M2 Data

Minority students are under represented in advanced degrees for data science and related disciplines. The most talented high school STEM students could be accepted directly into a Master's program, eliminating undergraduate debt, undergraduate graduation risk and finding a job after graduation. A group of 5-10 corporations beginning in Miami could fund the cost of the degree plus a stipend for 10-20 students per year. New cities with high concentrations of large corporations could be added each year. Participating in such a prestigious program would increase student self-esteem, shorten from six to two years the time to complete a Masters and start earning a high salary and position them in the most in demand professions for the 21st century. 

According to Forbes, among all technical fields, data science had the lowest percentage of African American, Latino/Hispanic, women and disabled individuals (URM) enrolled in full-time or part-time university programs or employed full time. Renown forecasters such as BCG, WEF and DT all predict that the biggest shortage of qualified future job applicants will be in data science, but URM students are unlikely to receive the education to prepare them for these data science jobs.

This problem is exacerbated by the comparatively low undergraduate graduation rates, 55.4 percent for black students and 65 percent for Latinos, according to INSIDE HIGHER ED. However, high quality education is increasingly shifting to online solutions, shorter format learning and the availability of leading university courses for anyone who can pay.

With leading companies like Google announcing that undergraduate degrees are no longer a job requirement, we have a convergence of economic, technological and cultural factors to permit disruptive new ideas. 

1. Visit 40-50 large local corporations and foundations to secure 2-3 year budget funding for the local program, local working space for students, job commitments and volunteer mentors (1:1).
2. Secure one accredited Master's degree institution as partner, similar to WorldQuant program. Design curriculum to be completed in 24 months.
3. Visit local public high schools to promote the program to high potential students and explain data science/jobs, the application requirements and course pre-reqs. Explain where students can take free online courses to meet requirements. Pass out link to apply. Do follow up visits to work with interested students, help them pick pre-requisite high school courses and prepare to apply. 
4. Select students based on applications and interviews at the end of 11th grade so they have a guaranteed degree program after graduation. Work with students on Saturdays to complete pre-reqs in math, statistics and programming, as required. 
5. Upon graduation, begin Master's degree with course lengths matched to curriculum. Students also do internships in data science related fields at sponsor corporations or the community, which allows parties to get to know each other.
6. Students graduate Master's program, are selected for jobs and start work. Graduates help with high school promotion.
7. Add new cities

The M2 Data program serves URM high school students in metropolitan public high schools. We begin to interact with them in 10th grade and look to give them a program commitment for an accredited advanced degree in data science by the end of 11th grade. Such an approach eliminates the anxiety of applying to university and the related financial burden, reduces their risk to complete a degree by offering a two year master's compared to the traditional 4+2 approach, positions them with credentials for the 21st century and continued employment and increases their self-esteem by being accepted into an exclusive program.

For the last five years the M2 Data team has worked at one of the largest minority serving universities in the U.S. We have taught traditional courses, workshops and badges and offered incubators and accelerators for experiential learning. Most importantly we have helped these students to increase their self-esteem and self-confidence so that they can compete with students from Harvard and Stanford to secure acceptance to PhD programs such as Georgia Tech and Northeastern and jobs at companies--Microsoft, Amazon and the skunkworks at Martin Marietta. These students have also worked with us to develop this proposal. 

We believe we are very responsive to the requirements for the Challenge:

1. We reimagine the Master's in Data Science
2. We offer a high value, accredited degree that matches high demand employer job requirements
3. M2 Data targets URM metropolitan high school students
4. The program pays a stipend to students to motivate to complete the program rather than incurring student debt.
5. The program provides an accredited Master's in two years, which reduces graduation risk (by eliminating duplicate courses, matching course length to curriculum rather than scheduling and using internships to achieve degree requirements).
6. The program provides critical first jobs with corporate sponsors

M2 Data is designed from the beginning to scale nationwide to 20-25 cities that have the following characteristics:

1. Large group of F1000 corporations and foundations
2. Large black and Latin populations
3. Universities that can provide coaches, mentors, interns and adjunct faculty

Miami is where the team has worked on a social entrepreneurship program targeting undergrads for the last five years. The university is one of the largest minority serving institutions in the U.S. Miami is also one of the ten largest cities in the U.S. in terms of both black and Latin populations. We believe Miami offers the opportunity to prototype the program to successfully appeal to both black and Latin high school students.

The next city to target for expansion is Atlanta, GA which has large minority populations, strong universities (Georgia Tech and Emory), 34 F1000 companies and active large foundations such as the Goizueta Foundation (where we have contacts).

We believe the key to scaling is to build the corporate commitment to funding and jobs. Foundations would also be a key factor in picking cities. In five years, with the necessary funding, M2 Data plans to be in ten cities, graduating twenty students per city per year. More optimistically, a city could graduate fifty URM students per year.

Key assumptions for the program:

• Problem--finding an accredited university to offer degree
• Solution--finding sufficient corporate sponsors and guaranteed jobs by city
• Scaling--expansion funding to secure new cities on schedule

De-risking these assumptions beginning in Miami is the key program objective.

The team currently includes the Founder, the CEO part time and three people have volunteered time to support concept development and the application.

Founder--built billion dollar company in seven years, COO/CFO MIT One Laptop per Child project, started high school entrepreneurship incubator program, started four other university incubators targeting undergraduates.

CEO--Latin woman, Masters in ______, two startups, currently Director of Student Entrepreneurship at a Florida university.

Three volunteers include professionals who are black and latin, female, 10-year Teach for America staff, Edtech startup and cruise line startup experience.

Four team members are bi-lingual English-Spanish.

Our team has experience building a diverse team. In the last team of sixteen, there were 11 women, 5 men, 4 whites, 1 Asian, 8 Latins and 3 blacks. What this team represented was simply the best people who applied. The key is to message well in job postings so the greatest number of qualified people apply. Then you simply pick the smartest, honest, self-motivated people who can work well in teams. 

"Diversity improves collaboration" is well documented. Hire for the fewest requirements. Motivate the team and leadership to cross train as a simple way to foster skills and social inclusion which reduces the opportunity for discrimination.

Equity is achieved by having a fixed pay range for 3-4 position levels (staff, junior professional, senior professional, executive). Everybody starts at the bottom of a pay range and then can receive merit raises. The simplicity of this compensation scheme fosters equity.   

Society in the last 10 years has embraced new alternatives to traditional secondary education. edX, OCW, Coursera, Khan and others are examples. Many found their impetus to change the traditional university model but few have rethought the need for the traditional four year undergraduate degree followed by a two year Master's. The existing model duplicates courses, teaches every course in the same X-week format, focuses on classroom and not experiential learning and uses professors as lecturers and not facilitators. 

Our innovation focuses on a change in process. McKinsey's ability to teach an MBA in fourteen days might be a data point for our approach.

Our proposal:

• A two year master's degree because employers care more about job skills, not so much 18th century literature; no prior undergraduate; 12 month programming. 
• No duplication, you learn Calc 1 and 2 well once.
• Course length matches time required to teach required fundamentals, which provides time for deep dives where critical
• All lectures are in online course videos assembled from MIT, CMU, Stanford, etc. Team problem sets and deliverables are reviewed in class with the instructor to foster application of the CS, data science and engineering concepts.
• Students do an internship related to data science each semester either at a corporate sponsor or a local social venture.
• Supplemental workshops explore the 4Cs, decision making, mental models and complimentary subjects.
• Data analytics would be developed on students to improve selection, pedagogy and learning alternatives and to report out to corporate and foundation sponsors. 

The core technology is a digital online learning platform that can support programs in multiple cities, for multiple courses and multiple types of learning activities. The platform can seamlessly serve up videos, syllabi, readings, assignments and any other course information a student would need, as well as info on an internship, job opportunity, subject tutor, coach, guest lecture or event.This platform would also be designed to automate reporting to sponsor corporations and foundations and to document student progress toward their degree.  

The core technology would also include a ML engine to provide analytics and predictive analytics on students in the program to enhance the selection process and student performance through refinements to curriculum, teaching methods, instructors and events. 

The technology for the learning platform and the analytics will begin with open source code probably from GitHub and the plan would be to further develop required functionality as open source. A key requirement will be database design, which may restrict the use of off the shelf products such as Canvas. The importance of UI/UX for all the different user groups might also argue against existing commercial products. A significant shortcoming in current learning systems is they are designed for instructors and not the students. One possibility would be to co-develop the platform and receive royalties from the commercialization success of the corporate partner. Maybe IBM would be interested in a new LMS.

The proposal is for a new process to provide a Master's degree. The process is the invention which serves as the "technology". Technically, the technology is likely all proven and off the shelf open source code. The algorithms for predictive analytics in student selection, pedagogy improvement and teacher performance would be proprietary but not unique.

The pedagogy is a flipped classroom approach, review lectures at home and do problems in class with instructor. Novel but not unique. Complimenting learning with coaches and mentors makes it distinctive but not original.

The WorldQuant Master's in Financial Engineering was the motivation for the proposal. WorldQuant is a private hedge fund that offers several free Master's degrees in data science. Airbnb's internal Data University for employees was another earlier inspiration for the proposal and confirmation from industry experts on the widespread need for data science education. The Founder's fifteen years of teaching at FIU, MIT Sloan (IAP) and UM undergraduate and graduate courses in engineering and social entrepreneurship also helped to shape the solution.

Outlier is another inspiration, offering low cost university courses by professors from top universities such as Yale, CMU, Columbia. Outlier might provide the ability to outsource curriculum. Outlier also demonstrates the component approach where we could focus on local adjunct faculty, coaches mentors and internships to increase the likelihood of program completion.

GradSchools.Com also reports eleven well-known universities, including Johns Hopkins and Brandeis, that offer Master's degrees without a Bachelor's.

Data science is a key technology for the 21st century and one of the largest opportunities for new jobs. URM students are under represented in university degree programs for data science and the related professional positions. Therefore, without intervention URMs may miss out on the new opportunity for high paying jobs in a field not likely to be displaced by artificial intelligence.

URM students are currently challenged to get the necessary degree(s) in data science by low participation in STEM courses, financial challenges to stay in school and resultant lower graduation rates, lack of mentors and lack of access to information about the most promising careers. As is the case with most social problems, an asymmetry of information is the root cause. By targeting students in 10th grade, the program creates the opportunity to shape the students curriculum, mentor them and show them a future working in data science. The opportunity to immediately pursue a Master's, start earning a good salary two years after high school graduation (debt free) and begin financially helping their families is a compelling opportunity. The shorter degree  reduces the risk of life's problems interfering with graduation and the free master's reduces all the financing challenges. A smallmonthly stipend provides for the student's daily expenses.

The program will attract students who can master math, CS and engineering, the same talent required for doctors, engineers and other important professions badly needed in minority populations. The lure of a full-time job two years after high school graduation will be nearly irresistible, but do the students have the necessary maturity for the workplace. Coaches and mentors during and after the program will help with this issue.

Education at all levels in the U.S. was designed in the early 1800s and rarely updated until home schooling, charter schools and edX led the change. A four year undergraduate degree matches the economic requirements of universities and the status quo thinking of academics. That is why the online programs have had so much popularity in the last ten years. We believe that a Master's in Data Science can be successfully taught to high potential minority students. Much of an undergraduate degree is repeated in a Master's. We preserve the Master's degree so the students cannot be discriminated against for lack of an advanced degree. 

If you look at the program in terms of students, in five years at most the program will operate in ten cities and graduate 500-1000 students annually. A point of pride for the program and personally uplifting and esteem building for each graduate.

However, the opportunity comes really in redefining university education and in showcasing the exceptional minority students who completed the program. High schools are now commonly teaching undergraduate courses, which are then repeated in university and then graduate school. Some high schools even have formal programs with universities. Why not recognize this trend as the opportunity for compression in education. We can achieve an undergraduate degree in two years if high school curriculum is structured to this end. If we offer more math, statistics and CS in high school we believe a student could go directly from high school to a Master's. The impact and scale of M2 Data is to stimulate the redefinition of university education in the U.S. by shortening the time frame, lowering the cost, using modern digital technology, focusing education on 21st subjects such as science, math and computation and disrupting the public education system to support faster university degrees.

Since the 1960s and Head Start, there have been few national programs that showcased the talent and potential of minority students. While many scholarships exist, nothing currently draws attention nationwide to the exceptional university minority students. M2 Data believes this objective is an important part of the mission of the program.

To launch the program in the next year, the following are key objectives:

• Secure accredited institution to issue the Master's degree using any one of multiple providers of curriculum
• Secure Miami corporations and foundations to fund the program, provide internships and jobs after graduation
• Create relationship with Miami Dade Public Schools to create a student pipeline starting in 10th grade
• Market the program, high school curriculum requirements and coaching to secure 100 interested students, such that at least ten enter program Year 1.
• Achieve credibility in minority communities for excellence, caring and empowerment of the students
• Build basic IT infrastructure for student, curriculum and administrative programs, using existing open source code bases

In five years the key challenges will be:

• To successfully operate a multi-city program at a consistent level of excellence such that graduation rates for degrees approach 100%.
• Document and promote a reputation for excellence, caring and empowerment of the students
• Establish an engaged alumni program to mentor and promote the program
• Continue to innovate around technology and pedagogy to sustain excellence
• Offer training so corporate partners can attract the students
• Expand multi-city corporate partners and foundations to accelerate nationwide expansion
• Consider establishing a GED program to feed high potential students to the program or offer a second Master's degree such as Advanced Manufacturing

The Founder has worked in universities for fifteen years. To find a partner for the accredited degree, we plan to approach World Quant to partner or universities already offering a master's without a previous degree.

In the last ten years the Founder has secured ~$5 million in grants and such experience includes corporation and foundation donors.

We have innovated around pedagogy and student learning groups at a large MSI university for five years.

An existing program with Miami Dade Public School for a high school incubator program established a relationship with the innovators in the school district.

Founder scaled a business from $40 million to $1 billion in annual revenue and IPO. We know how to scale an operation. However, real scale will be achieved by expanding the number of degree programs matched to corporate job needs.

Key outcome will be energizing the corporate community to prepay for qualified candidates and appreciate the need for a new model to attract URM students.

The CEO is an immigrant, minority woman who is a self-taught programmer and data analytics user. She has worked with undergraduate students for five years in an experiential learning program for entrepreneurship. Her life is summarized by social impact and education.

The Founder is the former COO/CFO of One Laptop per Child and sold one million laptops to governments around the world in 3+ years. He co-taught the first IAP course in social entrepreneurship at MIT Sloan for seven years. He also built a publicly-traded billion dollar company in Indonesia in seven years. Education is his personal and professional focus.

Third team member is expected to be an African-American woman, former ten year veteran of one of the most well-known high school support programs in the U.S.

Fourth team member is expected to be a Colombian woman and an example of the target student for the program--community college, overachiever held back by asymmetry of information, naturally inclined to engineering and data analytics. 

We are still in stealth mode.

We provide a two year Masters degree in Data Science with a guaranteed corporate job at completion, debt free to the student. We pay a stipend to the students to cover their living expenses. No undergraduate degree in advance is required. All students are URM.

The courses use a flipped classroom where the students use remote video learning to cover lecture materials and in classroom for experiential learning and problem solving with instructors and mentors. 12-month yearly instruction, holiday breaks, for twenty-four months. Courses are supplemented by 2-3 internships with multiple corporate sponsors to showcase the students and provide early preparation for work skills and work environment.Guest speakers complete the learning. Digital LMS such as Moodle would be used.

The metric is the number of degreed students that join the corporate sponsors in full-time data science positions within two years of starting the program and their willingness to mentor and promote the program after graduation. Students that are corporate sponsored to pursue a PhD is a bonus metric, but not a declared objective. Jobs is the metric.

Profits and capital raised first go into expanding the city network and secondly into adding new Masters degrees in high potential domains.

Initial funding would come from corporate sponsors. Follow on strategy is through grants from non-profits and perhaps the NSF who supports innovation in education. Initially foundations would be leaders in the specific cities, such as Knight Foundation in Miami or Charlotte. Over time, the standing of the foundations would hopefully increase to the national funders such as Kellogg or Rockefeller.

At the time of the launch of the second degree, approximately year five, the program would have a demonstrated business model and traction sufficient to attract social impact VCs. The Founder has raised over $1.2 billion in capital in his career, including $60 million in venture capital. Some equity ownership would be given to the original foundations as compensation for their initial funding.

Fund raising would be constant, focusing on corporate student sponsors, foundations and VCs. Capital must always be raised 2-3 years in advance of need. Corporates would be targeted for minimum $50,000, foundations $250,000+ and VCs $1-2 million when traction is documented. Select social impact VCs would be briefed on the program from the start and updated.

Estimated expenses are $250,000 per year of which $200,000 is for salaries and $50,000 is for administrative and infrastructure startup. An override on the corporate contributions of $10,000 per student would cover the build out and update of the LMS infrastructure and course content.An estimated $30-40,000 of corporate student sponsorship would cover annual costs to deliver the program and pay student stipends.

The MIT Solve Challenge itself and the participating sponsors provide immediate legitimacy for the proposal with corporate sponsors. M2 Data is very innovative and conservative corporate sponsors need comfort in well known names to legitimatize the program.

Securing the accreditation, securing the content at no cost and organizing the corporate sponsors are all statistical challenges. Open X number of doors to get Y supporter/providers. The network supporting this contest has a wide ranging network of door openers and advisors on how best to pitch potential sponsors regardless of their type of organization. We can open the necessary doors without contest sponsors but it will go 10X faster with that support.

1. World Quant (Accreditation)
2. Southern New Hampshire University (Accreditation)
3. Any top university offering Masters without a BA/BS (Accreditation)
4. NSF education grant (additional credibility and funding)