Graduate Certificate in Data Analytics Online

Nov 07, 2019

Many schools offer graduate certificates in data analytics completely online. Find out more details about these programs, including core courses and general admission requirements.

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A graduate certificate in data analytics covers different statistical, computational, and mathematical methods, algorithms, and software tools for data analysis. Students looking for a foundation in data science for career advancement or master's studies can pursue this program, and many programs are offered online.

Core Courses of an Online Graduate Certificate in Data Analytics

An online graduate certificate in data analytics typically includes three to five courses covering different big data analysis concepts. These programs can range from 12 to 20 credit hours, and some of the course topics commonly found in this program are listed below.

Foundation of Analytics with R Programming

This type of course generally introduces the fundamentals of mathematical and statistical concepts for data analysis. It might also introduce R programming for statistical analysis and graphical representation. Students may learn R programming for hypothesis testing, data summation, plotting, and continuous and multivariate distribution. Students may also explore concepts related to sample analysis, error measurement, and estimation techniques.

Data Visualization Concepts

This type of course focuses on visualization techniques and powerful software tools to present the insights found from analysis. Students can explore color theory, effective communication, and presentation techniques to reveal the evidence found. Students may also get a chance to learn the latest visualization tools and analyze real-world scenarios for understanding different approaches of visualization and communication.

Statistical Analysis

In this course, students may explore advanced statistical concepts useful for quantitative data analysis. Univariate and bivariate concepts, hypothesis testing, central limit theorem, estimation techniques, and time series concepts could be a part of the curriculum. Hands-on programs might be included to work with software tools like R and SPSS to address the data problems. This course might also cover topics related to probability, regression, and applications of statistics in machine learning algorithms.

Data Mining Concepts

Topics related to data collection, classification, data mining techniques, rule mining, and performance evaluation could be discussed in this course. Students can learn various algorithms useful for mining, such as decision trees and regression concepts. Software programming and packages like R, Python, Weka, SQL, and Matlab could be covered to provide practical experience in data training, classification, model creation, testing, and evaluation.

Big Data Management

This kind of course includes data management concepts, tools, and approaches in managing big data of an organization. This course can present data collection techniques, storage concepts, and transformation of raw data to make it suitable for analysis. Data structures, data modeling concepts, and querying techniques in relational and non-relational databases could be a part of this course. Students can get an opportunity to work on case studies or hands-on programs to understand the different approaches and challenges of big data management.

Admission Requirements

Candidates applying for this program may need to have an undergraduate degree from an accredited university. A few schools prefer a bachelor's degree in statistics, mathematics, or a computer-related major. Official transcripts of previous coursework and a cover letter in a specific format might be required. Professional work experience can be an advantage. A resume, along with two to three recommendation letters might also be necessary.

Students opting for an online graduate certificate might take core courses such as data mining and visualization concepts, big data management, statistical analysis, and foundation to programming languages like R and Python. The curriculum of this program includes hands-on programs to provide working experience with different analysis methods, algorithms, and software tools.

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