates offerings based mostly upon IBM's acquisition of crimson Hat Inc. (RHT - Get report) . What makes IBM new once again might be the building AI applications in line with the Watson platform and aggressive choices in facts evaluation, cell applied sciences and safety.
The each day Chart for IBM
Courtesy of Refinitiv XENITH
Let's appear on the daily chart for IBM because its Dec. 26 low of $one hundred and five.ninety four. That day become a "key reversal" as the day's shut at $111.39 was above the Dec. 24 excessive of $111.00. This signal verified a tradeable rally. The close of $113.67 on Dec. 31 become enter to my proprietary analytics and resulted in semiannual and quarterly pivots at $126.75 and $122.49, respectively, and in an annual dangerous level at $166.09. word how the quarterly stage of $122.forty nine provided a buying possibility on Jan. 22. The shut of $138.13 on Feb. 28 became an input to my analytics and resulted in a monthly value degree at $128.forty five. be aware how the 50-day standard moving common at $134.ninety five is rising toward its 200-day standard moving common at $a hundred thirty five.61. When the 50-day crosses above the 200-day, a "golden move" might be verified and indicate that greater expenditures will follow.
The Weekly Chart for IBM
Courtesy of Refinitiv XENITH
The weekly chart for IBM is positive however overbought with the inventory above its five-week modified moving common of $136.32. The stock is under its 200-week essential relocating regular or "reversion to the imply" at $149.35 last proven right through the week of Oct. 5 when the regular was $153.sixty six. The 12x3x3 weekly gradual stochastic reading is projected to upward thrust to 91.eighty three this week up from 91.forty three on March 22. This studying is smartly above the overbought threshold of eighty.00 and the analyzing above 90.00 makes the inventory an "inflating parabolic bubble."
buying and selling strategy: purchase weakness to the 200-day essential relocating general at $135.61 and cut back holdings on electricity to the 200-week standard moving usual at $149.35.
the way to Use My cost tiers and risky tiers:
cost tiers and harmful levels are based mostly upon the remaining 9 weekly, month-to-month, quarterly, semiannual and annual closes. the primary set of ranges became in response to the closes on Dec. 31. The long-established quarterly, semiannual and annual levels stay in play. The weekly level adjustments every week; the monthly stage became modified at the conclusion of January and February. My idea is that 9 years of volatility between closes are ample to anticipate that every one viable bullish or bearish pursuits for the stock are factored in already. To trap share expense volatility, investors should purchase on weakness to a value stage and in the reduction of holdings on power to a harmful level. A pivot is a price level or risky stage that changed into violated within its time horizon. Pivots act as magnets which have a excessive probability of being confirmed once again earlier than its time horizon expires.
how to Use 12x3x3 Weekly slow Stochastic Readings:
My option of the usage of 12x3x3 weekly slow stochastic readings become according to back-trying out many strategies of studying share-expense momentum with the goal of finding the aggregate that resulted within the fewest false indicators. I did this following the stock market crash of 1987, so I actually have been chuffed with the effects for more than 30 years. The stochastic studying covers the final 12 weeks of highs, lows and closes for the inventory. there is a uncooked calculation of the changes between the highest excessive and lowest low vs. the closes. These levels are modified to a fast reading and a slow reading and that i discovered that the sluggish studying worked the most excellent. The stochastic reading scales between 00.00 and one hundred.00 with readings above 80.00 regarded overbought and readings below 20.00 regarded oversold. recently, I mentioned that shares are inclined to peak and decline 10% to 20% and more shortly after a studying rises above 90.00, so I name that an "inflating parabolic bubble" as a bubble at all times pops. I also call a studying beneath 10.00 as being "too low priced to ignore."
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More and more business schools are offering business masters degrees, from generalist programs in management to specialized master’s offerings covering topics like accounting, data analytics, finance, and marketing.
With so many options out there, choosing the best master’s degree to suit your career ambitions can be a challenge.
Before applying, candidates should know the answers to the following questions: Who are the different master’s programs for? What jobs do they lead to? What do you study on the course? What do you need to know to get accepted?
To answer those questions, we spoke with students and faculty from five of the most common business master’s degree options at some of the world’s leading business schools.
Read on to find out how to choose the best master’s degree for you.
You can find out more about the master’s degree that interests you most by clicking on one of the links below.
Masters in Accounting
Masters in Analytics
Masters in Finance
Masters in Management
Masters in Marketing
Find out more about business master’s degrees via webinar
Masters in Accounting
Find out more about Masters in Accounting via webinar
Dr Wang Jiwei, MSc in Accounting director at Singapore Management University (SMU)
Who is a Master’s in Accounting for?
Our Master of Professional Accounting (MPA) is designed to create a pathway for non-accounting graduates working or seeking to work in a field that requires specialized knowledge in accounting.
We also have a Master of Science in Accounting (MSA) which specializes in data and analytics. It’s designed for students who already have a background in accounting or business related disciplines, but wish to further their career and skills set in this data-driven era.
What skills/qualifications do you need to apply?
The overall quality of the applicant determines admission to the program. This would include prior academic performance at university, work experience, referees reports, interview scores, and GMAT/SMU Admission Test is compulsory.
Applicants must have sufficient background in math. Applicants with post-undergraduate working experience will have an added advantage. Applicants to the MSA must also have a business or accounting background.
What will you learn on a Master’s in Accounting?
Through the MPA course, students will learn about the three pillars of accounting; the business fundamentals, accounting core and the professional services which would include the following modules: management accounting, taxation, audit, corporate financial management and financial accounting.
The MSA equips students with the essential skills to harness data for critical decision making. They learn to make use of tools such as R, Python, Tableau, and SQL for data analysis, data visualization and data management. They also can learn machine learning techniques, and understand how they can be applied to the accounting and finance work.
What jobs can you qualify for after a Master’s in Accounting?
An accounting master’s degree is versatile. Every company needs somebody who is well versed in finance and accounting. Hence, the career of an accountant is not limited to the accounting, banking and finance industry. Good accountants are in demand in every sector.
In the recent years, the role of an accountant has evolved. Many organizations are eliminating manual accounting work, because computers and robots can automatically generate accounting entries.
But at the same time, we also see a growing demand in other job functions, mainly in the areas of data processing, management analysis and financial analysis. In the current market, there is a demand for domain experts who understand finance, accounting and data technology and how the systems work.
Top tip for applying to SMU?
What will give our applicants an edge is whether they have the drive to excel, the gumption to overcome challenges and the passion to make a meaningful impact through their work.
Master’s in Accounting student Q&A
Ryan Guo Yu Sheng, SMU MSA student (part-time), manager of Global Operating Certificates at Visa in Singapore
Why pursue a Master’s in Accounting?
I have always been a tech-savvy accountancy graduate, and most of the process and data technology applied in my work was self-taught and unstructured. After completing a specialist diploma in Business Analytics in 2017, I was looking to further my knowledge and build on the basics learned in that course.
An understanding of data analysis and usage of automation and robotics have become essential to the modern finance function and this is also part of my effort in future-proofing my own career. There is an increasing focus on the application of technology to finance processes and simply relying on our IT colleagues to fill us in on technical developments is a thing of the past.
What advice do you have for prospective Master's in Accounting students?
A candidate should be technically savvy. Dealing with data requires a highly analytical and structured skillset, along with a sense of creativity for data transformation and programming. Having a conceptual understanding of accounting is important.
Masters in Analytics
Find out more about Masters in Analytics via webinar
Crystal Grant, director of admissions (pictured above), and Kalyan Talluri, MSc Business Analytics program director at Imperial College Business School (pictured below)
Who is a Master’s in Analytics for?
There's a huge range of Master's in Analytics programs available, which reflects the growing demand from candidates and employers for graduates who are able to derive business insights and competitive advantage from big data.
Some will be aimed at new or recent graduates looking to launch their career, whilst others will be suited to professionals hoping to up-skill to progress or change jobs.
What skills/qualifications do you need to apply?
Programs vary in the balance of business versus technical experience required, but if you have a passion for data analytics, quantitative skills and commercial awareness, you should be able to find a Master's in Analytics that supports your goals.
Successful candidates to our MSc Business Analytics bring a wide variety of academic and work experience. We look for strong quantitative, communication, problem-solving and team-working skills. A passion for coding is also important, as the curriculum is very practical and you should be ready to learn and use programming languages.
What will you learn on a Master’s in Analytics?
Masters in Analytics aim to help graduates and young professionals understand the content, relevance, and importance of big data problems facing businesses. Our program takes a practical approach including a variety of machine learning, statistical, and operations research techniques.
Students can expect to learn solid technical skills in programming, including R, Python and SQL, data structures, and algorithms. They’ll learn skills in optimization, econometrics and statistics, networks, and machine learning. Industry-specific application is core to the program, and students learn to apply their mastery of tools to real business problems.
What jobs can you qualify for after a Master’s in Analytics?
The most popular destination for our graduates is consulting, however others go on to work in technology, FMCG, e-commerce, finance, healthcare, and media. Graduate roles vary depending on candidates’ previous experience, but include positions like business analyst, data scientist and, consultant. Employers include Accenture, Amazon, BCG, eBay, EY, Microsoft, and Revolut.
Top tip for applying to Imperial?
Don’t just list your experience of coding and programming languages. Highlight your problem solving skills by showing how you’ve applied your technical expertise on interesting projects. Demonstrating big-picture thinking, commercial awareness, and the ability to articulate complex information in a compelling and accessible way will help your application stand out.
Master’s in Analytics student Q&A
Pinelopi Chamalelli, MSc Business Analytics student at Imperial, joining the analytics graduate scheme of a major consulting firm in London after graduation
Why pursue a Master’s in Business Analytics?
Analytics is the future; every business no matter its size collects more and more data, and desires to utilize them for a more efficient decision making. So, the opportunities we have after this program are endless not only in terms of quantity but also in terms of variety, since analytics can be applied in every single industry.
What advice do you have for prospective Master's in Analytics students?
Coding skills or generally the ability to learn a new programming language or a new software package quickly is crucial. Three main programming languages we use are Python, R, and SQL, since these are widely used in the industry.
Good knowledge of linear algebra and statistics also help a lot. A good business understanding is also crucial for a successful data scientist.
Masters in Finance
Find out more about Masters in Finance via webinar
Diane Jordan, associate director of admissions, MIT Sloan
Who is a Master’s in Finance for?
Our Master of Finance is for individuals interested in learning the fundamentals of modern finance that emphasizes a foundation in how markets work. Students of the program come from a variety of bachelor’s degree disciplines including business, economics, finance, and significant representation from the STEM areas.
What skills/qualifications do you need to apply?
We seek smart, motivated applicants who are passionate about finance. Recent graduates, early career professionals in finance, engineers, mathematicians, physicists, computer programmers, and other high-tech professionals are encouraged to apply.
We look for: Success in academic, extracurricular, and professional endeavors; Ability to collaborate to accomplish a common goal; Ability to inspire others to achieve success; Excellent communication skills; Willingness to seek alternative solutions to existing challenges; Motivation to pursue goals.
What will you learn on a Master’s in Finance?
You’ll begin with rigorous courses in finance theory, financial mathematics, and financial accounting. You’ll be required to pass the Programming Literacy Test in either R or Python programming languages.
You’ll work with analytical tools—financial modeling, portfolio and pricing theory, statistics and data analytics, and computational methods—to help tackle multi-faceted challenges that arise in finance, from capital budgeting and cash flow analysis, to dynamic asset allocation and big-data-based investment strategies, and more.
You’ll take part in project-based courses solving real-world problems with MIT's partner corporations and culminating in presentations to corporate decision makers. You’ll choose electives from financial technology, to quantitative methods, to economics, to specialized disciplines such as healthcare finance, fixed income, mergers and acquisitions and asset management.
What jobs can you qualify for after a Master’s in Finance?
Jobs at asset managers, consulting firms, investment and corporate banks, brokerage firms, financial data providers, ratings firms, hedge funds, venture capitalists, insurance companies, public institutions, fintech, and more—from Fortune 500 companies to leading-edge boutiques.
Companies who have hired Master of Finance graduates from MIT for each of the last three years include Bain & Company, BCG, BlackRock, Deloitte, Morgan Stanley, McKinsey, and Citi.
Top tip for applying to MIT Sloan?
Get to know us and the admissions process through online chats and events to help you decide if MIT is a good fit.
Master’s in Finance student Q&A
David Smadja, student at MIT Sloan, has an offer for an associate position at a major investment bank in New York
Why pursue a Master’s in Finance?
Prior to my master’s, I graduated from a master of applied mathematics. Though I had a good understanding of the quantitative side of financial markets, I felt I was missing some important financial knowledge. To me, masters in accounting, for example, are not capable to cover everything you need to know to succeed in the field of finance like a master’s in finance does.
What advice do you have for prospective Master's in Finance students?
The role of the master’s is to teach you what’s necessary to break through the industry but I reckon that some preliminary skills are beneficial. If you’re interested in financial markets, some coding and analytics skills (statistics, optimization, AI a plus) are a minimum to have. If you’re more interested by the corporate side, a good understanding of financial statements analysis and accountability rules is crucial.
Masters in Management
Find out more about Masters in Management via webinar
Eric Simard, associate director, MSc admissions at Ivey Business School, University of Western Ontario, Canada
Who is a Master’s in Management for?
It’s for ambitious, recent graduates who want a specialized Master’s degree, that will hone their skill-set, develop their leadership abilities and accelerate their career success. Students are admitted from a variety of disciplines, backgrounds, cultures.
This creates a unique experience in classrooms which is meant to help students understand, reflect on, and challenge their tendencies, preferences and biases.
At Ivey, students apply to a specific field of study which will allow them to focus their learning in one of three areas; Business Analytics, Digital Management, or International Business.
What skills/qualifications do you need to apply?
We evaluate applicants on a number of dimensions including; Relationship management; Leadership orientation; Openness to learning; Adaptability; Program readiness; Academic performance in their final two years of their undergraduate degree; Motivation for joining the program; Short and long-term career objectives; Pre-program experience; and International exposure/experience.
What will you learn on a Master’s in Management?
Master’s in Management programs are designed to help students bridge the gap between a solid theoretical foundation received in undergraduate studies and the practical and soft skills needed to make a difference in the early stages of their career.
Through Case-Method approach to education, Ivey MSc students are put in the position of the decision-maker and are asked to analyze data, develop alternatives and then make and defend their recommendations. We are focused on creating the next group of business leaders who are able to walk into a new role and take action.
What jobs can you qualify for after a Master’s in Management?
Graduates take positions in a variety of industries including; Technology, Management Consulting, Financial Institutions, Telecommunications, Consumer Packaged Goods, and more.
The majority of our graduates take starting positions in leadership rotational programs or in consulting firms focused in Risk, Technology or Human Capital. Many have titles such as data science associate, area manager or data analyst, and they work in a variety of organizations such as PwC, Scotiabank, Amazon, Walmart, Deloitte, Proctor & Gamble, Accenture, Sun Life Financial, BMO, Bell, EY, and Morgan Stanley.
Top tip for applying to Ivey?
Candidates should connect with our admission and recruitment team before submitting their application. They should spend time learning about what our programs have to offer by attending webinars, talking to current students, and reaching out to our MSc alumni. And they should consider and reflect on how the Ivey MSc in Management can support their short and long-term career goals.
Master’s in Management student Q&A
Deanna Hamilton, student at Ivey Business School, starting a consulting career after graduation
Why pursue a Master’s in Management?
I wanted to pursue a career in business and a Master’s in Management seemed like the perfect way for me to develop my knowledge as I did not study business during my undergrad. I also aspire to work internationally one day. Ivey’s Global Lab opportunity offered an eight-week, hands-on, international in-company experience which allowed me to immerse myself in an unfamiliar environment and work cross-culturally.
What advice do you have for prospective Master's in Management students?
Aside from the obvious skills of being a hard worker and passionate about the program, I think it is crucial for students to be open to learning and willing to adapt. It is essential for you to be willing to step out of your comfort zone to really take advantage of every opportunity on the program. Interpersonal and communication skills are also necessary for most jobs and, coming into the program, many of my classmates already had exceptional communication skills.
Masters in Marketing
Find out more about Masters in Marketing via webinar
Ning Li, academic program director of the MS in Marketing at Johns Hopkins, Carey School of Business
Who is a Master’s in Marketing for?
For students looking to continue in their marketing studies or students looking to pivot into marketing from another field. Those who are looking to become marketing experts will receive training in business foundations in addition to numerous high-level marketing electives.
What skills/qualifications do you need to apply?
Candidates will need undergraduate level communication and analytical skills.
What will you learn on a Master’s in Marketing?
Students will learn behavioral and quantitative methods and best marketing practices to address the most critical issues facing today’s marketer, including the impact of data and social media on marketing strategy, consumer behavior, social responsibility, competitive economics, business-to-business marketing, and more.
What jobs can you qualify for after a Master’s in Marketing?
Students can pursue careers in Marketing and Business Analytics, Marketing and Sales, General Management, Consulting, and more. Some employers who have hired our recent graduates include Amazon, China Telecom Americas, IBM, Johns Hopkins Medicine, P&G, and Tencent.
Top tip for applying to Carey?
Be Honest. No applicant is perfect and if there is a part of your application you are concerned about, be honest. Be open to why this program appeals specifically to you. This is important because honestly addressing concerns is important in marketing roles.
Master’s in Marketing student Q&A
Charlie Wang, MS in Marketing student at Johns Hopkins, Carey School of Business
Why pursue a Master’s in Marketing?
I value a program with a multi-disciplinary scientific approach to business where I’m exposed to a range of subjects, such as economics, psychology, and data science. Compared to other programs, Master’s in Marketing students explore a wider range of theories and models, and are able to quickly adapt to work in a variety of industries.
What advice do you have for prospective Master's in Marketing students?
Two skills that are essential are curiosity and fast learning skills. You don’t need to have all the answers, but you do need to be curious. You are expected to quickly adapt to the multi-disciplinary curriculum at a fast pace.
My advice: Start making friends now! Marketing students should be aware of the importance of relationships. Developing deep and enduring relationships with not only clients but also organizations will directly or indirectly affect one’s success.
Are you interested in a business master’s degree?
Register now for the Master’s Week webinar series held by the Graduate Management Admission Council (GMAC).
Running from 8-to-12 April, different business schools will talk about a different master’s program each day.
Monday: Accounting; Tuesday: Business Analytics; Wednesday: Finance; Thursday: Management; Friday: Marketing
As one of the top three international conferences on global data mining, IEEE ICDM has been dedicated to in-depth data mining in statistics, machine learning, pattern recognition, database and data warehouse, data visualization, knowledge-based systems, high-performance computing and other fields. The conference also invited UBTECH's chief scientist Prof. Dacheng Tao; IBM Almaden fellow C. Mohan; Ramamohanarao (Rao) Kotagiri, dean of the School of Computing and Information Systems, Melbourne School of Engineering, the University of Melbourne; Graham William, Microsoft Asia Pacific R&D Group's director of cloud computing, AI and data science; Steve Miller, SMU Vice Provost (Research) of Information Systems; and other tech giants. They displayed their latest technologies and achievements in computer vision, blockchain, machine learning and other related fields. In addition, the attendees also discussed a wide range of common concerns about the promotion and application of cutting-edge technologies such as database and machine learning, as well as their future challenges.
World-renowned data mining and AI scholar Prof. Xindong Wu delivered an opening keynote speech titled "Great Wisdom". He proposed combining human intelligence (HI), artificial intelligence (AI) and organization/business intelligence (O/BI) with big data analysis for industrial intelligence in organizational activities. UBTECH's chief scientist Prof. Dacheng Tao introduced his team's achievements in machine vision, including breakthroughs in object detection, scenario analysis, depth recovery from single color images, target tracking and other aspects. IBM Almaden fellow C. Mohan shared a robust, effective and accurate automatic 3D segmentation algorithm for OCT imaging of retinal tissue layer and choroid.
As one of the pioneers of the application of AI and big data in education scenarios in China, Squirrel AI Learning has opened more than 1,600 learning centers in more than 300 cites in China, which have served more than 1 million students. The contract renewal rate is about 80%. Now it has become an important force in revolutionizing traditional education in China. Squirrel AI Learning's chief scientist Dr. Wei Cui pointed out in his speech that these achievements rely on Squirrel AI's self-built algorithm core. By collecting and analyzing learning data, Squirrel AI uses a nanoscale knowledge graph to detect knowledge points related to the targets in the least time and create personalized dynamic student portraits, forming self-learning and feedback on the prediction ability of AI and the effect of learning content.
At the technical level, Squirrel AI has integrated the most cutting-edge AI, big data technology, psychology, pedagogy and other relevant theories, forming a set of adaptive learning strategic algorithms running through the whole teaching process.
First, Squirrel AI has rebuilt the knowledge graph, benefiting from the improvement in AI and algorithm technology. In education, knowledge graph and graph theory are usually used to describe and represent each subject's knowledge system. However, there were always two problems in past knowledge graphs. First, the description of students' knowledge points was very crude; second, only strong correlations between knowledge points were marked, while weak correlations in the majority were not taken into account.
Squirrel AI adopts nanoscale separation to mark the four key points, i.e. difficulty level, importance, mastery and status with different icons and colors. In addition, the formerly universal 4-6 dimensions of student knowledge portraits in global intelligent adaptative learning have been upgraded to more than 30, so that each student's mastery of knowledge points can be clearly displayed in one knowledge graph. Taking middle school math as an example, in the adaptive system of Squirrel AI, the original 300 knowledge points have been refined to 30,000 knowledge points. In this process, Squirrel AI uses the theory of association probability of non-correlated knowledge points to build a network structure between knowledge points, so that knowledge points can be deduced from each other. At the operational level, Squirrel AI can adjust test questions with the largest amount of information in real time according to different feedback on each question from each student, so as to test the most knowledge points with the least questions.
Second, Squirrel AI can track students' learning conditions through data analysis and grasp their learning curves in real time. According to the human forgetting curve in educational psychology, students always accumulate some knowledge loopholes in their daily learning process. However, such knowledge loopholes will not disappear because students advance to the next stage, but will affect students' knowledge learning in the next stage. Using the Bayesian network and probabilistic graphical model, Squirrel AI can clearly complete students' knowledge portraits and understand students' overall mastery of knowledge more comprehensively. In addition, Squirrel AI adopts Bayesian knowledge tracking theory, which can detect students' past knowledge loopholes.
Third, based on the dynamic tracking of students' knowledge points, Squirrel AI can help students build personalized learning paths. With the genetic algorithm, neural network and machine learning technology, Squirrel AI can push appropriate learning content to students, get feedback and keep drawing multi-dimensional student portraits. According to the degree and state of students' knowledge mastery, the system automatically plans the most suitable learning difficulty and order for students, helps them check their omissions and fill in the gaps, so as to ensure that students can use the least time to grasp the knowledge should be grasped.
To sum up, Squirrel AI can go deep into the links of teaching, learning, testing and practicing through data collection and analysis, and truly realize accurate control of the whole chain of students' learning, turning education concepts such as "individualized teaching" and "teaching students in accordance with their aptitude" into reality.
In addition, Squirrel AI Learning's chief scientist Wei Cui said: "Although Squirrel AI already has a very precise question pushing system, which can continue to improve through the improvement of Squirrel AI's knowledge detection system, we still hope to improve interactivity. In the future, students' real-time heart rate, brain wave and facial expression recognition during learning will be added for comprehensive analysis. Each student will be equipped with a virtual personal assistant to provide better learning services for them."
In fact, Squirrel AI Learning has always attached great importance to technology research and development. Since its establishment, the company has gathered three of the world's leading experts in intelligent adaptive learning, namely Wei Cui, Richard Tong and Dan Bindman, as chief scientist, chief architect and chief data scientist of Squirrel AI Learning. They respectively came from three world famous AI adaptive education enterprises RealizeIT, Knewton and ALEKS. Integrating their nearly ten years of first-hand experience in the application and R&D of AI adaptive education technology with China's education and teaching habits, they successfully developed Squirrel AI, China's first AI adaptive learning engine centered on advanced algorithms with proprietary intellectual property rights. At the AIAED on Nov. 16, Prof. Tom Mitchell, the godfather of global machine learning, dean of CMU School of Computer Science, a member of the American Academy of Arts and Sciences and the National Academy of Engineering, AAAS fellow and AAAI fellow, officially accepted Squirrel AI Learning's offer of the position of Chief AI Officer. As Squirrel AI Learning's first person in charge in the field of AI, Mitchell will lead a team of more than 10 AI scientists and hundreds of AI application engineers and technical teams to conduct basic AI research in the field of intelligent adaptative education, as well as the development and application of related products.
Moreover, Squirrel AI Learning has established a joint AI Lab with Stanford Research Institute (SRI) and a joint AI adaptive education lab with Chinese Academy of Sciences (CAS) to enable a customizable, measurable, and teachable personalized education mode. In the past two years, Squirrel AI Learning has made great achievements in AI and big data. Its scientific research papers have been taken by EDM, CSEDU, AIED, AERA and other top international academic conferences. The company has won many international scientific research awards, such as EdTechX innovation award, which has established its leading position in the field of AI+ education.
In the future, Squirrel AI Learning will continue to increase investment in AI, big data, machine learning, education theory and other related fields, and promote the practical application of AI in education scenarios.
SOURCE Squirrel AI Learning
Hacking with NPOs: Collaborative Analytics and Broker Roles in Civic Data Hackathons 53:15
PACMHCI, Vol. 1, Issue. 2, Article 53. Publication date: November 2017.
In this paper, we identify several best strategies for the collaborative analytics in civic data hackathons,
which greatly help NPOs leverage diverse data volunteers’ knowledge and expertise to support NPO’s data-
driven work. We find that a good civic data hackathons should address the tensions between benefiting
NPOs’ data-driven work and helping volunteers learning expectations. We contribute to improve
collaborative analytics during civic data hackthons with a rich case study of applying the broker
framework as a lens in examining the client teams’ facilitation practice. More specifically, we identify four
important brokering activities that hackathon client teams performed to support collaborative analytics:
translation, coordination, alignment, and contact brokering. In addition to the practical and design
implications, we call on CSCW community’s attention to further study collaborative analytics and civic
We thank our participants for sharing their time and experiences with us. We are particularly indebted to
the Ann Arbor Data Dive organizing team—Karthik Ramanathan Dhanalakshmi, Stephanie Miller, Sunny
Choi, and Verena Getahun—for their support of this research.
 Jennifer Alexander, Jeffrey L. Brudney, Kaifeng Yang, Kelly Leroux and Nathaniel S Wright. 2010. Does performance measurement
improve strategic decision making? Findings from a national survey of nonprofit social service agencies. Nonprofit and Voluntary Sector
Quarterly 39, 4: 571-587.
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driven work. In Proceedings of CHI '17, 3608-3619.
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collaborations. Interacting with Computers 18, 1: 21-46.
 Joohee Choi. 2017. Characteristics of collaboration in the emerging practice of open data analysis. In Proceedings of CSCW '17, 835-846.
 Gregorio Convertino, Helena M Mentis, Aleksandra Slavkovic, Mary Beth Rosson and John M Carroll. 2011. Supporting common
ground and awareness in emergency management planning: A design research project. ACM Transactions on Computer-Human Interaction
(TOCHI) 18, 4: 22.
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of concern. In Proceedings of CHI '14, 2397-2406.
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 Sheena Erete, Emily Ryou, Geoff Smith, Khristina Fassett and Sarah Duda. 2016. Storytelling with data: examining the use of data by
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and Computer Graphics 12, 5.
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Symposium on Visual Analytics Science and Technology, 171-178.
 Daniel Hellmann, Carleen Maitland and Andrea Tapia. 2016. Collaborative analytics and brokering in digital humanitarian response.
In Proceedings of CSCW '16, 1284-1294.
 Youyang Hou and Cliff Lampe. 2015. Social media effectiveness for public engagement: example of small nonprofits. In Proceedings of
CHI '15, 3107-3116.
 Lilly Irani. 2015. Hackathons and the making of entrepreneurial citizenship. Science, Technology & Human Values 40, 5: 799-824.
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Research 31, 4: 349-357.
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for entrepreneurs. IEEE Access 1, 558-563.
 Michael Lenczner and Susan Phillips. 2012. From stories to evidence: how mining data can promote innovation in the nonprofit
sector. Technology Innovation Management Review 2, 7: 10.
 Thomas James Lodato and Carl Disalvo. 2016. Issue-oriented hackathons as material participation. New Media & Society 18, 4: 539 -
 Nan L. Maxwell, Dana Rotz and Christina Garcia. 2016. Data and decision making: Same organization, different perceptions; different
organizations, different perceptions. American Journal of Evaluation 37, 4: 463-485.
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Proceedings of CSCW '00, 231-240.
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learning in nonprofit community organizations: Methodological challenges and opportunities. In Proceedings of CHIMIT '07, 8.
 Jonathan Morgan, Wikimedia Foundation, Dharma Dailey and Benjamin Mako Hill. 2017. Hacking and making at time-bounded
events: Current trends and next steps in research and event Design. In Proceedings of CSCW '17 Companion, 363-370.
 Andreas Paepcke. 1996. Information needs in technical work settings and their implications for the design of computer tools. CSCW 5,
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