A master's degree in data science provides the necessary skills and hands-on experience required to become a data scientist. Taking classes part-time provides a flexible option for working professionals to develop the high-level skills required to start or advance careers in this field.
Core Courses in a Part-Time Master's Degree in Data Science
A master's program in data science enables students to design predictive analysis models to draw useful insights from structured, semi-structured, and unstructured data. In a part-time format, classes can be offered on the weekends, in the evenings, or entirely online, taking students between two and three years to complete; explore some common course topics below.
This kind of course typically includes topics related to designing and analyzing computer algorithms useful for data management. It might include advanced data structures, loop invariants, sorting, dynamic programming concepts, matching, filtering, and searching techniques. Newton and quasi-Newton methods, graph algorithms, greedy algorithms, NP-completeness, and hashing techniques could also be a part of this course.
Database Systems and Data Processing
In this type of course students can learn about relational and hierarchical database systems as well as network databases. Database design concepts, database models, data warehousing, query languages, optimization, and tuning techniques may be part of this program. Students may get an opportunity to learn methods related to data collection, data transformation, data cleaning, and data processing in relational and distributed data systems using high-level languages such as SQL, Python, and R. This course may explore data compression and encryption methods as well as advanced security measures for protecting database systems and securely processing data.
Data Visualization Techniques
This type of course introduces different software tools and techniques for presenting and communicating the patterns and insights of data after a thorough analysis. It may discuss the fundamental techniques for a visual representation of data from various fields of study, such as health sciences, physics, business, technology, economics, and biology. Different visualization methods such as parallel coordinate plots, continuous variables, clustering and classification, ggobi, dynamic graphics, linked pots, brushing, mosaic pots, and model visualization may be part of this course.
Machine Learning Concepts for Data Science
In this course, students generally get detailed exposure to supervised and unsupervised learning theories in machine learning. They can understand different machine learning algorithms such as generalized additive models, decision trees, local regression, k-means clustering, support vector machines, and random forests for training computers in analyzing and identifying patterns in large data sets. Students may get an opportunity to work on multiple hands-on programs to design machine learning systems using high-level computer programming languages like R, Python, and MATLAB.
Statistical Analysis of Data Science
This course may include advanced concepts for statistical computing and analysis of data. It might focus on topics related to inferential statistics, linear and logistic regression, continuous and random variables, correlation, interval estimation, and hypothesis testing. Probability models, distribution concepts, central limit theorem, sample analysis, and expectations methods could be included in this course. It may also cover different computer programming languages and tools useful for statistical analysis of large data sets.
Students are generally expected to hold an undergraduate degree to be eligible for a part-time master's program in data science. An undergraduate degree in mathematics, statistics, or computer science might be an advantage, or students may need to have coursework in these subjects. The official transcripts of previous schools and GRE scores might be necessary along with a personal statement and letters of recommendation. Some schools may also require professional experience as well as an interview.
A part-time master's program in data science explores different mathematical, statistical, and computational concepts to analyze large data sets to find meaningful patterns. Students can get a chance to learn high-level programming languages like Python and R to design machine learning systems, data processing techniques, and presentation skills.