Online Data Science Certificate Programs

May 29, 2020

General Information for an Online Data Science Certificate Program

An online data science certificate program typically covers concepts such as data-driven prediction, data collection, data modeling, computational statistics, visualization, pattern recognition, and machine learning. These programs might introduce students to several data analysis and presentation tools that use high-level programming languages like Python or R to help data science professionals advance in their careers.

There are online certificates available in data science at both the undergraduate and graduate levels. Though many online data science certificate programs have no degree requirements for admission, strong knowledge of statistics and prior exposure to popular programming languages likes Java, C+, Python, or R might be required. Although the typical duration of these certificate programs is 12-17 months, there are schools offering data science certificates that can be completed in as few as 9 months. Students can complete many of these certificates in a 100% online format.

Online Data Science Certificate Program Requirements

Online data science certificates mainly deal with computational statistics, big data concepts, machine learning methods, and several data management methodologies. The curriculum typically includes around 4 or 5 courses and 10 to 15 credits, which might include several required and elective courses. In some schools, students may need to complete the curriculum with a minimum grade point average of 2.5 or more.

Core Courses in a Data Science Certificate

Big Data Management

Courses like these explore big data concepts and introduce several methods and tools to manage and process large data sets. They also typically discuss privacy issues with big data, management of multiple databases, and concepts in storage. The design of data storage systems for large data sets, data warehouse concepts, and the use of Hadoop to handle big data could also be a part of this course.

Python for Data Science

These courses introduce the Python language and discuss its features, including basic syntax, data types, variables, functions, loops, and input and output operations. It also might dive into python packages useful for data analysis, like Numpy, Pandas, Scipy, and Matplotlib, and explain how to use these packages to conduct analytical and statistical analysis. It may also include a hands-on project to provide practical training in the language.

R Programming Concepts

In this course, students have a chance to learn the fundamentals of R programming, such as basic syntax, data types, vectors arithmetic, matrices, lists, and indexing. It also aims to teach how to use R for data operations like sorting, wriggling, plot making, visualization, and several advanced graphical capabilities. Using R programming for statistical operations, such as regression, hypothesis testing, inferential statistics, correlation, and matrix operations, could also be a part of this course.

Career Overview of Data Scientists

A data science certificate could offer some of the training required for positions like data engineers, data analysts, big data admins, and data scientists. The career growth of data scientists may depend on several factors like educational qualifications, work experience, and certifications, but a certificate might help current professionals become more qualified for higher positions in the field. It's important to note that students may need to hold at least a bachelor's degree to get an entry-level job and a master's degree for management positions in data science.

As per the U.S. Bureau of Labor Statistics (BLS) in 2019, the median pay for computer information and research scientists in general was $122,840 per year, which equates to $59.06 per hour. The job growth is predicted to be 16% between 2018 and 2028, which is quite a bit faster than the average for all occupations.

An online data science certificate program is designed to help students understand data analysis concepts, programming languages, and the design of data analysis systems. These programs usually take around 12-16 months to complete.

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