When it comes to choosing a place to study STEM, the United States is one of the top choices. For foreign students who wish to enroll in data science courses in USA, it is the perfect study abroad destination. The data science courses in USA market is anticipated to increase from 95.3 billion USD to 322.9 billion USD in 2026, with a CAGR of 27.7% globally, as a result of its escalating popularity. Also, the number of data science specialists employed in the business will increase by 31.4% by 2030.
Hence, if you want to enroll in data science courses in USA, this article will be of great use to you. Here, we've covered all you need to know about data science courses.
Why Study Data Science Courses in USA ?
Because a degree from the USA is recognised and in demand internationally, enrolling in data science courses in USA allows students to pursue a variety of employment options both in the USA and in many other nations. Also, data science courses in USA is a highly sought-after curriculum that offers excellent employment prospects for the reasons listed below.
- There are numerous well regarded colleges in the US that offer degree programmes in data science. Many ranking polls place several US universities that specialize in data science among the Top 10, Top 50, and even Top 100 universities worldwide. Among the universities that made the Top 10 QS Global University Rankings for 2023 are MIT, Harvard, and Stanford.
- Graduates in data science in the USA have a wide range of opportunities and exposure. Between 2020 and 2030, the US Bureau of Labor Statistics predicts a 32% increase in employment for data scientists. Also, it is predicted that 19,800 employment will become available during the next ten years.
- The US is one of the top countries paying data science specialists the most. A data scientist's median annual salary in the USA is 108,660 USD, or around 52.24 USD per hour. The top 25 percent of data scientists make an annual salary of 130,370 USD, while the bottom 25 percent earned roughly 71,790 USD.
- A data scientist is one of the most sought careers in the Nation, with a work satisfaction score of 4.4.
- Employees in San Francisco receive salaries that are 26.4% more than the national average, making it the highest paid city in the USA for master's degrees in data science.
- According to IBM's Analytics Department, around 61,799 new jobs for data scientists and other advanced analytics positions would be generated in the USA.
- By 2028, the United States of Labor Statistics predicts a 20% rise in employment in the field of data science.
Data Science Courses in USA
In the USA, the data science course curriculum consists of eight core classes and a capstone project that lasts two quarters. Students get the chance to work on data science difficulties encountered by diverse outside organisations through its capstone projects. Some universities like Northwestern University also offer MS in Analytics which is a related program of MS in Data Science. The data science courses in USA is created to offer the breadth and depth of knowledge necessary to begin a successful career in big data, regardless of whether the student holds a bachelor's or master's degree in the field. Among the topics covered in the best data science courses in USA are:
1. Statistical modelling
A mathematical model known as a statistical model encapsulates a collection of statistical presumptions relating to the production of sample data (and similar data from a larger population). A statistical model simulates the data generation process, frequently in a highly idealized form.
A mathematical link between one or more random variables and other non-random variables is typically how a statistical model is defined. A statistical model is hence "a formal representation of a theory" (Herman Adèr, citing Kenneth Bollen).
All statistical estimators and hypothesis tests are produced using statistical models. In a broader sense, statistical models provide the basis of statistical inference.
2. Data management & data visualization
Data is becoming increasingly crucial to business, whether it is being used to tailor advertisements to millions of internet users or to simplify inventory ordering at a small restaurant. We don't always know how to use data to get the answers to the questions that will increase our productivity. Even if you've never thought about data before, you will learn what data is in this course and consider what questions you have that the data might be able to answer. You will discover how to create a research topic based on available data, explain the variables and their relationships, do elementary statistical calculations, and properly communicate your findings. At the end of the course, you will be able to handle and display your data using robust data analysis tools, such as SAS or Python, including how to deal with missing data, variable groups, and graphs. You will share your efforts with others throughout the course to get helpful feedback and to observe how your classmates utilize data to address their own concerns.
3. Machine learning
Machine learning (ML) is a topic of study focused on comprehending and developing "learning" methods, or methods that use data to enhance performance on a certain set of tasks. It is considered to be a component of artificial intelligence. Without being expressly taught to do so, machine learning algorithms create a model using sample data, sometimes referred to as training data, in order to make predictions or judgements. Machine learning algorithms are utilised in a broad range of applications, including computer vision, speech recognition, email filtering, medicine, and agriculture, when it is challenging or impractical to create traditional algorithms that can accomplish the required tasks.
Computational statistics, which focuses on making predictions using computers, is closely connected to a subset of machine learning, although not all machine learning is statistical learning. The discipline of machine learning benefits from the tools, theory, and application fields that come from the study of mathematical optimization. Data mining is a related area of research that focuses on unsupervised learning for exploratory data analysis.
4. Human-Centred Science
The design, development, and implementation of mixed-initiative human-computer systems are studied by human-centered computing (HCC). It is the result of the interdisciplinary fields' shared interest in both human comprehension and the creation of computer products. Human-computer interaction, information science, and human-centered computing are all closely linked fields. Human-centered computing typically focuses on the systems and procedures of technology use, whereas human-computer interaction is more concerned with the ergonomics and usability of computing artefacts, and information science is concerned with the procedures of information gathering, processing, and use.
Computing that puts people first the majority of scholars and practitioners are drawn from one or more of the following fields: computer science, human factors, sociology, psychology, cognitive science, anthropology, communication studies, graphic design, and industrial design. By concentrating on the ways that people embrace and organise their lives around computational technology, some academics aim to comprehend people both as individuals and as social groupings. Some concentrate on creating and producing fresh computational artefacts.
5. Scalable Data Systems & Algorithms
For students pursuing computational and data science, this course covers subjects in computer systems that are crucial. It explains concepts related to architecture, operating systems, and data structures that may be unfamiliar to students without a computer science degree. The discussion then shifts to more complex subjects including Big Data platforms, HPC/GPGPU programming, and tree/graph data structures.
Scalability is a property of computers, networks, algorithms, networking protocols, programmes, and applications in the computing world. For instance, a search engine must be able to handle the growing number of users and subjects it indexes. Webscale is a computer architecture strategy that allows business data centers to utilize the capabilities of large-scale cloud computing providers. Scalability is most often used in mathematics to describe closure under scalar multiplication.
6. Software engineering
A methodical engineering approach to software development is known as software engineering. A software engineer is a person who designs, develops, maintains, tests, and evaluates computer software using the concepts of software engineering. Although the term "programmer" is occasionally used as a synonym, it may not always imply technical training or expertise.
The software development process, which includes the definition, implementation, evaluation, measurement, management, modification, and improvement of the software life cycle process itself, is informed by engineering methodologies. It makes extensive use of software configuration management, which focuses on methodically regulating configuration changes and preserving the integrity and traceability of the code throughout the system life cycle. Software versioning is used in modern workflows.
7. Research design & data ethics
Data ethics, commonly referred to as big data ethics or just data ethics, is the systematic organisation, defence, and promotion of conceptions of good and wrong behaviour in connection to data, particularly personal data. With the invention of the Internet, the amount and caliber of data have grown significantly and are still growing exponentially. Big data refers to the enormous volume of data that is so complicated and voluminous that it cannot be processed by conventional data processing technologies. High-throughput gene sequencing, high-resolution imaging, electronic medical patient records, and a variety of internet-connected health equipment are recent developments in medical research and healthcare that have sparked a data flood that will soon approach the exabyte range. The importance of data ethics grows as data volume does due to the magnitude of the impact.
8. Data Mining
Data mining is the process of identifying patterns and extracting information from big data sets using techniques that combine machine learning, statistics, and database systems. With the overarching objective of extracting information (using intelligent techniques) from a data collection and structuring the information into an intelligible form for subsequent use, data mining is an interdisciplinary branch of computer science and statistics. The analytical stage of the "knowledge discovery in databases" process is known as data mining. Together with the raw analysis stage, other components of the process include database and data management, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of found structures, visualization, and online updating. Data mining is used wherever there is digital data available today. Notable examples of data mining can be found throughout business, medicine, science, and surveillance.
9. Data Optimization
Data programming, also known as data optimization, is the process of choosing the optimal element from a range of possibilities in order to satisfy a certain set of criteria. Discrete optimization and continuous optimization are the two main subfields. Many quantitative fields, including computer science, engineering, operations research, and economics, encounter some form of optimization issue, and the mathematical community has been interested in developing solutions for decades.
According to a more comprehensive perspective, an optimization issue entails either maximizing or minimizing a real function by methodically selecting input values from a permitted set and determining the value of the function. A significant field of applied mathematics is the extension of optimization theory and methods to new formulations. In a broader sense, optimization entails determining the "best possible" values of an objective function given a specified domain (or input), taking into account a wide range of various sorts of objective functions and different types of domains.
The United States is a leading country in data science education, with many universities and institutions offering courses. Data science courses range from undergraduate programs to graduate degrees and professional certifications. Common courses include statistics, programming, machine learning, data mining, and data visualization. The quality and focus of data science courses can vary greatly between institutions, so prospective students should research their options and consider factors such as program structure, faculty expertise, and job placement rates before making a decision.
Frequently Asked Questions (FAQs)
Is MS in data science in USA worth it?
MS in Data science from USA may be worth considering if you're looking for a high-paying position in a field that is in demand and is expanding quickly. The job outlook for data scientists is expected to grow 22% from 2020 to 2030, much faster than the average for all occupations, according to the US Bureau of Labor Statistics.
What is the duration of MS in Data Science in USA?
MS in Data Science in USA from top-ranking universities requires 2 years to complete.
How much is the MS in Data Science in USA fees?
Masters in Data Science in US from top universities costs around 21,000 to 75,000 USD per annum. In addition to this, cost of living in USA for international students is approximately 15,000 USD per year.