Up until 2028, the Bureau of Labor Statistics predicts that demand for data scientists would increase faster than average. As of February 2021, a data scientist makes an average income of $113,609. It’s a wonderful time to enter the field right now because of high earnings and above-average job growth.

What are the Admissions Requirements?

There are certain parallels among the admissions standards, even though each school and program has its own. A background in statistics, mathematics, or computer science is required for all master’s programs in data science. You can be asked to submit writing samples, writing samples, writing samples, or other work samples, including programming projects. It’s preferable to initially concentrate on the course alternatives while limiting your program possibilities.

Review the admission requirements after you’ve created a list of colleges offering the appropriate curriculum. A GRE or GMAT score is not necessary for all programs. One of the numerous programs not requiring the GRE is mentioned below as George Washington University.

Which Programs have Electives?

All programs will put a primary emphasis on fundamental data science. However, there is also a tonne of flexibility within the industry to create a specialty that can open up new employment possibilities. For instance, NYU students choose six electives from among business, health, or analytics courses. Consider the elective choices when comparing different programs to locate one that fits your interests.

Which programs have Internships? And what about Career Placement?

A professional degree is a master’s in data science. Therefore, it’s crucial to consider how the program will lay a long-term career basis. It is insufficient to only attend lectures and do assignments. Graduate programs should also offer contacts and assistance in creating professional networks.

The following are examples of typical career placement components:

• Portfolio – You can display your work through group projects, capstone projects, and class assignments. When trying to get interviews and apply for employment, this will prove to be really helpful.
• Internships: Internships give students practical experience and frequently result in long-term employment. Internships, like electives, are a good way to experience several subfields in the data science industry. Check the program’s existing internship program or requirement before applying.
• Career guidance: The majority of graduate institutions offer some sort of career placement or advice service. Be careful to seek effective career assistance activities when assessing programs.
• Alumni network: An excellent resource for creating professional opportunities is alumni. Check to see if there are any current chances to get in touch with alumni who are employed in data science.

How long will the Program Last? Is it Available Part-Time?

The majority of master’s programs in data science can be finished in between 18 months and 3 years of full-time study. Part-time choices are being offered by more and more programs, like the University of Washington. Working professionals have a fantastic opportunity because of this.

How much is Tuition?

When choosing a graduate program, the cost is frequently the most essential consideration. The cost per credit for each master’s degree in data science is listed below. One thing to keep in mind is that greater results do not always follow from higher quality or a higher cost per credit. For this reason, it is helpful to evaluate every part of a data science program. Key considerations include location, elective availability, and career services. The majority of recognized data science programs offer financial aid or scholarships.

Data Science Programs And Related Degrees

Higher education offers a wide range of specialty areas and program kinds, from data systems administration and security to machine learning and artificial intelligence. Data science master’s degrees, bachelor’s degrees, boot camps, and short courses are a few of your alternatives.

1. Master’s in Data Science

Gaining a master’s degree in data science will assist you in developing a broad skill set that can be applied to a wide range of tech-related occupations, including computer programming, data engineering, and data architecture.

As a participant in this graduate degree program, you can anticipate delving into key ideas in the following fields:

• Applied Statistics
• Database Systems and Data Preparation
• Practical Machine Learning

Programming languages like Python, R, and SQL will also be covered.

1. Master’s in Business Analytics

Data science is used in business analytics to comprehend and forecast the market, consumer, and global economic trends. A master’s degree in business analytics can assist you in gaining the knowledge and abilities necessary to convert massive amounts of data into insights that can be used to inform business strategy.

You will become knowledgeable in these fields as a student in this advanced degree program:

• Programming
• Data Analytics and Management
• Data Models and Visualization

The goal of business analytics aficionados is to make “data-driven decisions.”

1. Master’s in Information Systems

You must be knowledgeable in using information systems and operations in order to architect new technologies for businesses. Your ability to construct and manage information systems, assess technical approaches and dangers, and interact with and manage people in organizations can all be improved with the help of an information systems master’s degree.

You will become knowledgeable in these fields as a student in this advanced degree program:

• Information Systems Analysis and Design
• Business Telecommunications
• Managing Emerging Information Technology

Conclusion

Many small, large, and mid-sized businesses are finding greater value in the dynamic field of data science. Data scientists play a crucial role in assisting firms in making strategic decisions and maximizing outcomes, from collecting data to presenting findings. Data science has evolved from its historical focus on data mining, programming, and data analysis to one that spans the complete data science life cycle today.

Author: mayank