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Get a Headstart: In-depth Analysis of MS in Data Science Curriculum

From A to Z: Your Ultimate Guide for MS in Data Science Curriculum

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From A to Z: Your Ultimate Guide for MS in Data Science Curriculum

Are you a graduate in engineering, mathematics, or another quantitative field who’s rethinking the educational choices you made?

Or perhaps you’re overwhelmed by the countless options, such as boot camps, graduate certificate programs, and online courses.

No matter your situation. We’ve got your back.

Data Science Platform Market

Source: The data science platform market will have a substantial growth of USD 68.02 billion between 2021 and 2026.

With the data science job market growing fast, it’s the perfect time to jump into this exciting field.

We’ll provide an in-depth analysis of the MS in Data Science curriculum through this ultimate guide. 

As you read further down, you will come across the following:

  • Overview of MS in Data Science program
  • MS in Data Science curriculum
  • List of 5 top data science universities
  • Preparing for the admission of MS in Data Science

So, join us. Let’s do an interesting read into the world of data science education!

Overview of MS in Data Science program

Data Science is the field where you use programming, mathematical techniques, and domain knowledge to analyze data. This analysis helps you develop insights that can be used for decision-making and problem-solving in corporate settings.

Choose MS in Data Science over other data science programs because:

  • It offers a comprehensive and in-depth curriculum that covers a wide range of topics in data science.
  • It gives a master’s degree that enhances your credibility and increases your competitiveness in the job market.
  • It provides opportunities to engage in research projects. Then you can collaborate with faculty members on cutting-edge research topics.
  • It takes 1.5 to 2 years to complete, that is 30 and 45 credit hours for completion.

MS in Data Science curriculum:

The core courses that form the backbone of the MS in Data Science curriculum:

  1. Machine Learning: You will learn to create models and algorithms that allow computers to learn from data.
  2. Big Data: You will learn to handle, store, and process large-scale datasets. It will help you extract valuable insights and make data-driven decisions.
  3. Data Visualization: Develop the skill to present complex data in an easy-to-understand format.
  4. Statistics for Data Science: You will gain a strong understanding of statistical concepts and methods. It will help you analyze data and make informed decisions.
  5. Data Wrangling: Learn techniques to clean, preprocess, and transform raw data. You can use it for a more usable format for examination.
  6. Programming for Data Science: Acquire proficiency in popular programming languages like Python or R.
  7. Databases and Data Storage: Understand various database systems and storage solutions that help manage and organize vast amounts of data.

As you progress in your Masters in Data Science, you’ll have to choose elective courses. Selecting the right electives will shape your expertise based on your career goals. 

Here are some popular elective courses and specializations in data science degree programs:

  1. Text Mining: Learn to extract insights from unstructured text data using natural language processing and machine learning techniques.
  2. Recommender Systems: Discover the methods behind personalized recommendations for products, services, or content, like those used by Amazon or Netflix.
  3. Computer Vision: Master the art of teaching computers to “see” and understand images or videos. It enables them to analyze visual content and make decisions.
  4. Data Engineering: Focus on designing, constructing, and managing large-scale data processing systems. It ensures data quality and efficient data pipelines.
  5. Network Analysis: Understand how to model, analyze, and visualize complex networks. It helps to reveal patterns and relationships.

Additionally, some universities offer internships or capstone projects in their MS in Data Science curriculum. It provides valuable experience, allowing you to work on real-world projects with companies to gain expertise.

List of 5 top data science universities:

  1. City University of New York (CUNY)
  2. New Jersey Institute of Technology (NJIT)
  3. Worcester Polytechnic Institute (WPI)
  4. University of Maryland (UMD)
  5. Carnegie Mellon University (CMU)
Apply Now

Preparing for the admission of MS in Data Science:

To secure your spot in an MS in Data Science curriculum abroad, here are some general steps to follow:

  • Qualifications:
    • Usually, a bachelor’s degree in a related field (math, statistics, computer science)
    • Some programs might need work experience in data science or related areas.
  • Prerequisites:
    • Brush up on essential skills like programming, statistics, and linear algebra.
    • Complete any missing courses from your bachelor’s degree by taking certificate courses.
  • Standardized tests:
    • GRE: Aim for a competitive score in both the quantitative and verbal sections
    • TOEFL/IELTS: Take one of these tests to prove your English language skills.
  • Application:
    • Statement of Purpose (SOP): Explain why you’re passionate about data science, your goals, and why you chose the specific program.
    • Letters of Recommendation (LORs): Ask professors or employers who can vouch for your abilities.

Are you ready to take the next step in your data science journey?

Then don’t make these mistakes:

  • Not verifying university-specific requirements: Reading individual university websites and contacting their admission offices for accurate and up-to-date admission details is essential.
  • Neglecting to research program specifics: Ensure you understand the requirements, curriculum, and unique features of each program before applying.
  • Ignoring university rankings: Consider the prestige and resources offered by the university. It can impact your future career opportunities.

Overlooking financial aspects: Research financial aid opportunities early. It will help ease the financial burden of pursuing higher education.

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Career Advice for MS in Data Science in the US

Career Advice for MS in Data Science in the US

Colleges and Universities, Must Read, Student Section, top20
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Career Advice for MS in Data Science in the US

300 million jobs could be affected by the latest wave of AI, says Goldman Sachs, CNN reports. 

Hey, aspiring data scientists! Are you at risk?

MS in Data Science salary range

Source: MS in Data Science salary range

 As the paychecks are impressive, the competition is also high for Data Science jobs.

But don’t worry, landing a data scientist job can be a breeze, as long as

  • You pick the right college for your degree. 
  • You’ve got the right career advice in your corner.

In this article, you will read the best career advice for MS in Data Science.

If you’re short on time, here’s a quick rundown:

  1. Choose the right MS in Data Science course
  2. Understand Data Science career opportunities
  3. Participate in Data Science networking opportunities
  4. Gain practical experience
  5. Build a strong portfolio
  6. Prepare for interviews.

Now dive in.

Apply Now

 

1. Choose the right MS in Data Science course

Why it matters: Never underestimate the importance of having a strong foundation for a successful career in Data Science.

Choosing a well-informed Data Science course helps you maximize your Data Science job prospects.

To do it: Consider factors such as curriculum, faculty expertise, availability of on-campus jobs, cost of living, alumni success, and job placement rates when selecting your program.

Our expert career advice for MS in Data Science courses and universities in the US are:

Name of the College/University

Name of the Course

Ying Wu College of Computing(NJIT)

M.S. in Data Science – Computational Track

Worcester Polytechnic Institute(WPI)

Master’s in Data Science

City University of New York(CUNY)

M.S. In Data Science

University of Maryland(UMD)

Master of Professional Studies in Data Science and Analytics

Carnegie Mellon University(CMU)

M.S. in Data Analytics for Science

2. Understand Data Science career opportunities

Data Science job prospects in different industries

Source: Data Science job prospects in different industries

Why it matters: Understanding the Data Science career paths, including in-demand job roles and industries, is crucial for portfolio development. 

If you have clarity about the job role or the industry you want to work in, you can start building a portfolio from the first year of college.

To do it: You can research the job market, network with professionals, and seek career advice for MS in Data Science from career advisors and alumni.

Our expert career advice for MS in Data Science in-demand job roles for you are:

  • Data Analyst
  • Data Scientist
  • Data Engineer
  • Machine Learning Engineer
  • Data Architect
  • Business Intelligence Engineer.

3. Participate in Data Science networking opportunities

Students participating in a networking event at NJIT

Source: Students participating in a networking event at NJIT

Why it matters: It helps you build connections with professionals and peers in the data science field. It will expand your network and open job opportunities, internships, and collaborations.

To do it: Participate in your area’s data science conferences, workshops, and meetups. Engage in discussions on LinkedIn, Reddit, and Data Science Stack Exchange platforms.

Our expert career advice for MS in Data Science is to connect with peers, alumni, and professors through university-sponsored data science networking events:

  • NJIT offers Data Science networking events like HackNJIT, Data Alliance Symposium
  • GOATHACKS is WPI’s annual event where teams tackle multidisciplinary problems in various fields, including data science.
  • The College of Staten Island Graduate Conference is an excellent opportunity for you to present your research to a broader audience. 
  • Make the best use of university-specific career centers and job fairs to access job opportunities and guidance.

4. Gain practical experience 

Why it matters: Hands-on experience on real-world projects improves your data science skills. It will make you competent in the job market, as employers value practical experience when hiring candidates.

To do it: Participating in internships, co-ops, or research projects will give you industry exposure and benefits your Data Science career path.

Our expert career advice for MS in Data Science is to:

Data Science Club at NJIT

Source: Data Science Club at NJIT

  • Join clubs like NJIT’s Data Science Club or societies at your university to collaborate on projects and learn from peers.
  • Offer your data science skills as a freelancer to gain experience, build a portfolio, and potentially secure longer-term opportunities.
  • Collaborate with students or professionals from other fields to apply data science techniques to a wide range of problems and industries.

5. Build a strong portfolio

Why it matters: An impressive portfolio can distinguish you from other MS in Data Science candidates. This will draw potential employers to your work, increasing Data Science job prospects.

To do it: As career advice for MS in Data Science, participate in competitions and contribute to open-source projects. Make sure to document your projects without fail to add them to your portfolio.

Our expert career advice for MS in Data Science is to add new projects and accomplishments:

    • Join platforms like Kaggle to compete and gain experience in real-world scenarios.
    • Promote your portfolio on LinkedIn, Twitter, and other relevant platforms to reach a wider audience.
    • Team up with fellow students or professionals on group projects to showcase your ability to work in a team.

 

6. Prepare for interviews.

Why it matters: Going well-prepared for interviews will make you competent and helps you boost your confidence. It also helps identify knowledge gaps; addressing them will improve your performance.

To do it: Familiarize yourself with frequently asked interview questions, conduct mock interviews, and research the company.

Our expert career advice for MS in Data Science is to leverage university resources for preparing job interviews:

  • Record a training interview session using InterviewStream, a resource in Handshake at WPI. It will prepare you for job interviews by creating a no-pressure environment to practice and develop skills.
  • Attend NJIT’s Big Interview, a training course that gives you hands-on practice in your specific subject.
  • Make the best use of employer literature to nail an interview.

With these tips in mind, you’re well on your way to a successful data science career in the US.

Bonus tips:

  • Choose a specialization within data science.
  • Stay updated with new tools, techniques, and trends.
  • Develop a growth mindset and embrace challenges.
  • Use Linkedin to share your insights and establish yourself as an industry expert.
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MS in Data Science Admission Requirements: What Do Top US Universities Expect?

MS in Data Science Admission Requirements: What Do Top US Universities Expect?

Colleges and Universities, Must Read, Student Section, Study Abroad Requirements
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MS in Data Science Admission Requirements: What Do Top US Universities Expect?

According to the U.S. News and World Report, here are some primary reasons why college applications in the US get rejected:

  • Fail to meet the academic requirements
  • Incomplete application
  • Choosing the wrong college/university
  • Errors in the application form
MS in Data Science Admission Requirements: What Do Top US Universities Expect?

If you’re interested in pursuing higher education, knowing how to apply for MS in Data Science in the US is crucial. Let’s show you how:

  1. Choose the college with a higher acceptance rate 
  2. Complete the application process in the right way

As you read ahead, you will see our shortlisted top universities and admission criteria for MS in Data Science in the US universities.

 

Sl. No

Name of the University

Acceptance Rate

1

City University of New York (CUNY)

94%

2

New Jersey Institute of Technology (NJIT)

69.1%

3

Worcester Polytechnic Institute (WPI)

60%

4

University of Maryland (UMD)

52%

5

Carnegie Mellon University (CMU)

14%

1. City University of New York (CUNY)

City University of New York (CUNY)

Source:

The CUNY Graduate Center offers an M.S. in Data Science, a 30 credits hour program. 

You can opt to do it either full-time or part-time. It’s intended to be completed within two years.

During the program, you’ll learn about Data Science Fundamentals, Data Analytics, and Data Applications.

CUNY MS in Data Science admission requirements:

GPA Requirements

Application Deadlines

Application Fees

Standardized Test Scores

3.0 or higher

November 1st  for spring enrollment

75 USD

GRE – 80th percentile


TOEFL iBT – 79


IELTS – 6.5

Application prerequisites for MS in Data Science in the US:

  • Must have the minimum TOEFL or IELTS score. 
  • You must have a minimum 80th percentile GRE score or similar program qualification. 
  • You need a bachelor’s degree (or equivalent) in computer science from an accredited college or university.
    • If you have a degree in STEM fields (mathematics, statistics, information science, information systems, or engineering), exceptional academic performance, required courses, programming prerequisites, and at least an 80th percentile quantitative score on the GRE, you will be eligible.
    • If you are a non-STEM degree student, the university is partnering with NYU Tandon Bridge Program. Attending this will help you to gain the required knowledge and skills to be eligible to apply for M.S. in Data Science program. 
  • You must have completed at least one course each in linear algebra, probability and statistics, and algorithms.
  • You should be fluent in Python, Java, or C++ programming.
  • You must have a minimum B grade point average in undergraduate or graduate coursework, demonstrating an aptitude for graduate study.

Admission documents for MS in Data Science in the US:

  • Submit two letters of recommendation from professional acquaintances.
  • Submit a statement of purpose. It should explain your career objectives, interests, and academic and professional background relevant to the degree program.
  • Submit the GRE score document if it is available. Or else prove your program qualification by submitting other relevant details.
  • Submit TOEFL or IELTS score documents.
  • Submit sample works (e.g., projects, websites, videos, programming code repositories, creative works) that showcase your professional knowledge related to the program(optional).
  • Submit the transcripts from each college or university you attended.
Apply Now

 

2. New Jersey Institute of Technology (NJIT)

New Jersey Institute of Technology (NJIT)

Source

To successfully complete the Master of Science in Data Science (MSDS) program at NJIT, you will need to finish 30 credits. 

You can choose any one of the options to complete this:

  • Courses (30 credits)
  • Courses (27 credits) + MS Project (3 credits)
  • Courses (24 credits) + MS Thesis (6 credits)

NJIT MS in Data Science admission requirements:

GPA Requirements

Application Deadlines

Application Fees

Standardized Test Scores

3.0 or higher

Before May 1st for fall enrollment.


Before November 15th for spring enrollment.

75 USD

GRE – Required (No specific cutoff mentioned).


TOEFL – 79


IELTS – 6.5


Duolingo – 120

Application prerequisites for MS in Data Science in the US:

  • You need a GPA score. If not, you must have graduated with a first-class. 
  • You must have a Bachelor’s degree in Data Science, Applied Statistics, Computer Science, or equivalent.
  • If you lack a computing background, you can enroll in one of the three associated Data Science Certificates programs(Data Mining, Data Visualization, or Big Data). After successfully completing the Certificate, you will be eligible to apply for the M.S. in DS program.
  • If you have an insufficient background in mathematics/statistics, you’ll be required to complete suitable bridge courses after the advisor’s review. 
  • You must have a GRE score. 
  • You also need to achieve a minimum score in TOEFL, IELTS, or Duolingo. 

Admission documents for MS in Data Science in the US:

  • You need to submit transcripts from all colleges and universities attended.
  • Submit your GPA score. If you do not have a GPA score, submit a transcript showing you graduated with a “first class” corresponding to a B average.
  • Submit TOEFL, IELTS, or Duolingo scores. 
  • Submit one letter of recommendation.

3. Worcester Polytechnic Institute (WPI)

Worcester Polytechnic Institute (WPI)

Source

If you’re interested in earning MS in Data Science from WPI, you’ll need to complete 30 credit hours. There are two options to complete the program. 

First, you can do it as a three-credit Graduate Qualifying Project (GQP). It involves working on a team project with an industry partner for real-world experience. Or you can finish it as a nine-credit M.S. thesis.

WPI MS in Data Science admission requirements:

GPA Requirements

Application Deadlines

Application Fees

Standardized Test Scores

3.5 or higher

Rolling.

 

Students who want funding must apply by October 1st for the spring batch. 

70 USD

GRE – Not required


TOEFL iBT – 84


TOEFL Essentials – 8.5


IELTS – 7 (minimum sub-score of 6.5)


Duolingo – 115

Application prerequisites for MS in Data Science in the US:

  • You need to have minimum GPA requirements.
  • You must have an eligible test score for TOEFL iBT, TOEFL Essentials, IELTS, or Duolingo.
  • To apply for MS in data science, you need a bachelor’s degree in mathematics, computer science, business, quantitative sciences, and engineering.
  • Your degree must have covered quantitative and computational topics such as data structures, programming, calculus, algorithms, linear algebra, and introductory statistics.

Admission documents for MS in Data Science in the US:

  • Submit three letters of recommendation from authorities eligible to comment on your qualification for pursuing graduate studies.
  • Submit transcripts of all the post-secondary colleges or universities.
  • Statement of Purpose
  • Submit official documents of TOEFL iBT, TOEFL Essentials, IELTS, or Duolingo scores.

 

4. University of Maryland (UMD)

University of Maryland (UMD)

Source

You can get a Master of Professional Studies(MPS) in Data Science and Analytics from the University of Maryland’s College of Computer, Mathematical, and Natural Sciences. 

This 30-credit program is for working professionals and takes less than two years to complete. The program ends with research methods and study design, but no thesis is involved in its course curriculum.

UMD MS in Data Science admission requirements:

GPA Requirements

Application Deadlines

Application Fees

Standardized Test Scores

3.0 or higher

March 10, 2023.


*The deadline for Fall enrollment is over.

75 USD

GRE – optional


TOEFL iBT – 80


IELTS – 7 

*Contact scienceacademy@umd.edu to know about the next enrollment.

Application prerequisites for MS in Data Science in the US:

  • Your degree must be equivalent to a four-year U.S. institution degree. 
  • You must have proficiency in programming languages.
  • You need a 3.0 GPA in undergraduate and graduate coursework. 
  • You must have a minimum score for TOEFL, IELTS, or PTE.
  • Having a GRE score will be an add-on.

Admission documents for MS in Data Science in the US:

  • You must submit official transcripts from all the colleges/universities you attended.
  • You must submit a Statement of Purpose. 
  • You must provide TOEFL/IELTS/PTE score. 
  • If you have a GRE score, submit it. 
  • Submit your CV/Resume. 
  • Provide your research/work experience description.
  • You need to submit previous coursework that proves your quantitative ability. This includes calculus II, linear algebra, statistics, etc.
  • You must also prove your proficiency in programming languages. This can be shown through previous programming coursework or substantial software development experience.

5. Carnegie Mellon University (CMU)

Carnegie Mellon University (CMU)

Source

CMU offers a Master of Computational Data Science program. All MCDS students must complete at least 144 units to graduate. 

You can earn this degree in two ways: 

  • Professional Preparation takes 16 months with a minimum of 48 units per semester, and you graduate in December.
  • Research Preparation takes 20 months with a minimum of 36 units per semester, and you graduate in May.

CMU MS in Data Science admission requirements:

GPA Requirements

Application Deadlines

Application Fees

Standardized Test Scores

3.0 or higher

Early Deadline November 29, 2023.



Final Deadline: December 13, 2023.

By the early deadline: 80 USD.



By final deadline: 100 USD

GRE – Required (No specific cutoff mentioned). 


TOEFL – 100


IELTS – 7.5


Duolingo – 120

Application prerequisites for MS in Data Science in the US:

  • The MCDS program is for students with a computer science, computer engineering, or related degree from a top-ranked university. 
  • You need to have a minimum TOEFL, IELTS, or DuoLingo test scores(TOEFL is preferred over the other two).
  • It’s highly recommended to have GRE scores.

Admission documents for MS in Data Science in the US:

  • You need to submit TOEFL, IELTS, or Duolingo scores.
  • It’s advised to provide GRE scores; if not, explain the reason briefly in your application.
  • Submit transcripts from all attended universities.
  • Submit your current resume outlining your research experience, education, work experience, and achievements like publications, scholarships, etc.
  • Prepare a statement of purpose in one or two pages describing your research interests, related experiences, and goals in pursuing a graduate degree at CMU.
  • Submit three letters of recommendation. At least two of the recommenders should be from your faculty/employers.
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Pursuing MS in Business Analytics in the USA? Follow these professors!

Pursuing MS in Business Analytics in the USA? Follow these professors!

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Pursuing MS in Business Analytics in the USA? Follow these professors!

The field of business analytics is constantly evolving. Staying up-to-date with the latest developments is crucial for students.

Pursuing MS in Business Analytics in the USA? Follow these professors!

While classroom study is important, following thought leaders in the field can help you stay abreast of emerging trends and best practices. 

Without further ado, these are some of the finest professors and researchers in the field of business analytics.

1. Dr. Nanda Kumar

Dr. Nanda Kumar is a Professor of Information Systems and Chair of the Paul H. Chook Department of Information Systems and Statistics at the Zicklin School of Business. 

He is also the academic director of the MS in Business Analytics program. 

His research interests include data science/analytics, IT strategy, technology policy, business analytics.

Dr. Nanda Kumar is a Professor of Information Systems and Chair of the Paul H. Chook Department of Information Systems and Statistics at the Zicklin School of Business.

source

Some information about his education, achievements, and publications are:

Education

  • BE in Electronics & Communication Engineering from College of Engineering, Guindy (CEG), Anna University, Madras, India
  • MBA in Business Administration from Narsee Monjee Institute of Management Studies, Bombay, India
  • PhD in Management Information Systems from Sauder School of Business, University of British Columbia, Canada

Achievements

  • Winner of a Teaching Excellence Award at Baruch College in 2009
  • Winner of the JAIS Paper of the Year award in the information systems field in 2010 for his article on social recommender systems

Publications

Some of his recent publications are:

  •  “The Impact of Social Media on Consumer Preferences: Evidence from a Natural Experiment.” 
  • “The Impact of Social Media on Consumer Preferences: Evidence from a Natural Experiment.” 

You can find more information about him on his faculty profile.

2. Dr. Renata Konrad

Dr. Renata Konrad is the Director of MS in Business Analytics program and Associate Professor of Operations and Industrial Engineering at WPI. She is also a Fulbright Scholar.

Her research focuses on the application of operations research to social justice issues and healthcare delivery to improve the quality, timeliness, and efficiency of operations.

Dr. Renata Konrad

Source 

Some information about her education, achievements, and publications are:

Education

  • B.A.Sc. and M.A.Sc. in Industrial Engineering from the University of Toronto
  • Ph.D. in Industrial Engineering from Purdue University

Achievements

Winner of the Romeo L. Moruzzi Young Faculty Award for Innovation in Undergraduate Education in 2018

Member of several governmental committees dealing with human trafficking, such as the U.S. Department of Transportation Advisory Committee on Human Trafficking 

Publications

Some of her recent publications are:

  • Select, Route and Schedule: Optimizing Community Paramedicine Service Delivery with Mandatory Visits and Patient Prioritization. Technical Report. Optimization Online.
  • Optimizing Placement of Residential Shelters for Human Trafficking Survivors. Socio-Economic Planning Sciences, Vol 70.
  • Overcoming Human Trafficking via Operations Research and Analytics: Opportunities for Methods, Models, and Applications. European Journal of Operational Research, Vol 259(2), pp. 733-745.

You can find more information about her on her faculty profile.

3. Dr. Jonathan Peters

Dr. Jonathan Peters is a Professor of Finance and Data Analytics in the Accounting and Finance Department in the Lucille and Jay Chazanoff School of Business at CSI CUNY. He teaches several courses of MS in Business Analytics program.

His research interests include mass transit financing, corporate and public sector performance metrics.

Dr. Jonathan Peters

Source 

Some information about his education, achievements, and publications are:

Education

  • AAS and BS from College of Staten Island
  • MA in Economics from Hunter College
  • PhD in Economics from CUNY Graduate School

Achievements

  • Expert and chair on panels at the National Academy of Sciences 
  • Member of the Trucking Research Committee at the Transportation Research Board

Publications

Some of his recent publications are:

  • For whom the CPI tolls: reporting of road pricing in the Consumer Expenditure Survey. Transportation Research Record: Journal of the Transportation Research Board, No. 2670. Fall 2017. 24-32.
  • The Impact of Road Pricing on Housing Prices: Preliminary Results from New York City. Journal of Public Transportation, Vol. 19(4), pp. 20-38.

You can find more information about him on his faculty profile.

4. Dr. Doug Lehmann

Dr. Doug Lehmann is an Associate Professor of Professional Practice and Director of MS in Business Analytics program at Miami Herbert Business School. 

His research interests include data science, machine learning, business analytics, and sports analytics.

Dr. Doug Lehmann is an Associate Professor of Professional Practice and Director of MS in Business Analytics program at Miami Herbert Business School.

Source 

Some information about his education, achievements, and publications are:

Education

  • PhD in Biostatistics from University of Michigan
  • MA in Economics and Statistics from University of Missouri-Columbia

Achievements

  • Recipient of several awards for teaching excellence at Miami Herbert Business School
  • Co-founder of PredictionStrike, a sports prediction platform 

Publications

Some of his recent publications are:

  • PredictionStrike: A Sports Prediction Platform. Journal of Sports Analytics, Vol. 6(4), pp. 255-264.
  • A Data Science Approach to Predicting NBA Player Salaries. Journal of Sports Analytics, Vol. 5(3), pp. 161-172.

You can find more information about him on his faculty profile.

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5. Dr. Blake LeBaron

Dr. Blake LeBaron is the Abram L. and Thelma Sachar Professor of International Economics and Director of MS in Business Analytics program at Brandeis International Business School.

Finance Prof. Blake LeBaron is director of the MSBA program.

His research interests include agent-based modeling, equity markets, finance/technical analysis.

Some information about his education, achievements, and publications are:

Education

  • PhD in Economics from University of Chicago
  • MA in Economics from University of Chicago
  • BS in Electrical Engineering from Rensselaer Polytechnic Institute

Achievements

  • Winner of the Mike Epstein Award from Market Technicians Educational Foundation in 2014
  • Recipient of a Sloan Fellowship from 1994 to 1996

Publications

Some of his recent publications are:

  • Agent-based models for economic policy design: Introduction to the special issue. Computational Economics , Vol. 55(3), pp. 685-702.
  • Heterogeneous expectations and the distributional properties of asset prices. Journal of Economic Interaction and Coordination , Vol. 14(3), pp. 511-535.

You can find more information about him on his faculty profile.

6. Dr. John Dickerson

Dr. John Dickerson is an Associate Professor of Computer Science and a member of the Maryland Center for Women in Computing at UMD. He also teaches the MS in Business Analytics program.

His research interests include machine learning and economic analysis.

Dr. John Dickerson Dr. John Dickerson is an Associate Professor of Computer Science and a member of the Maryland Center for Women in Computing at UMD

Source 

Some information about his education, achievements, and publications are:

Education

  • PhD in Computer Science from Carnegie Mellon University
  • BS in Computer Science from University of Maryland, College Park

Achievements

  • Recipient of an NSF CAREER award in 2019
  • Recipient of a Facebook Fellowship (2015–2017) and a Siebel Scholarship (class of 2016)

Publications

Some of his recent publications are:

  • Failure-aware kidney exchange. Artificial Intelligence, Vol. 267, pp. 132-152.
  • Using sentiment to detect bots on Twitter: Are humans more opinionated than bots? Proceedings of the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), Sydney.

You can find more information about him on his faculty profile. 

In conclusion

You must remain up-to-date with thought leaders in the field. The professors mentioned in this article are the finest educators in business analytics. They have extensive experience and knowledge. 

Following their work and research can offer young graduates immense insights. 

Keep abreast of the latest developments and follow the work of top industry leaders, to excel in your career.

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