Cloud Hosting

Editorial Note: We earn a commission from partner links on Forbes Advisor. Commissions do not affect our editors' opinions or evaluations.

Advances in technology have resulted in what seems like an endless amount of data. From personal wearable devices and laptops to large-scale manufacturing projects and government programs, our world relies on computer systems and technologies to manage and secure data.

Data science professionals work with large amounts of data, develop strategies to improve these systems and help businesses and organizations run more effectively and efficiently. If youre a computer technology or data science professional and youre ready for more job opportunities, higher earning potential, and a chance to use your analytical and leadership skills, consider earning a masters in data science.

If you ultimately plan to earn a doctoral degree, a masters in data science can push you toward that goal as well.

This article gives a detailed explanation of masters degrees in data science, including admission requirements, common courses, specializations and career options. Keep reading for everything you should know about a masters in data science.

A masters in data science provides you with advanced knowledge and skills essential for many data science careers. This degree takes you from gathering and analyzing raw data to using predictive modeling methods, machine learning, data mining, artificial intelligence and other technologies for extracting valuable insights from data. It also strengthens your leadership and communication skills.

A data science masters degree typically requires around 30 credits of coursework and takes two years to complete, though part-time students might need longer. Accelerated programs can take as little as a year to finish.

Masters in data science programs typically require a capstone or thesis project to graduate, allowing learners to demonstrate mastery of their data science knowledge under faculty guidance.

Admission requirements for data science masters degrees vary by program; below we list a few common standards for admission:

Data science masters programs typically allow you to choose an area of specialization, allowing you to focus your studies on an area that interests you. Specialization offerings differ among programs but may include the following:

Students in this specialization focus on applied statistics, using algorithms to develop marketing models, predictive modeling and analytics, and other applications. Learners understand how to test real-world projections based on data.

This specialization covers the foundations of data engineering and analytics application engineering, teaching students to use problem-solving skills, build software systems and choose hardware systems with the ultimate goal of scaling data analysts models in production systems.

An analytics management specialization covers communication strategies, management principles, and the use of statistical data and analyses to optimize business performance. The curriculum explores accounting and finance for technology managers, project management, business leadership and communications, ethics, legal issues and data governance.

This specialization teaches students how to pivot from traditional applied statistics models to data-adaptive models for machine learning, natural language processing, software robotics, artificial intelligence and deep learning.

Students specializing in technology entrepreneurship learn about innovative advances in technology and science, data science, artificial intelligence and machine learning, including how these advances provide opportunities for entrepreneurship. This specialization covers technology entrepreneurship and accounting and finance for technology managers.

A big data informatics specialization focuses on the use of cutting-edge tools and technologies to address big data issues in analytics, data processing and applications. It covers natural language processing, web information management, advanced data mining, interactive media, text mining, and information systems analysis and design.

Course titles and offerings are unique to each data science program, but you can expect to take a few standard courses as a data science masters student. Below we explore some common data science masters courses.

This course focuses on data organization methods, streaming algorithms for computing statistics, dynamic programming, numerical algorithm stability, gradient and stochastic descent, large-scale applications, collaborative filtering and basic graph models for searching.

A Python for data science course covers the fundamentals of the Python programming language in computer science. The curriculum includes data structure implementation, data analysis solutions, programming paradigms, data stream processing, object-oriented programming and an overview of the Python library and its packages.

This course introduces machine learning on the graduate level, focusing on the statistical concepts used in supervised machine learning, popular algorithmic paradigms, and representation and online learning.

Students in a statistical inference and modeling course learn the basics of statistical inference and testing. This course covers hypothesis testing, maximum likelihood estimates, generalized linear regression models, statistical computing and nonparametric regression.

In this course, you can expect to learn how to use reinforcement learning as a strategy for working with intelligent systems. You might study Markov decision processes, temporal difference learning, eligibility traces, dynamic programming, implementation of intelligent agents and function approximation.

No degree can guarantee any particular career outcome, but if you have a masters in data science, several roles should be within reach. Below are some well-paying careers you may qualify for with a masters in data science. We sourced salary data from the U.S. Bureau of Labor Statistics and Payscale.

Median Annual Salary: $103,500Minimum Required Education: Bachelors degree, masters or doctoral degree sometimes preferredJob Overview: Data scientists use various technologies and tools to identify, gather, categorize, analyze and extract insights and information from data. They leverage web-scraping tools to turn raw data into usable data. Data scientists also develop algorithms, classify data with machine learning and use data visualization software.

Median Annual Salary: $95,290Minimum Required Education: Bachelors degree, masters degree sometimes preferredJob Overview: Management analysts gather and analyze an organizations data to identify problems and recommend improvements. They typically work with financial data, such as revenue and expenditures; they also gather data from observing and interviewing personnel. Management analysts may specialize in a specific area or industry, such as inventory control, corporate reorganization or government agencies.

Median Annual Salary: $99,890Minimum Required Education: Bachelors degree, masters degree sometimes preferredJob Overview: Database administrators manage, implement and test databases. They identify performance issuessuch as those related to scalability, capacity and other potential problemsand find solutions. They may also plan and execute security measures to protect databases.

Median Annual Salary: $134,870Minimum Required Education: Bachelors degree, masters degree sometimes preferredJob Overview: Database architects develop strategies and procedures for data warehouse systems, enterprise databases and multidimensional networks. They design relational databases, establish database operations standards, develop data models for warehouse infrastructure and ensure systems are functional.

Average Annual Salary: Around $95,200Minimum Required Education: Bachelors degreeJob Overview: Data engineers create and translate algorithms into prototype code. They may also analyze and identify trends in data sets, create process documentation, develop tools and dashboards, recommend improvements for data usage and access, and provide advice on technological resources and tools.

If your desired career involves interpreting and analyzing data and using your findings to recommend solutions for businesses and organizations, then yes, earning a masters in data science is worth it. This degree opens up a large selection of career opportunities in the data science field.

A degrees difficulty level is subjective, but you can expect a masters in data science to challenge you. If youre interested in the data science field and enjoy using technologies, tools and other resources to work with data, you may be primed for success in this degree program. If youre not an analytical thinker, you might find it more difficult.

Program length varies by degree. Many data science masters programs take one and a half to two years to complete, but some accelerated online programs ready you for graduation in as little as one year. On the other hand, if you study part time, you may need longer to earn your degree.

Go here to see the original:

What Is A Master's In Data Science? Everything You Should Know - Forbes