Online Graduate Certificate Programs in Data Science

Nov 01, 2019

This article describes some of the core courses included in an online graduate certificate in data science. Read further to understand the details of common courses and general admission requirements.

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A graduate certificate in data science presents advanced data analysis, data management, and visualization skills. Students may get an opportunity to use different statistical and mathematical algorithms to design machine-learning models for drawing useful insights from raw data, across a range of different subjects.

Common Courses in an Online Data Science Graduate Certificate

The typical duration of this graduate certificate is 6 to 12 months, with part-time options available in some schools. Students might need to complete 12-18 credits covering a number of different subjects.

Foundation to Design and Analytical Principles

This course will introduce the data science process from collecting unstructured data to drawing useful insights after processing the data. It might present various software tools, statistical and mathematical methods, and machine learning algorithms for data analysis. It might also introduce programming languages such as R, Python for designing analysis systems, and data visualization.

Big Data Management

Big data management studies typically focus on the fundamentals of the database, query languages, optimization techniques, and performing transactions on large datasets. This course might provide the details of relational databases, NoSQL databases, including various data modeling concepts, and traditional and modern data storage strategies in the context of big data. Best practices related to planning, designing, storing, and managing data repositories for structured, semi-structured, and unstructured data could be covered in this course.

Machine Learning Concepts

This kind of course introduces the fundamentals of machine learning and the concepts related to statistical analysis in machine learning. Different algorithms related to supervised learning and unsupervised learning, such as decision trees, neural networks, deep learning, clustering, and auto-encoders could be a part of this course. It might include some of the widely used tools and programming concepts such as R, Python, Matlab, and SPSS. Hands-on programs to implement machine learning concepts on data might be a part of this course.

Data Mining Concepts

Students can learn basic data mining concepts, different tools, techniques, and algorithms in this course. Topics related to data preparation, data classification, clustering, and performance evaluation of the data mining systems could be included in this course. Statistical methods to analyze the data, cluster analyzation methods, genetic algorithms, and association rule mining could also be discussed. Students might get an opportunity to practice data mining techniques using various advanced software platforms.

Statistical Analysis

Statistical analysis courses allow students to explore details of statistical concepts useful in data analysis. They can learn regression concepts, descriptive methods, probability, and other advanced statistical topics. This course might also introduce the R programming language for implementing statistical methods to address data problems. Various machine learning algorithms using statistics and advanced concepts of mathematical statistics such as distributions, interval estimation, hypothesis testing, and sufficiency could be included. Hands-on programs to design predictive statistical models might be a part of this course.

Admission Requirements

A bachelor's degree is generally mandatory to be accepted into this program. A few schools might require applicants to complete foundational courses in statistics and algebra as a prerequisite. Students might need to submit the official transcripts of undergraduate studies. Additionally, a personal statement, a resume, and three letters of recommendation might be necessary. A few schools expect a minimum GPA of 2.75 or above in undergraduate studies; also, professional work experience might be helpful.

Online graduate certificates in data science can be completed in around 6-12 months and can include course work related to big data management, machine learning concepts, statistical analysis, and data mining and visualization. To apply for this program, students might need to hold a bachelor's degree with a preferable GPA of 2.75 or higher.

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