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Master the Future: Key 2023 Trends for MS in Data Science

Master the Future: Key 2023 Trends for MS in Data Science

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Master the Future: Key 2023 Trends for MS in Data Science

Picture yourself fresh out of university, with an MS degree in Data Science in hand, ready to conquer the world. 

But you quickly realize the job market’s speaking a language you’re not quite fluent in. 

Machine learning, Data-as-a-service, and real-time data streaming – these are the buzzwords on everyone’s lips, yet they were never part of your coursework.

Master the Future: Key 2023 Trends for MS in Data Science

Why it matters?

Take our word for it – it doesn’t feel good to fall behind.

That’s why you need:

  1. Wisdom of guidance from experts
  2. Knowledge about the 2023 trends in data science

In this article, you’ll explore what’s hot in data science in 2023 and why you need to be in the know.

As you read further, you will come across:

  • Importance of data science in today’s world
  • How data science trends influence the data science study
  • Data science trends in 2023
  • Universities offering MS in Data Science incorporating emerging trends in their curriculum

If you make it to the conclusion of this article, there are some bonus tips waiting for you.

Importance of data science in today’s world

Data Science in Today's World

Source: Data science used for taking decision making

From social media posts to banking transactions, every digital action involves a pool of data. This is where data science comes in.

Data science allows us to make sense of this sea of data. It helps businesses make informed decisions, governments develop better policies, and scientists uncover new insights. 

Whether it’s predicting customer behavior, detecting fraudulent transactions, or powering the latest AI technologies, data science is making a massive impact on our world.

How data science trends influence the data science study

Data science trends often reflect advancements in technology, shifts in industry needs, and societal changes. 

It can influence the field of data science by introducing new tools, methodologies, and areas of focus.

When it comes to studies, these data science trends can affect course offerings, research opportunities, and the skills that employers are seeking. 

By staying updated on these trends, you can align your studies with the future of the field. It will give you a competitive edge as you launch your data science career.

Data science trends in 2023

1. The Rise of AI and Machine Learning

AI is the broad concept of machines being able to carry out tasks in a way we would consider “smart.” And it’s undeniably the future of data science.

Machine Learning, a subset of AI, involves using algorithms to parse data, learn from it, and then make predictions or decisions without being explicitly programmed to do so. 

These technologies have a wide range of applications, from healthcare and finance to entertainment and transportation.

Data science trends: Impact on MS in Data Science curriculum:

  • Incorporation of specialized AI and ML courses: These courses span from introductory AI and ML principles to more advanced subjects such as deep learning and neural networks.
  • Integration of practical AI and ML projects: This approach involves the design, implementation, and testing of AI and ML models.
  • Research prospects in AI and ML: The rise of AI and ML opens up a wealth of research opportunities. As an MS student, you may have the opportunity to contribute to leading-edge research in fields such as explainable AI or reinforcement learning.

2. Automation in Data Science

With automation, one of the 2023 trends in data science, you’ll use technology to perform tasks that typically require human intervention.

As the volume of data increases every day, automation becomes crucial in managing and making sense of this data.

Automation could be anything from data cleaning and pre-processing to feature selection and even model building. It helps to save valuable time and reduces the chance of human error.

Data science trends: Impact on MS in Data Science curriculum:

  • Learning about automation tools and techniques: Automation tools can include everything from data pre-processing utilities to automated machine learning (AutoML) systems.
  • Engaging in automation-based projects: Increasingly, your academic projects will incorporate elements of automation. This might involve automating aspects of data cleaning, feature selection, or model tuning.
  • Understanding the importance of automation: It’s vital not just to learn about automated tools but also to understand automation’s foundational principles. This helps in understanding when automation is the most effective and when manual intervention is necessary.
  • Examining the ethical aspects of automation: Be ready to engage in discussions about the consequences of depending on automation for data-driven decision-making.

3. Ethical Data Science and Privacy

Ethical Data Science and Privacy

Source

Ethical Data Science revolves around responsibly handling data, maintaining privacy, ensuring transparency, and avoiding harmful outcomes.

It’s about ensuring the algorithms you create and the analyses you conduct are fair, unbiased, and respect individual privacy.

Data privacy refers to the right of individuals to have their personal data protected. In data science, this means working with anonymized datasets, or ensuring personally identifiable information is securely handled and stored.

Data science trends: Impact on MS in Data Science curriculum:

  • Ethics and privacy courses: Expect to see dedicated courses that dive deep into the ethical aspects of data science and data privacy laws worldwide.
  • Ethics in projects: Project work will emphasize ethical data handling, requiring you to incorporate privacy-preserving techniques.
  • Discussion on ethical case studies: There’ll be an increased focus on discussing real-world ethical dilemmas faced by data scientists. It will help you navigate complex ethical issues in your future career.

4. Data Visualization

Data visualization is the process of translating large, complex datasets into visual graphics to reveal patterns, trends, and insights that might go unnoticed in raw, numerical data. 

It plays a crucial role in data science, making it easier for data scientists and stakeholders to understand and interpret data findings.

Data science trends: Impact on MS in Data Science curriculum:

  • Data visualization courses: You’ll find specialized courses that teach you to create accurate, and easy-to-understand data visualizations.
  • Visualization in projects: Your project work will require the use of data visualization tools to represent your findings. It will help you master this essential skill.
  • Focus on storytelling: Alongside technical skills, there’ll be an emphasis on data storytelling. It will teach you to present your data insights that will resonate with non-technical stakeholders.

5. Data-as-a-service (DaaS)

Data-as-a-service (DaaS)

Source

DaaS is a cloud-based service where data is stored, processed, and provided to users over the internet. 

It eliminates the need for businesses to invest in their own data infrastructure, providing them with real-time, scalable access to large datasets.

Data science trends: Impact on MS in Data Science curriculum:

  • Courses on DaaS: As DaaS becomes more prevalent, you’ll find courses dedicated to understanding how DaaS works and how to leverage it for data analysis.
  • DaaS in Projects: Your projects might require you to use DaaS platforms. It will give you hands-on experience with this emerging trend.
  • Understanding data management: As part of the DaaS trend, there’ll be an emphasis on understanding data management principles. It includes data quality, data integration, and data governance.
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Universities offering MS in Data Science incorporating emerging trends in their curriculum

    1. Massachusetts Institute of Technology
    2. Stanford University
    3. Harvard University
    4. University of California(UC)
    5. Carnegie Mellon University
    6. DePaul University
    7. Worcester Polytechnic Institute
    8. New Jersey Institute of Technology
    9. University of San Francisco
    10. City University of New York

Staying updated with the 2023 trends in Data Science significantly influences your academic journey.

Bonus tips for selecting the best Data Science program:

  • Confirm the program’s syllabus is up-to-date, embracing new data science trends.
  • Choose programs with faculty members actively involved in the data science industry.
  • Opt for programs offering real-world experiences like internships or projects.
  • Consider both university rankings and feedback from students and alumni.
  • Look for programs providing comprehensive career support and networking opportunities.
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Career Advice for MS in Data Science in the US

Career Advice for MS in Data Science in the US

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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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