MS in Data Analytics from Carnegie Mellon University

Check out the details here about MS in data analytics from Carnegie Mellon University

Carnegie Mellon University is making every possible effort to ensure that students are ready for their next job before they leave college. To this end, the establishment of the MS-DAS program tailored to the needs of tomorrow's scientific leaders becomes one of our priorities.

Authenticating the MS-DAS first is a one-year program. Students will plunge into the MS in Data Analytics program in the fall semester. The MS in Data Analytics program consists of challenging courses, including applied linear algebra, programming skills, machine learning algorithms, statistical methods, and neural networks. All these MS in data analytic tools will help students solve modern science issues. MS in data analytics from Carnegie Mellon University classes will be held at the Mellon College of Science, Department of Statistics, with the Pittsburgh Supercomputing Center, a world leader in high-performance computing and data analytics.

The MS in data analytics course is emphasized with a semester-last project, which is undertaken in collaboration with industry partners, and that is an attempt to ensure that the practitioners have a hands-on experience in the area of data analysis, in which an impact on scientific discovery is achieved. An important factor for data science students is to be prepared for the work; as such, a course will be implemented as a compulsory 6-week mini-course in the spring semester.

Students are required to complete at least 99 units in order to meet the academic criteria for this learning program.

Curriculum Overview

Fall

The semester "1" provides foundational training in mathematical thinking, statistics, and programming, which are the basics needed to model, analyze, and apply machine learning. Students enroll for a number of 6-week mini-courses and some semester courses during one term to study their relevance and importance. In the spring, they proceed to advanced courses, which require deeper understanding and more extensive knowledge.

Mini I (August – October)

21670 – Linear Algebra for Data Science

This course introduces and discusses the most important aspects of linear algebra that are of interest in Data Analytics. The students will be taught mainly to develop their "feeling" for linear algebra. However, these skills are more important than "proofs."

Mini II (October – December)

This consists of skills related to communication and professional development

Full Semester (August – December)

Computational Linear Algebra

The following section is going to start with a review of essential methods in computational linear algebra. The choice of examples for this course naturally revolves around algorithms for solving (dense or large and sparse) linear systems. Brief appreciations of regularization and underdetermined systems will be provided in this section. At the same time, we assume no prior knowledge about numerical analysis or matrix theory we will present when it is needed, conventional methods or outcomes. Besides, most of the materials are "self-contained." Theoretical results will be discussed in the main text, while experimental results will be covered separately, with an emphasis on cost, reliability, and convergence.

21671 Computational Linear Algebra

This is an overview of the computational linear algebra approaches. This course covers topics from the set of algorithms for solving dense or large and sparse linear systems. Regularization and underdetermined systems will be dealt with in detail. Rather than assuming front that the students are already familiar with numerical analysis or matrix theory, we will introduce main methods or results when they are needed. Therefore, many sections stand on their own. Theoretical and experimental results will be carefully put together, considering cost, stability, and convergence.

38615 – Computational Modelling, Statistical Analysis, and Machine Learning in Science

The purpose of this course is to introduce STEM students to the practical aspects of the core concepts and tools of machine learning understandably and intuitively. The class covers the basics of ML, Data Science, and modern statistics, e.g., the bias-variance tradeoff, overfitting, regularization, and generalization both in supervised and unsupervised learning.

Students will be able to select a large dataset from the list of physics, math, biology, and chemistry datasets furnished by PSC and use it throughout the 2-semester of the MS program. Various concepts throughout the course involve students in computerized experiments with the given dataset. Anyway, many teachers will require years of Python or other computing languages to gain more excellent knowledge since scripting will be taught to students at first with simple tasks, which they will then refine.

38614 – Large-Scale Computing in Data Science

Here, the key emphasis will be on instruction on the techniques needed to manipulate and analyze massive data that are generated in circumstances that can be encountered in scientific computing. This course is Python-based and includes modern software engineering tools and techniques as well as an introduction to data science frameworks that can tackle multi-sized problems and work on computing platforms. It is experiential and applies the Spark engine for the mongering of complex and massive scientific data sets. The goal is to take the students through the scale of data analyses and finally get to the essential machine learning on various scalable platforms like supercomputers, virtualization, clouds, etc. Finally, the lesson on functions in TensorFlow is aimed at providing a foundation for deep learning. Among the entry-level materials will be concepts like optimizing performance and concurrency as they move forward. The exercises will be driven by real and currently pressing scientific datasets that line up some of the specific community presentations. It is the course obligatory for data analytics for the science program's MS students.

36600 – Essentials of Statistical Practice for Graduate Students

These are the preparatory courses in statistical methods designed for graduate students across the university, except those coming from the statistics and machine learning fields. Aimed both as a basic introduction to the concepts of probability and statistics and as a way for statisticians to use data, this module serves as an introductory unit to the field. The class will involve distributional data, parameter estimation, hypothesis testing, clustering, and typical regression and classification models. We might discuss some of the particular topics like text mining, experimental design, and time series, although time permits. Students will complete the hands-on portion of the workshop using the R software language.

36617 – Applied Linear Models

With the completion of this class, students should be capable of properly taking datasets from the real world using uniform regression and other associated methods through both R and SAS software. Also, the students need to learn EDA (exploratory data analysis) techniques to comprehend data characteristics. Thereafter, they should be able to develop appropriate models based on their regard for EDA. They should find out whether there are any specific assumptions viol

Spring

Through the second semester, the students will be able to capitalize on the skills from the first semester and go deeper into the scientific area that the students are interested in through the capstone work and electives that the industry partners provided in MCS.

Mini III (January – March)

38612 Information Visualization for Scientists

This classroom paper is mainly focused on data visualization intro and tools. Vivid presentation of the information is portrayed with a limited raster analysis of spatial frame of reference. Through this exercise, the student gets a feeling of how to work with an array of visualization toolkits such as matplotlib, ggplot, and VisIt, which will be accessible from both Python and R. The course is compulsory for the study major in Data Science for the MS.

Full Semester (January – May)

38616 – Neural Networks and Deep Learning in Science

38617 – MS-DAS Capstone Project Course

Electives

This further consists of the following courses such as Fundamentals of Bioinformatics, Algorithms & Advanced Data Structures, Computational Genomics, Molecular Modeling and Computational Chemistry, Special Topics in Computational Chemistry: Digital Molecular Design Studio, Probabilistic Graphical Models, Convex Optimization, Computer Vision, Introduction to Mathematical Finance, Methods of Optimization, Intro to Parallel Computing & Scientific Computation, Advanced Computational Physics, Methods of Statistical Learning, and Biostatistics.

FAQ

Is Carnegie Mellon University a good choice for studying data science?

Carnegie Mellon University is the world's renowned university due to its significance in statistical theory and practice. It has outstanding interdisciplinary applied research, which will prepare students to innovate data and tackle local, global, and national challenges.

Is it a difficult task to get Carnegie Mellon to do a Master's?

Only a small percentage of applicants are admitted to Carnegie Mellon University. Many are highly qualified but are not selected for various reasons, such as funding and capacity constraints.

How prestigious is Carnegie Mellon University?

Carnegie Mellon University is a private research institution located in Pennsylvania. It has 14,000 students from all over the world. The university is ranked as the #22 nationally and ranked as the #3 in most innovative schools.

Which field at Carnegie Mellon University is best?

Carnegie Mellon University is well known for its technology and science programs. It has seven schools and colleges.

How do I get admission to Carnegie Mellon University from India?

To get admission to Carnegie Mellon University, a CGPA of 3.9 in your UG is needed, along with a CMU writing supplement, and you also need to submit scores from English proficiency tests along with other constraints.