Apr 19, 18: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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Big Data knows everything  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ AnalyticsWeek BYTES]

>> Startup Styles [Infographic] by v1shal

>> The 7 Most Unusual Applications of Big Data You’ve Ever Seen! by analyticsweekpick

>> The biggest names in the world of big data are set to help New Orleans crunch numbers by analyticsweekpick

Wanna write? Click Here

[ NEWS BYTES]

>>
 Stocks making the biggest moves premarket: MKC, INFO, FDS, NWL, RHT, OSTK & more – CNBC Under  Financial Analytics

>>
 Microsoft, Accenture Partner To Scale B2B Startups – Youth Incorporated (press release) (blog) Under  Big Data Security

>>
 Machine Learning and Artificial Intelligence – Two Conferences to Attend in 2018 – InfoQ.com Under  Artificial Intelligence

More NEWS ? Click Here

[ FEATURED COURSE]

Machine Learning

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6.867 is an introductory course on machine learning which gives an overview of many concepts, techniques, and algorithms in machine learning, beginning with topics such as classification and linear regression and ending … more

[ FEATURED READ]

The Signal and the Noise: Why So Many Predictions Fail–but Some Don’t

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People love statistics. Statistics, however, do not always love them back. The Signal and the Noise, Nate Silver’s brilliant and elegant tour of the modern science-slash-art of forecasting, shows what happens when Big Da… more

[ TIPS & TRICKS OF THE WEEK]

Keeping Biases Checked during the last mile of decision making
Today a data driven leader, a data scientist or a data driven expert is always put to test by helping his team solve a problem using his skills and expertise. Believe it or not but a part of that decision tree is derived from the intuition that adds a bias in our judgement that makes the suggestions tainted. Most skilled professionals do understand and handle the biases well, but in few cases, we give into tiny traps and could find ourselves trapped in those biases which impairs the judgement. So, it is important that we keep the intuition bias in check when working on a data problem.

[ DATA SCIENCE Q&A]

Q:What does NLP stand for?
A: * Interaction with human (natural) and computers languages
* Involves natural language understanding

Major tasks:
– Machine translation
– Question answering: “what’s the capital of Canada?”
– Sentiment analysis: extract subjective information from a set of documents, identify trends or public opinions in the social media

– Information retrieval

Source

[ VIDEO OF THE WEEK]

The History and Use of R

 The History and Use of R

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

Data is the new science. Big Data holds the answers. – Pat Gelsinger

[ PODCAST OF THE WEEK]

@RCKashyap @Cylance on State of Security & Technologist Mindset #FutureOfData #Podcast

 @RCKashyap @Cylance on State of Security & Technologist Mindset #FutureOfData #Podcast

Subscribe 

iTunes  GooglePlay

[ FACT OF THE WEEK]

140,000 to 190,000. Too few people with deep analytical skills to fill the demand of Big Data jobs in the U.S. by 2018.

Sourced from: Analytics.CLUB #WEB Newsletter

Oct 12, 17: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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Tour of Accounting  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ AnalyticsWeek BYTES]

>> CMOs’ Journey from Big Data to Big Profits (Infographic) by v1shal

>> Underpinning Enterprise Data Governance with Machine Intelligence by jelaniharper

>> The Practice of Customer Experience Management: Paper for a Tweet by bobehayes

Wanna write? Click Here

[ FEATURED COURSE]

Introduction to Apache Spark

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Learn the fundamentals and architecture of Apache Spark, the leading cluster-computing framework among professionals…. more

[ FEATURED READ]

The Future of the Professions: How Technology Will Transform the Work of Human Experts

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This book predicts the decline of today’s professions and describes the people and systems that will replace them. In an Internet society, according to Richard Susskind and Daniel Susskind, we will neither need nor want … more

[ TIPS & TRICKS OF THE WEEK]

Save yourself from zombie apocalypse from unscalable models
One living and breathing zombie in today’s analytical models is the pulsating absence of error bars. Not every model is scalable or holds ground with increasing data. Error bars that is tagged to almost every models should be duly calibrated. As business models rake in more data the error bars keep it sensible and in check. If error bars are not accounted for, we will make our models susceptible to failure leading us to halloween that we never wants to see.

[ DATA SCIENCE Q&A]

Q:What does NLP stand for?
A: * Interaction with human (natural) and computers languages
* Involves natural language understanding

Major tasks:
– Machine translation
– Question answering: “what’s the capital of Canada?”
– Sentiment analysis: extract subjective information from a set of documents, identify trends or public opinions in the social media

– Information retrieval

Source

[ VIDEO OF THE WEEK]

#DataScience Approach to Reducing #Employee #Attrition

 #DataScience Approach to Reducing #Employee #Attrition

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

I keep saying that the sexy job in the next 10 years will be statisticians. And I’m not kidding. – Hal Varian

[ PODCAST OF THE WEEK]

Understanding Data Analytics in Information Security with @JayJarome, @BitSight

 Understanding Data Analytics in Information Security with @JayJarome, @BitSight

Subscribe 

iTunes  GooglePlay

[ FACT OF THE WEEK]

As recently as 2009 there were only a handful of big data projects and total industry revenues were under $100 million. By the end of 2012 more than 90 percent of the Fortune 500 will likely have at least some big data initiatives under way.

Sourced from: Analytics.CLUB #WEB Newsletter

Source by admin

Apr 12, 18: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

image
Big Data knows everything  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ AnalyticsWeek BYTES]

>> Building A Billion Dollar Company, Dropbox Story by v1shal

>> Out of the Loop on the Internet of Things? Here’s a Brief Guide. by analyticsweekpick

>> How to Use Social Media to Find Customers (Infographic) by v1shal

Wanna write? Click Here

[ NEWS BYTES]

>>
 How to spot a machine learning opportunity – The Enterprisers Project Under  Machine Learning

>>
 Aetna Grant Helps PA Expand Opioid Data Analytics Dashboard – Health IT Analytics Under  Health Analytics

>>
 Booz Allen & Kaggle’s Annual Data Science Competition Puts AI to Work Accelerating Life-Saving Medical Research – insideBIGDATA Under  Data Science

More NEWS ? Click Here

[ FEATURED COURSE]

CS229 – Machine Learning

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This course provides a broad introduction to machine learning and statistical pattern recognition. … more

[ FEATURED READ]

The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World

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In the world’s top research labs and universities, the race is on to invent the ultimate learning algorithm: one capable of discovering any knowledge from data, and doing anything we want, before we even ask. In The Mast… more

[ TIPS & TRICKS OF THE WEEK]

Fix the Culture, spread awareness to get awareness
Adoption of analytics tools and capabilities has not yet caught up to industry standards. Talent has always been the bottleneck towards achieving the comparative enterprise adoption. One of the primal reason is lack of understanding and knowledge within the stakeholders. To facilitate wider adoption, data analytics leaders, users, and community members needs to step up to create awareness within the organization. An aware organization goes a long way in helping get quick buy-ins and better funding which ultimately leads to faster adoption. So be the voice that you want to hear from leadership.

[ DATA SCIENCE Q&A]

Q:Why is naive Bayes so bad? How would you improve a spam detection algorithm that uses naive Bayes?
A: Naïve: the features are assumed independent/uncorrelated
Assumption not feasible in many cases
Improvement: decorrelate features (covariance matrix into identity matrix)

Source

[ VIDEO OF THE WEEK]

Discussing #InfoSec with @travturn, @hrbrmstr(@rapid7) @thebearconomist(@boozallen) @yaxa_io

 Discussing #InfoSec with @travturn, @hrbrmstr(@rapid7) @thebearconomist(@boozallen) @yaxa_io

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

If you can’t explain it simply, you don’t understand it well enough. – Albert Einstein

[ PODCAST OF THE WEEK]

#BigData @AnalyticsWeek #FutureOfData #Podcast with @Beena_Ammanath, @GE

 #BigData @AnalyticsWeek #FutureOfData #Podcast with @Beena_Ammanath, @GE

Subscribe 

iTunes  GooglePlay

[ FACT OF THE WEEK]

As recently as 2009 there were only a handful of big data projects and total industry revenues were under $100 million. By the end of 2012 more than 90 percent of the Fortune 500 will likely have at least some big data initiatives under way.

Sourced from: Analytics.CLUB #WEB Newsletter

What Role Do Startups Play in Fixing Consumer Debt? Essentials You Need to Know!

There was once a time when economy used to be more straightforward. Financial shortcomings were not non-existent, and debts were manageable with pocket-friendly interest rates and considerable repayment time. It allowed people to live their life on their own terms without having to worry about debt payments continually. Since then, much time has passed, and economic crises have become more frequent. Loans are unavoidable and paying them off without emptying one’s savings is almost impossible. In fact, consumer debt remains one of the most significant challenges that today’s world is facing.

Household expenses have increased alarmingly over the last few years, leading to consumer debts:

With America’s total credit card debt continuing to rise at an alarming rate in 2017, financial analysts are saying that an average household that is trying to pay off a loan has a balance of $15,000. In fact, there has been almost 8% increase in credit card debts in 2017 as compared to last year. A recent study has found that on an average, households with debts owe around $130,000 to their loan providers.

It is indeed a matter of grave concern if the average households have to pay a considerable amount, which is almost equal to more than 8% of the entire household’s income, as debt interests. It is to be noted here that one of the main reasons that consumer debts have gone through the roof in recent years is because the cost of living has skyrocketed at an astounding rate. While most households are trying to increase their income every year, the expenses far outweigh the earning in most cases. Moreover, there has been a 50% increase in medical costs, and food and beverage costs have also increased by more than 35% in recent times. All these have happened in the same time frame, burdening people with more expenses for necessary provisions.

The situation is so terrible that at this stage, medical bills are the most prominent cause of bankruptcy in the United States of America. It is quite sad to know that some Americans are under so much financial pressure that they might not be equipped enough to tackle an emergency of a few hundred dollars.

In this bleak situation, some startups are providing people with the ray of hope. Skeptics may say that tech companies are looking for opportunities to profit from this unfortunate financial condition. However, that is not the case! These companies are genuinely trying to help individuals pay off their loans in a systematic manner and are building solutions to address them.

What are the types of loans that these startups are focusing on?

Mortgages – Mortgages top the consumer debt category, and some startups are leveraging consumer data to assess their creditworthiness as well as improve underwriting accuracy. They also offer better rates, lower borrowing costs and even offer extra credit options to qualified buyers.

Medical bills – As mentioned earlier, medical bills are the most significant reason for bankruptcy in the USA. Startups are now connecting error detection algorithms with a network of medical billing specialists to eliminate medical billing errors and overcharges, helping families to save significantly. These solutions can even scan past medical records and detect errors or inaccuracies, and also arrange for reimbursements.

Credit card debt– New companies are giving personal loans at lower interest rates to individuals based on their past and future earning potential. This potential does not depend on factors that impact credit score such as education, hobbies and so on.

Student loans– Some startups are helping students refinance as well as consolidate their loans. They also assist students in chalking out a monthly payment plan that would be in line with their career trajectory.

What is the next batch of startups going to do to address the situation?

While some startups have done a fantastic job in laying the foundation of the solutions that will potentially fix the consumer debt issue, experts think that this is just the beginning. More and more businesses will enter the space, and the next wave of startups will bring in even more user-centric convenience. They will primarily focus on helping individuals be on top of their debt obligation management process. These new solutions will also help one to scale and reshape their lifestyle to avoid getting into an unfavorable financial situation. From doing away with unnecessary subscriptions on credit cards to scaling down the expenditure on a daily or monthly basis – the possibilities are endless for these solutions. How will they do it? Well, with the help of Artificial Intelligence (AI), of course. AI-based solutions can scan invoices and receipts to stay updated about day to day expenses, provide the users with real-time information about their financial condition, optimize payments to ensure that no debt is ever missed and so much more.

Future income streams will play a critical role in managing debts:

One of the main reasons so many people have to put up with debt-related issues is that the interest on the loan builds up faster than you can repay the sum. Even if it may sound idealistic, one must consider that they can use their future income to reduce their current debt. This would solve quite a bit of the problem. Again, there would be cynics who would say that there have been suggestions of this before, so what guarantee is there that this would work this time? In reply, it can be said that this time, the sheer wealth of data can enable AI to accomplish what was previously unachievable. From a predictive analysis of finances to prescriptive suggestions of expenses, savings, and payments, AI can render debt management much more comfortable.

In conclusion, it can be safely stated that technology has empowered individuals with detailed knowledge and better control of their finances, and it has also made it easier to handle debts. The financial management industry has seen newer efficiencies with new products. As startups make collective effort to tackle the issues related to consumer debt, much support needs to be extended to the new entrepreneurs who are just starting out in this sector. Upon success, they can successfully alleviate some of the most significant stress-inducting factors from today’s economy.

Source: What Role Do Startups Play in Fixing Consumer Debt? Essentials You Need to Know!

Upon earning a Business Doctorate in Mid-Career: What it means and what can you do with it? #DataScience, #Doctoral Education Trends

Upon earning a Business Doctorate in Mid-Career: What it means and what can you do with it?

My Doctoral Experience: Part I (of a two-part article).

The purpose of this article is to explain to business professionals the scope of obtaining a doctoral degree; I will briefly discuss the distinct types of doctorates and discuss my experience in a practitioner-based program. Later, in this article, I discuss the benefits of achieving a doctorate and some of the potential career paths.

Introduction and Gratitude:

I am pleased to announce to my network that I recently completed my Doctorate in Marketing with my research concentration in Marketing Analytics. I had the great fortune of being able to earn a Doctorate in Business while working, which made the trials and tribulations of such a venture extremely challenging. The quintessential achievement of a doctorate means that you have mastered a subject entirely. With that said, I am forever grateful to Pace University for creating a program that is extremely rigorous and AACSB accredited and well rounded. Also, I had the great honor to have two very famous authors in analytics and loyalty marketing on my Dissertation committee: Dr. Tom Davenport and Dr. Terry Vavra. I am incredibly grateful for their participation on my committee. I highly recommend all the following practitioner books for those interested in analytics, CRM, digital marketing and customer satisfaction and engagement:

Top Books by Thomas Davenport

Competing on Analytics(Revised)

Analytics at Work

Only Humans Needs Apply

Enterprise Analytics

Big Data at Work

Keeping Up with The Quants

Top Books by Terry Vavra

The Customer Delight Principle

After Marketing

Loyalty Myths

Improving your measurement of Customer Sat

Customer Satisfaction Measurement Simplified.

Not being that familiar with doctoral programs, early on, I learned that earning a Doctorate in NYC adds to the competitiveness of the programs and the caliber of the students and faculty that you meet along the way. I benefited because many of the professors at Pace have their degrees from top schools and there is a level of competition for students between these Manhattan-based schools Pace, CUNY, Fordham and a variety of others. A doctoral program is about 20 courses or 60 credits. The cost of Doctoral programs can run from $75k to $150k depending on the program. The time to obtain a professional doctorate can range from 3 years to 7 years maximum. I finished in approximately five years. It is important to point out that while a majority of doctoral students complete their coursework, a smaller percentage finish the dissertation, mostly because of the motivation and self-discipline required to drive the writing of a manuscript mostly by yourself. So, for folks who hold a doctoral degree either Ph.D. or D.B.A or D.P.S. the level of rigor, research discipline and significant statistical knowledge that is acquired is a benefit and a significant differentiator over M.B.A. or M.S. holders who have become highly commoditized as of late. While many practitioner roles in marketing and analytics do not require more than MBA, I would encourage prospective employers to look at the level of statistical knowledge and evidence-based process that doctoral candidates learn about and how it may be beneficial for specific roles like the Chief Data Scientist or other quantitative and technical roles.

Alphabet Soup: Ph.D. (Research-Based Doctorate/Academic) versus D.B.A./D.P.S. (Practitioner or Executive Based on a Research Discipline).

Very often I am asked is one type of doctorate; academic versus practitioner better than the other and my point of view is it depends on what you are planning to do with it once achieved. When one is evaluating which path to choose it is vital to understand or try and forecast how you might use the doctorate when you finish. If your lifelong goal is to pursue a tenure-track academic career, then a research-based doctorate, like the Pace program, has some advantages over a purely practitioner doctorate. The fact is there is not a consensus in 2018 which one is better because it depends on many things, including what you are going to do with it. Most Ph.D. programs seek to prepare candidates for a career in academia to educate students and conduct research.

Career Paths after completing the Doctorate:

Some schools hire traditional tenure-track academicians, clinical professors (non-tenure track but usually former practitioners) and adjunct faculty (part-time faculty with full-time professional careers) solely based on holding a doctorate. Which path a student wants to take will often dictate the type of doctorate one will pursue. One thing to point out is that there is a wide variance in both Ph.D. and DBA/DPS degrees and much overlap, so some Ph.D. programs look more like DBA/DPS programs, and some DBA/DPS programs look more like Ph.D. programs.

Some professional doctoral programs such as DBA/DPS take a broader scope and breadth of their curriculum, for example, having students take courses in all aspects of the business from management, to finance(somewhat like an MBA and Ph.D. program combined). Said differently some professional doctorates take a multi-disciplinary approach that gives students a flavor of theory and research in a variety of functional areas while others allow them to specialize in one area. With this said, there had been much debate in practitioner and academic circles which program type is better, a purely research-based Ph.D. or an executive doctorate such as a DBA/DPS. To provide more clarification, it is essential to point out that some doctorates focus on research to create new knowledge while others on research to solve organizational problems by applying theory to practice. The former is more likely to focus on quantitative research methodologies which the latter are more likely to focus on qualitative research methodologies. Again, not all PhDs are one and all professional doctorates the other. One developing trend that is happening now is a merging of Professional Doctorates such as the DBA/DPS and Ph.D.’s in that Business doctorates whether professional or academic both now require original research and a full dissertation which is based on a research study written in a manuscript form.

AACSB Accreditation is more critical that the School Brand

What is most important in choosing a doctoral program, believe it or not, is not necessarily the schools brand name but whether the school is professionally accredited by the Premier Accrediting body which in the case of business is the AACSB, which Pace, CUNY, NYU, Fordham, and Columbia all have. Why does having the premier accreditation like the AACSB matter one might ask? It ensures a substantial level of rigor to the program and makes them somewhat more comparable across the board by creating standards for doctoral education. I also learned that accreditation could create a significant barrier to entry for those desiring to teach at an accredited university. For example, AACSB requires a certain percentage of faculty to have earned doctoral degrees and be productive scholars. This discourages schools from hiring full-time faculty who do not meet this requirement. Most Ph.D. programs and some professional doctorates require students to demonstrate the ability to conduct research and the skill to use a variety of multivariate statistical methods.

Components of any Doctoral Program

The goal of the students pursuing a doctorate is becoming a bonafide expert in a field. Doctoral programs require them to think about the world quite differently and in the framework of theory and research. Most doctoral programs have at their core the following elements.

– Reading and understanding the theory to become expert in one or more academic disciplines.

– Research methods courses that focus on research design, measurement, and evaluation.

– Advanced statistics courses that develop skill in applying techniques like analysis of variance, regression and correlation analysis, factor analysis, cluster analysis, and structural equation modeling.

– Conducting research and writing many papers in a chosen field (such as marketing). Some require developing an article for acceptance at academic conferences or publication in scholarly journals.

– Creating and defending a dissertation, which is similar to writing a treatise. Depending on the program, it can either develop and test theory to create new knowledge or apply theory and analytics to investigate and solve a practitioner problem. It demonstrates the expertise a student develops throughout the doctoral program and provides a platform for future interest and study.

Doctoral Program Requirements: My experience from Pace’s Executive Doctorate.

Ok, now the next question, what does one study in a Business Doctoral program, regardless of the assorted flavors and program names. Here are some of the elements and I would argue are quite common in academic doctorates and practitioner doctorates (PhDs, DBAs/DPS).

#1) All programs have a massive research component. The program at Pace had me reading, analyzing and critiquing over 150 journal articles in my chosen major/research area, which was Marketing and Analytics. These critiques were due every week, sometimes twice a week, so it was not unusual to be up after midnight to submit them online. In addition, to reading over 150 journal articles (and this was the kind of reading that could cure insomnia, for example very esoteric topics and very high-level concepts in addition to very detailed methodologies and testing results). The doctoral student must write between 2-3 research papers in every class they take during their coursework, which included reading between 15-30 articles to understand the theoretical basis for any article (Not light reading, the kind of reading you have to re-read like 3-5x to make sure you understood what the heck they were saying in some cases).

#2) Since a dissertation is in effect like writing a book it helps you build and demonstrate competency in your chosen filed. By building competence or expertise in a topical area and then identifying the dissertation, you can prove and defend that expertise. Mine was Marketing Analytics.

#3) Development of critical thinking through reading, summarizing and discussing the scholarly literature. Reviewing the literature enabled us to better understand research methodology and, we were required to comment on testing approach as well as having a point of view on the analysis.

#4) All reasonable programs prepare you for publishing research results in peer-reviewed, scholarly journals. As most good programs are emphasizing research, Pace’s program required a one-year publishing tutorial where the student is required to submit a paper to a national conference and ultimately a scholarly journal. This included co-authoring an article with a published author and learning SPSS, AMOS, SEM and Factor analysis and other supporting statistical analysis in excruciating detail including programming these tools oneself. I had the good fortune of having my paper accepted at a National Conference right out of the gate. This demonstrates the rigor and competitiveness of a doctoral program.

#5) Comprehensive Exam (Includes and Written and Oral Exam): A written – 6-hour exam on over 150 articles in your discipline (mine was marketing) and an Oral Exam: A 3-hour cross-examination (by two tenured faculties). The purpose of the examination is to certify your competence and currency in an academic discipline.

#6) You are invited to form your committee with 5 Ph.D.’s (3 internal to your school, 2 External Experts)

#7) Dissertation Proposal Approval: 3 hours and 3 Chapters of your manuscript. Intro, Lit Review, Methodology Proposal. Approval to collect data. The format is a 3-hour in-person presentation with the five members of your committee. Your committee provides you with valuable feedback and suggests alternative ways of approaching your methodology.

#8) Dissertation Final Defense Meeting: 3 hours with your committee. The entire manuscript including the Analysis and Results and Discussion, Directions for future research, implications for theory and practice. 150-200 page paper(“book”). Depending on the methodology may have dozens of analyses: factor analysis, ANOVA, mediation and moderation tests that all lead to proving a structural model and measurement model. Takes about a year or 2 to write this.

#9) If you are still alive after all this you get a piece of paper that says you are a Dr. This is a mammoth life-altering task and is the pinnacle of academic achievement.

The following are the course that one takes over the five years to complete the doctorate.

Coursework by Year

Year 1

Fall

Elective

Doctoral Foundation Seminar in Management

Explorations in Business Research

Spring

Elective

Doctoral Foundation Seminar in Finance and Economics

Doctoral Foundation Seminar in Marketing

Year 2

Autumn

Regression Analysis

Publishing Tutorial 1

Spring

Elective

Selected Topics in Multivariate Analysis

Publishing Tutorial 2

Year 3

Autumn

Concentration Seminar

Research Design and Measurement

Doctoral Concentration Seminar in

Corporate Finance (FIN 821) or

Consumer Research (MAR 831) or

Organization Behavior (MGT 835)

Spring

Concentration Seminar

Doctoral Foundation Seminar in Cross Cultural Management

Doctoral Concentration Seminar in

Capital Markets (FIN 822) or

Marketing Management (MAR 832) or

Strategic Management (MGT 836)

Year 4

Autumn

Dissertation Seminar 1

Candidate passes doctoral comprehensive examination

Doctoral Program appoints a dissertation committee

Spring

Dissertation Seminar 2

Candidate presents dissertation proposal to committee for approval

Year 5

Autumn

Dissertation Seminar 3

Candidate collects and analyzes empirical data

Spring

Dissertation Seminar 3

Candidate completes and defends a dissertation

Explaining the Value of a Doctorate:

Doctoral Degree benefits for careers in Academia or as an academic practitioner.

The doctorate brings more credibility to academic research. The credibility is clearly based on the fact that research doctorates inculcate the knowledge and skill necessary to conduct research worth publishing. While not the primary intent a doctorate may lend credibility to consumers of professional books, if they see that the book is written in a manner that is well researched by a doctor. The doctorate does distinguish you from your colleagues as it is a rare degree in the U.S. The next benefit is to acquire research skills and to be competent observers of the world and doing analysis and applying them to practical problems. It does remain to be seen if access to jobs is increased, but in data science and specific fields, doctoral marketing holders are often preferred given the statistical training of doctors.

How does a Doctorate Help a Consulting Practice?

While the doctorate helps with providing expanded opportunities in teaching, it also allows autonomy to do consulting. Having a doctorate lends credibility to a consulting practice or in my case a Chief Analytics role or a CMO role in a corporation. While not required, very often clients want to send their PhDs to talk with you (doctoral holder) in the event you are working on any projects that are methodology driven so that they can review the work and ensure senior management that the consultant’s practice is sound. Clients and firms have a higher regard for the consultant if they know they have a doctorate as they very often understand the rigor of going through such a program and they know that they can expect detailed research and analysis and often a more value-added focus.

My Dissertation, Articles, Plans for Future Research

In conclusion, I will briefly discuss my dissertation topic, the expertise I developed and my research plans. My dissertation topic was regarding Marketing Analytics. Marketing Analytics is a relatively new but increasingly prominent field in which data tools are applied to quantify and monitor marketing performance and customer information to optimize investments in marketing programs and maximizing customer interaction. My dissertation is a B2B study, in which I established a set of predictors that help determine the degree to which a firm’s marketing function is analytically driven. The research builds on extant theories of market orientation by establishing the presence of a new construct known as Marketing Analytics Orientation (MAO) through qualitative and quantitative research methods including factor analysis and structured equation modeling. Firms in the study are scored on the MAO Index, and the characteristics of the more analytical firms are discussed. Furthermore, the study explores the relationship between the factors that comprise Marketing Analytics Orientation (MAO) and Marketing Performance (MP). I am in the process of publishing various parts of this study in scholarly journals. The study provides the opportunity for future research into the drivers of how analytical marketing organizations are and the impact analytics has on marketing performance and other campaign key performance indicators and market level performance indicators such as company stock price.

Please check back here if you are interested in reading my Dissertation on the adoption of Marketing Analytics or other related articles. This will take some time to appear as I am working on publishing now.

I hope this addresses the universal questions of why pursue a doctorate and its’ potential benefits aligned to several career paths.

In future articles and Blogs, I will explore ways in which Industry and Academia can further align for success. (Part II of this series on Education)

Sincerely,

Dr. Tony Branda

Source by tony

Apr 05, 18: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

image
Tour of Accounting  Source

[ AnalyticsWeek BYTES]

>> Uber: When Big Data Threatens Local Democracy by analyticsweekpick

>> The Business of Data by analyticsweekpick

>> Creating Your Own Threat Intel Through ‘Hunting’ & Visualization by analyticsweekpick

Wanna write? Click Here

[ NEWS BYTES]

>>
 Scala’s Intelligent Visual and Consumer Engagement Solutions on … – MarTech Series Under  Marketing Analytics

>>
 The best content marketers think like data scientists – TechHQ – TechHQ Under  Social Analytics

>>
 Cloudera brings the Shared Data Experience to its machine learning … – SDTimes.com Under  Streaming Analytics

More NEWS ? Click Here

[ FEATURED COURSE]

R, ggplot, and Simple Linear Regression

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Begin to use R and ggplot while learning the basics of linear regression… more

[ FEATURED READ]

Research Design: Qualitative, Quantitative, and Mixed Methods Approaches, 4th Edition

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The eagerly anticipated Fourth Edition of the title that pioneered the comparison of qualitative, quantitative, and mixed methods research design is here! For all three approaches, Creswell includes a preliminary conside… more

[ TIPS & TRICKS OF THE WEEK]

Analytics Strategy that is Startup Compliant
With right tools, capturing data is easy but not being able to handle data could lead to chaos. One of the most reliable startup strategy for adopting data analytics is TUM or The Ultimate Metric. This is the metric that matters the most to your startup. Some advantages of TUM: It answers the most important business question, it cleans up your goals, it inspires innovation and helps you understand the entire quantified business.

[ DATA SCIENCE Q&A]

Q:Do you know / used data reduction techniques other than PCA? What do you think of step-wise regression? What kind of step-wise techniques are you familiar with?
A: data reduction techniques other than PCA?:
Partial least squares: like PCR (principal component regression) but chooses the principal components in a supervised way. Gives higher weights to variables that are most strongly related to the response

step-wise regression?
– the choice of predictive variables are carried out using a systematic procedure
– Usually, it takes the form of a sequence of F-tests, t-tests, adjusted R-squared, AIC, BIC
– at any given step, the model is fit using unconstrained least squares
– can get stuck in local optima
– Better: Lasso

step-wise techniques:
– Forward-selection: begin with no variables, adding them when they improve a chosen model comparison criterion
– Backward-selection: begin with all the variables, removing them when it improves a chosen model comparison criterion

Better than reduced data:
Example 1: If all the components have a high variance: which components to discard with a guarantee that there will be no significant loss of the information?
Example 2 (classification):
– One has 2 classes; the within class variance is very high as compared to between class variance
– PCA might discard the very information that separates the two classes

Better than a sample:
– When number of variables is high relative to the number of observations

Source

[ VIDEO OF THE WEEK]

#BigData @AnalyticsWeek #FutureOfData #Podcast with @MPFlowersNYC, @enigma_data

 #BigData @AnalyticsWeek #FutureOfData #Podcast with @MPFlowersNYC, @enigma_data

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[ QUOTE OF THE WEEK]

Data beats emotions. – Sean Rad, founder of Ad.ly

[ PODCAST OF THE WEEK]

@TimothyChou on World of #IOT & Its #Future Part 1 #FutureOfData #Podcast

 @TimothyChou on World of #IOT & Its #Future Part 1 #FutureOfData #Podcast

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[ FACT OF THE WEEK]

Decoding the human genome originally took 10 years to process; now it can be achieved in one week.

Sourced from: Analytics.CLUB #WEB Newsletter

@BrianHaugli @The_Hanover ‏on Building a #Leadership #Security #Mindset

[youtube https://www.youtube.com/watch?v=XoEPadDSp6E]

In This podcast Brian Haugli from The Hanover Insurance Group sat with Vishal to talk about some of the security led leader’s mindset. From discussing some of the leadership mindset to practitioner tactical guide to help future security leaders to understand how to secure their organization. This session is great for any security passionate leader willing to create a security wrapped growth mindset.

Brian’s Read Recommendation:
On The Road by Jack Kerouac http://amzn.to/2hMhOhG

Podcast Link:
iTunes: http://math.im/itunes

GooglePlay: http://math.im/gplay

Brian’s BIO:
Brian Haugli is a Certified Information Systems Security Professional (CISSP) and a Global Industrial Cyber Security Professional (GICSP). Brian previously served as a senior advisor on cyber security and information risk management for the Department of Defense, US Army ITA, and Pentagon. He has 20 years of professional experience and expertise in network topologies, design, implementation, architecture, and cyber security. He has extensive knowledge of and has implemented risk management frameworks, methodologies, and processes. He has been responsible for creating compliant and secure networks for multiple sites through his extensive background in intrusion detection and full network end-to-end testing. He has outstanding communication skills, a positive demeanor, and the ability to interface with all levels of an organization.

About #Podcast:
#FutureOfData podcast is a conversation starter to bring leaders, influencers and lead practitioners to come on show and discuss their journey in creating the data driven future.

Wanna Join?
If you or any you know wants to join in,
Register your interest @ http://play.analyticsweek.com/guest/

Want to sponsor?
Email us @ info@analyticsweek.com

Keywords:
FutureOfData
Data
Analytics
Leadership Podcast
Big Data
Strategy

Originally Posted at: @BrianHaugli @The_Hanover ‏on Building a #Leadership #Security #Mindset by v1shal

Apache Spark for Big Analytics

Apache Spark is hot.   Spark, a top-level Apache project, is an open source distributed computing framework for advanced analytics in Hadoop.  Originally developed as a research project at UC Berkeley’s AMPLab, the project achieved incubator status in Apache in June 2013 and top-level status in February 2014.

Spark seeks to address the critical challenges for advanced analytics in Hadoop.  First, Spark is designed to support in-memory processing, so developers can write iterative algorithms without writing out a result set after each pass through the data.  This enables true high performance advanced analytics; for techniques like logistic regression, project sponsors report runtimes in Spark 100X faster than what they are able to achieve with MapReduce.

Second, Spark offers an integrated framework for advanced analytics, including a machine learning library (MLLib); a graph engine (GraphX); a streaming analytics engine (Spark Streaming) and fast interactive query tool (Shark).   (Update:  Databricks recently announced Alpha availability of Spark SQL).   This eliminates the need to support multiple point solutions, such as Giraph, and GraphLab for graph engines; Storm and S4 for streaming; or Hive and Impala for interactive queries.  A single platform simplifies integration, and ensures that users can produce consistent results across different types of analysis.

spark-project-header1-cropped

At Spark’s core is an abstraction layer called Resilient Distributed Datasets, or RDDs.  RDDs are read-only partitioned collections of records created through deterministic operations on stable data or other RDDs.  RDDs include information about data lineage together with instructions for data transformation and (optional) instructions for persistence.  They are designed to be fault tolerant, so that if an operation fails it can be reconstructed.

For data sources, Spark works with any file stored in HDFS, or any other storage system supported by Hadoop (including local file systems, Amazon S3, Hypertable and HBase).  Hadoop supports text files, SequenceFiles and any other Hadoop InputFormat.

Spark’s machine learning library, MLLib, is rapidly growing.   In the latest release it includes linear support vector machines and logistic regression for binary classification; linear regression; k-means clustering; and alternating least squares for collaborative filtering.  Linear regression, logistic regression and support vector machines are all based on a gradient descent optimization algorithm, with options for L1 and L2 regularization.  MLLib is part of a larger machine learning project (MLBase), which includes an API for feature extraction and an optimizer (currently in development with planned release in 2014).

GraphX, Spark’s graph engine, combines the advantages of data-parallel and graph-parallel systems by efficiently expressing graph computation within the Spark framework.  It enables users to interactively load, transform, and compute on massive graphs.  Project sponsors report performance comparable to Apache Giraph, but in a fault tolerant environment that is readily integrated with other advanced analytics.

Spark Streaming offers an additional abstraction called discretized streams, or DStreams.  DStreams are a continuous sequence of RDDs representing a stream of data; they are created from live incoming data or generated by transforming other DStreams.  Spark receives data, divides it into batches, then replicates the batches for fault tolerance and persists them in memory where they are available for mathematical operations.

Currently, Spark supports programming interfaces for Scala, Java and Python.  For R users, the team at Berkeley’s AMPLab released a developer preview of SparkR in January.

There is an active and growing developer community for Spark; 83 developers contributed to Release 0.9.  In the past six months, developers contributed more commits to Spark than to all of the other Apache analytics projects combined.   In 2013, the Spark project published seven double-dot releases, including Spark 0.8.1 published on December 19; this release included YARN 2.2 support, high availability mode for cluster management, performance optimizations and improvements to the machine learning library and Python interface.  The Spark team released 0.9.0 in February, 2014, and 0.9.1, a maintenance release, in April, 2014.  Release 0.9 includes Scala 2.10 support, a configuration library, improvements to Spark Streaming, the Alpha release for GraphX, enhancements to MLLib and many other enhancements).

In a nod to Spark’s rapid progress, Cloudera announced immediate support for Spark in February.   MapR recently announced that it will distribute the complete Spark stack, including Shark (Cloudera does not distribute Shark).  Hortonworks also recently announced plans to distribute Spark for machine learning, though it plans to stick with Storm for streaming analytics and Giraph for graph engines.  Databricks offers a certification program for Spark; participants currently include Adatao, Alpine Data Labs, ClearStory and Tresata.)

In December, the first Spark Summit attracted more than 450 participants from more than 180 companies.  Presentations covered a range of applications such as neuroscience, audience expansion, real-time network optimization and real-time data center management, together with a range of technical topics.  The 2014 Spark Summit will be held in San Francisco this June 30-July 2.

In recognition of Spark’s rapid development, on February 27 Apache announced that Spark is a top-level project.  Developers expect to continue adding machine learning features and to simplify implementation.  Together with an R interface and commercial support, we can expect continued interest and application for Spark.   Enhancements are coming rapidly — expect more announcements before the Spark Summit.

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Mar 29, 18: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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Fake data  Source

[ AnalyticsWeek BYTES]

>> Caterpillar’s Next Dig: Big Data by analyticsweekpick

>> Oct 19, 17: #AnalyticsClub #Newsletter (Events, Tips, News & more..) by admin

>> Getting to Love: Customer Word Clouds by bobehayes

Wanna write? Click Here

[ NEWS BYTES]

>>
 Introducing New Data Center Worldisms – Data Center Knowledge Under  Data Center

>>
 Location Data in Your Salesforce CRM: CARTO Brings Geospatial Data and Analytics to Salesforce Einstein Analytics – MarTech Series Under  Sales Analytics

>>
 UCHealth Yampa Valley Medical Center recognized for overall excellence in quality and patient satisfaction – Steamboat Pilot & Today Under  Health Analytics

More NEWS ? Click Here

[ FEATURED COURSE]

Python for Beginners with Examples

image

A practical Python course for beginners with examples and exercises…. more

[ FEATURED READ]

On Intelligence

image

Jeff Hawkins, the man who created the PalmPilot, Treo smart phone, and other handheld devices, has reshaped our relationship to computers. Now he stands ready to revolutionize both neuroscience and computing in one strok… more

[ TIPS & TRICKS OF THE WEEK]

Fix the Culture, spread awareness to get awareness
Adoption of analytics tools and capabilities has not yet caught up to industry standards. Talent has always been the bottleneck towards achieving the comparative enterprise adoption. One of the primal reason is lack of understanding and knowledge within the stakeholders. To facilitate wider adoption, data analytics leaders, users, and community members needs to step up to create awareness within the organization. An aware organization goes a long way in helping get quick buy-ins and better funding which ultimately leads to faster adoption. So be the voice that you want to hear from leadership.

[ DATA SCIENCE Q&A]

Q:How to detect individual paid accounts shared by multiple users?
A: * Check geographical region: Friday morning a log in from Paris and Friday evening a log in from Tokyo
* Bandwidth consumption: if a user goes over some high limit
* Counter of live sessions: if they have 100 sessions per day (4 times per hour) that seems more than one person can do

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[ VIDEO OF THE WEEK]

#BigData @AnalyticsWeek #FutureOfData #Podcast with  John Young, @Epsilonmktg

 #BigData @AnalyticsWeek #FutureOfData #Podcast with John Young, @Epsilonmktg

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[ QUOTE OF THE WEEK]

I keep saying that the sexy job in the next 10 years will be statisticians. And I’m not kidding. – Hal Varian

[ PODCAST OF THE WEEK]

#BigData @AnalyticsWeek #FutureOfData #Podcast with Nathaniel Lin (@analytics123), @NFPA

 #BigData @AnalyticsWeek #FutureOfData #Podcast with Nathaniel Lin (@analytics123), @NFPA

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[ FACT OF THE WEEK]

Every person in the world having more than 215m high-resolution MRI scans a day.

Sourced from: Analytics.CLUB #WEB Newsletter

ADP Launches Its Own Big Data Analytics Cloud Platform

ADPlogo.290x195
Automatic Data Processing—better known as ADP, the payroll service to much of the world—went into the big data platform business on May 12. And why not, with more than 600,000 client businesses and 24 million employees in the U.S. alone from which to access piles of metadata.
Roseland, N.J.-based ADP is the payroll accounting service that uses its aggregate metadata to generate a monthly jobs report that is respected by many economists. In fact, because it is the largest payroll processor in the nation, ADP’s vast big data silo from U.S. companies and their employees ranks second only to the employee records of the U.S. federal government.
So ADP has started to use all that data for a new business wing: a big data-based cloud service. ADP DataCloud is designed to put day-to-day analytics capabilities in the hands of line-of-business accounting and HR staff members, enabling them to obtain insights from the workforce data already embedded in their individual ADP human capital management systems.
ADP DataCloud aims to boost business and workforce management goals, such as workforce productivity, talent development, retention and the identification of flight risks. More than 1,000 ADP clients already are using these analytics capabilities.
Exploring the Trends Impacting the Modern Data Center Download Now
ADP DataCloud provides companies with critical information that can help answer key questions facing not only HR, but the overall business. A consumer-grade user experience blended with analytics allows clients to obtain deep enterprise insights across the organization’s HCM data. The embedded big data platform is within ADP’s core solutions, including ADP Vantage HCM, Enterprise HR, ADP Workforce Now and ADP Time & Attendance.

Key features include the following:
Benchmarking: Offers companies the ability to compare HCM metrics with an aggregated and anonymous market benchmark at the industry, location and job-title level to inform key workforce decisions.
Data exchange: Provides companies with the ability to combine workforce data with other types of business data, such as sales or customer satisfaction scores, from non-ADP platforms to identify key deeper business insights and actions.
Predictive analytics: Utilizes predictive models derived from ADP data to help employers make smarter, forward-looking workforce decisions by providing insight into the likelihood of specific workforce management outcomes. The first capability helps employers identify those employees likely to leave their organization.
A new research project commissioned by ADP found that 75 percent of companies with 1,000 or more employees have access to data to inform business decisions, but only 46 percent are using workforce analytics capabilities to improve business decision making. The same study found that 42 percent of company finance executives and 32 percent of midlevel managers want to utilize workforce analytics.
The study consisted of a 2015 survey of 300 HR executives, finance executives and managers at companies with 1,000+ employees.

Originally posted at: http://www.eweek.com/enterprise-apps/adp-launches-its-own-big-data-analytics-platform.html

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