Part-Time Master's in Business Analytics

A part-time Master of Science in Analytics program equips business professionals with advanced training and technical skills to forward their careers. This article discusses the common courses and entrance requirements for these degree programs. Schools offering Business Intelligence degrees can also be found in these popular choices.

How to Earn a Part-Time Master's Degree in Business Analytics

Part-time Master of Science in Data Analytics programs are designed to help working professionals develop their business acumen and analytics skill-set while maintaining their full-time job. There is a range of scheduling options to meet the needs of part-time students, including programs composed of a few sets of short intensive periods of training, self-paced online programs, and weekend and/or evening programs. Coursework often includes a combination of advanced statistics, data management, analytical modeling, business decision-making and programming (in languages like Python and R, the most commonly used by data analysts). Examples of common courses include:

Introduction to Business Analytics

This course serves as a general introduction to the theories, tools and strategies used in business. Topics may include a general introduction to important techniques, like data mining, data analysis technologies, and automating decision-making processes. Students may have the opportunity to look at case studies of how data can be used to optimize operations and improve business decision-making. Other topics may include predictive modeling, data fitting, model performance analytics, Bayesian reasoning, and text classification.

Advanced Statistics

Because statistics form the basis for understanding how to use data in business settings, most programs begin with core coursework in statistical theory and its applications. In these courses, students will likely learn how to use probability in decision-making, how to use software to visualize data, and how to build regression and multiple regression models. Students practice using mathematical tools in a business context. Modules may include statistical inference, probability models, hypothesis testing, contingency table analysis, and methods of estimation.

Operations Management

In this course, students learn about the most up-to-date techniques that business operators can utilize to organize and optimize the supply chain of their companies. The goal is for students to be able to analyze how different business processes impact an organization's performance and thus provide solutions for improving a business's overall operation. Students will likely learn the significance of common terminologies like process flow, process design, quality, value and cost.

Data Mining

In data mining courses, students learn how to find, extract, clean and make use of large amounts of raw data. One important aspect of data mining is knowledge discovery, wherein students learn to spot pertinent information within raw data. Students must learn how to apply data analysis tools to large data sets. Topics in this course may also include machine learning, data warehousing, and data mining software.

Programming (in R or Python)

Data analysts commonly use programming languages like R and Python to process data. Students may learn how to automatically combine data from different sources, design programs to solve problems, extract online data from multiple sources, and use Python or R in machine learning and artificial intelligence applications.

Business Decision-Making

The desired outcome for business professionals who pursue advanced analytics training is to be able to improve their business decision-making, and they do this by making use of analytics tools and techniques. In this course, students practice synthesizing their growing mathematics, programming and analytics tools in a business context. Students may practice using spreadsheet models with tools like Solver and Crystal Ball. They may also study linear programming, simulation, optimization, regression models, log regressions and the Monte Carlo simulation.

Capstone Project/Practicum

Some programs require students to complete a real-world data analytics project near the end of their course. Students design a project proposal and ask a professor to act as their academic mentor. Once approved, they work with real data from their own organization or one of their university's corporate partners. Students have the opportunity to make use of the technical skills that they have studied throughout their program. After completing the project, students typically showcase their results to a panel of professors (in either a written report or presentation).

Entrance Requirements for an Online Master's Degree in Business Analytics

All applicants must have completed a bachelor's degree with a minimum GPA (usually 2.5 or 3.0). Many universities expect applicants to have completed undergraduate coursework in advanced mathematics and statistics. Experience with a programming language (like C, R, Java, Python or Matlab) gives students an edge. Because part-time programs are often specifically designed for working professionals, some programs prefer or require applicants to have a few years of business experience. To apply, students must send in their undergraduate degree transcripts, GRE test scores, letters of recommendation from previous professors, and a statement of purpose essay.

A Master's of Science in Business Analytics gives employees across many industries a competitive edge. Part-time programs offer flexible options for busy working professionals who wish to advance their careers.

To continue researching, browse degree options below for course curriculum, prerequisites and financial aid information. Or, learn more about the subject by reading the related articles below:

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