Become a leader in this emerging field by unifying statistics, data analysis and machine learning. This program provides a foundation for new computational methods that capture, manage and analyze massive datasets to solve real-world problems.
Learn from faculty who are pioneering these new methods. Take advantage of internships in one of America’s leading tech hubs.
Data science is a complex field that requires high-level programming skills and math and statistics expertise. This is why it’s not surprising that data scientists earn an average salary of more than $100,000.
While there are plenty of opportunities to get a job with a master of data science degree, it’s important to consider your professional and academic background before pursuing this advanced degree. The salary you can expect to receive depends on many factors, including your area of specialization, industry, and the type of company you work for.
A data scientist can help businesses understand and analyze large volumes of information, leading to unique discoveries and increased revenue. They can also identify hidden patterns in big data, which could be valuable for a wide range of industries. Whether you’re interested in data analytics, business intelligence, or machine learning, there is a degree program that can meet your needs.
Prepare for the MS-DS Master of Data science exam with our free practice test modules. Each quiz covers key topics to help you pass on your first try.
New York University’s Master of Science in Data Science program covers a wide range of topics, including data management and machine learning. This comprehensive curriculum allows students to gain a deep understanding of the field and apply their skills to solve real-world problems. The program also addresses the ethical and social implications of data science.
The program’s curriculum focuses on developing the necessary knowledge, skills and techniques to extract and analyze information from large data sets. This information can then be used to make better decisions and improve business processes. It draws from a broad range of disciplines, including engineering, statistics, information science, and computer science.
The program offers a number of tracks to help students tailor the degree to their career interests. Choosing the right track is a crucial step in completing a successful master’s program. You should always review the curriculum of each track before making a decision. It is also important to look at the track’s faculty and alumni. If possible, you should also talk to alumni who are currently working in the field of data science.
Data science bachelor’s degrees are available from many universities, although they typically need four years to complete.
Gaining a master’s degree in data science opens up a wide range of career opportunities, including increased pay, a quicker route to leadership roles, and greater understanding of the field’s theoretical and practical applications.
Data science master’s degree holders operate in a variety of industries. These include fields like designing computer systems, providing healthcare, and working in government. Data scientist, data manager, biostatistics scientist, machine learning engineer, and clinical data scientist are some examples of job titles.
• Establish a Solid Foundation in Math and Statistics
• Learn to Program Using Python and R
• Learning About Databases
• Study data analysis techniques
• Discover, Practice, and Repeat
• Develop Your Data Science Skills
• Work on projects involving data science
• Learn how to tell data stories
• Never Stop Learning
A bachelor’s degree from an approved institution is normally required to be eligible for a master’s in data science program. It’s not always necessary, but it can be beneficial to have a bachelor’s degree in data science or a closely related subject like computer science, cybersecurity, accounting, mathematics, or statistics.
The quick response is no. The different programming languages and tools you’ll come across along the way may cause you to get confused or overwhelmed, but a master’s in data science could help you grow in your data science profession more effectively.
Data science is a challenging subject.
Job opportunities:
• Machine learning engineer
• Big data engineer
• Data modeler
• Data scientist
• Software engineer
• Data engineer
• Artificial intelligence engineer
• Data analyst
A data science online master’s degree is not worthwhile because abilities and experience are more important than degrees.
• Business, finance, or economics
• Computer science
• Statistics
• Biostatistics
• Physics
• Computer information systems
• Data/analytics
• Information technology
• Applied mathematics
• Engineering (mechanical, electrical, or industrial)
• Harvard University
• Cornell University
• Princeton University
• Carnegie Mellon University
• Purdue University
• Georgia Institute of Technology
• University of Michigan
• Massachusetts Institute of Technology
• University of Illinois Urbana-Champaign
• University of California, Berkeley
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| Pros | Cons |
|---|---|
| ms ds — mS-DS Master of Data science credential is recognized by employers and industry professionals | Preparation requires significant time and study commitment |
| Higher earning potential compared to non-credentialed peers | Associated costs for exams, materials, and renewal fees |
| Expanded career opportunities and professional advancement | Continuing education needed to maintain credentials |
| Structured learning path builds comprehensive knowledge | Competition for advanced positions can be challenging |
| Professional development that stays current with industry standards | Requirements and standards may vary by state or region |
Try these questions from our free MS-DS Master of Data science practice tests. The correct answer and an explanation follow each question.
A data scientist trains a decision tree model. They observe that the model achieves 99% accuracy on the training data but only 70% accuracy on the test data. The performance on the training data is excellent, but the performance on the test data is significantly worse. This situation is a classic example of what?
Answer: D. High variance (Overfitting)
High variance, or overfitting, occurs when a model learns the training data too well, including its noise and random fluctuations, rather than the underlying general patterns. This results in excellent performance on the training data but poor performance on new, unseen data (the test set).
A data engineering team is building a unified data processing pipeline. They need a framework that can handle both batch jobs on historical data and real-time processing of incoming data streams using a single, consistent API. This approach would allow them to reuse code and simplify logic. Which framework is explicitly designed to unify these two processing models?
Answer: C. Apache Spark
Apache Spark provides a unified engine that supports both batch and stream processing through its high-level APIs like DataFrames and Spark SQL. Spark's Structured Streaming module treats a live data stream as a continuously appended table, allowing developers to use the same batch-like queries on streaming data. This unification is a core design principle of Spark. While Flink is a true stream processor, and Kafka Streams is a library for Kafka-centric streaming, Spark is most known for its powerful, unified batch and streaming API. YARN is a resource manager, not a processing framework.
A dataset containing sensor readings has missing values in a 'Temperature' column. The data is missing completely at random (MCAR), and the overall temperature distribution is not heavily skewed. To handle the missing data, a data scientist decides to use an imputation method. Which of the following is a simple, yet reasonable, imputation strategy in this scenario?
Answer: C. Replace missing values with the mean of the 'Temperature' column
For numerical data that is missing completely at random and has a symmetric distribution, mean imputation is a straightforward and common technique. [17, 22] It replaces the missing values with the average of the observed values, which can be a reasonable estimate that doesn't significantly distort the overall distribution. [18] Deleting rows can lead to a significant loss of information, and replacing with a constant can introduce bias. KNN Imputation is a more complex method that, while powerful, might be unnecessarily complex for this simple case.
You generated a document term matrix from the input data of 100K documents while developing a machine learning model on text data. Which of the following remedies can be used to reduce the dimensions of data – 1. Latent Dirichlet Allocation 2. Latent Semantic Indexing 3. Keyword Normalization
Answer: C. 1, 2, and 3
All three listed remedies are effective for reducing the dimensionality of a document-term matrix. Latent Dirichlet Allocation (LDA) and Latent Semantic Indexing (LSI) are both topic modeling and dimensionality reduction techniques that transform high-dimensional term representations into lower-dimensional topic or semantic spaces. Keyword normalization, which includes stemming, lemmatization, and lowercasing, directly reduces the number of unique terms in the vocabulary, thereby decreasing the number of columns in the matrix.