Management and analytics degree

Since its inception 20 years ago, business intelligence BI has become a huge industrial area and a significant driver of the economy. It covers many fields of science and technology data warehousing and mining, content analysis, business process management, etc. Pla d'estudis no disponible. Business intelligence BI has become an important industrial domain that encompasses many scientific and technological fields, including data warehouses, data mining, visual and content analytics and business process management. It requires competencies in information systems, Web science, decision science, software engineering, innovation and entrepreneurship.

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What does the future hold for master’s degree programs in business analytics?

According to a recent report by McKinsey , data needs to be treated as one of the products produced within the organization rather than as a raw material that supports decisions. As data-driven business models change the face of industry, developing the ability to capitalize upon this valuable resource is compulsory. While the jobs are there, there is a shortage of analytics-enabled personnel who combine expertise in their industry with the ability to understand and leverage big data to engage in predictive analytics or make data-informed business decisions.

Although solving textbook problems can be a good tool to use, MET has an inclination to use powerful tools and projects that allow a problem and process of problem solving to be brought into the classroom.

There are no classes that will leave a student to wonder where they will apply the concepts they are learning. Since all instructors are experts in their craft, and working professionals, students get to work with tangible perspectives and problems.

This is a great way to develop confident leaders. Students are challenged from the perspective of a decision-maker in many courses. The need to apply critical thinking, and to work through problems with uncertainty, becomes a muscle that is exercised until students become comfortable in leadership situations. These situations are ones in which information needs to be processed and creatively applied to projects that represent the way it would be done in an actual situation.

Practice is primary and theory plays a supporting role with many of the courses, which is highly valuable from my experience. Use the Career Insights tool to explore jobs that are the right fit for you. Filter by career area and job title or by industry sector to explore employment demand and average salaries. Graduates of the program will be able to analyze data-driven business processes, select appropriate analytical methods to monitor and identify performance trends, prescribe possible outcomes, and propose optimal data-driven solutions.

Our self-paced laboratories SPLs are organized in two levels:. Upon successful completion of an SPL, you earn a standardized, digitally verifiable badge in recognition of your performance and visibility to current and future employers. To be eligible for the degree, you must apply for admission and be accepted into the degree program. Consult with a graduate admissions advisor to learn more about this option. Students who already hold the Graduate Certificate in Applied Business Analytics may waive the core course Business Analytics Foundations and three of the specialization courses.

These are resources that will introduce students to the software environments we use throughout the program. Some courses may have additional prerequisites. Prereq: AD Pre-Analytics Laboratory and ADR Introduction to R This course presents fundamental knowledge and skills for applying business analytics to managerial decision-making in corporate environments. Topics include descriptive analytics techniques for categorizing, characterizing, consolidating, and classifying data for conversion into useful information for the purposes of understanding and analyzing business performance , predictive analytics techniques for detection of hidden patterns in large quantities of data to segment and group data into coherent sets in order to predict behavior and trends , prescriptive analytics techniques for identification of best alternatives for maximizing or minimizing business objectives.

Students will learn how to use data effectively to drive rapid, precise, and profitable analytics-based decisions. The framework of using interlinked data inputs, analytics models, and decision-support tools will be applied within a proprietary business analytics shell and demonstrated with examples from different functional areas of the enterprise. This course will provide students with the analytical tools to analyze, manage, and improve manufacturing, service, and business processes.

Quantitative methods include application of stochastic simulation, analysis of random outcomes, statistical analysis routines confidence intervals, hypothesis testing, machine learning , system reliability analysis, and statistical process control.

The Deming philosophy of management, Lean operations principles, and Six Sigma process improvement methodologies form the underlying foundation of the course coverage. Introduction to the concepts, methods and problems of accounting and financial analysis. Includes accounting principles, measurement and disclosure issues, financial statement analysis, time value of money, cash flow projection and analysis, capital budgeting and project evaluation, bond and equity valuation, cost of capital and capital structure.

The purpose of this course is to help improve business problem solving and managerial decision-making through the use of quantitative and qualitative decision-making tools and techniques. This course will provide the student with an overview of how decisions are made to solve management problems in the business environment.

It introduces the fundamental concepts and methodologies of the decision-making process, problem-solving, decision analysis, data collection, probability distribution, evaluation, and prediction methods. Students will learn how to apply different quantitative and qualitative analytical tools commonly used in business to provide a depth of understanding and support to various decision-making activities within each subject area of management.

Through the use of case studies of decisions made by managers in various production and service industries and a business simulation package specifically prepared for this course, the scope and breadth of decision-making in business will be described. Prereq: METAD The course offers an overview of the key current and emerging enterprise risk analytical approaches used by corporations and governmental institutions and is focused on understanding and implementing the enterprise risk management framework on how to leverage the opportunities around a firm to increase firm value.

The major risk categories of the enterprise risk management such as financial risk, strategic risk, and operational risk will be discussed and risk analytics approaches for each of these risks will be covered. Students will learn how to use interlinked data inputs, analytics models, business statistics, optimization techniques, simulation, and decision-support tools. An integrated enterprise risk analytics approach will be demonstrated with examples from different functional areas of the enterprise.

Prereq: METAD Become familiar with the foundations of modern marketing analytics and develop your ability to select, apply, and interpret readily available data on customer purchase behavior, new customer acquisition, current customer retention, and marketing mix optimization. This course explores approaches and techniques to support the managerial decision-making process and skills in using state-of-the- art statistical and analytics tools.

Students will have an opportunity to gain a basic understanding of how transaction and descriptive data are used to construct customer segmentation schemas, build and calibrate predictive models, and quantify the incremental impact of specific marketing actions. The web analytics part of the course studies the metrics of websites, their content, user behavior, and reporting. The Google Analytics tool is used for the collection of website data and doing the analysis.

The text mining module covers the analysis of text including content extraction, string matching, clustering, classification, and recommendation systems.

The web mining module presents how web crawlers process and index the content of websites, how search works, and how results are ranked. Application areas mining the social web and game metrics will be extensively investigated. Prereqs: AD,ADR,AD Enterprises, organizations, and individuals are creating, collecting, and using a massive amount of structured and unstructured data with the goal to convert the information into knowledge, improving the quality and the efficiency of their decision-making process, and better positioning themselves in the highly competitive marketplace.

Data mining is the process of finding, extracting, visualizing, and reporting useful information and insights from both small and large datasets with the help of sophisticated data analysis methods. It is part of business analytics, which refers to the process of leveraging different forms of analytical techniques to achieve desired business outcomes through requiring business relevancy, actionable insight, performance management, and value management.

The students in this course will study the fundamental principles and techniques of data mining. They will learn how to apply advanced models and software applications for data mining. Finally, students will learn how to examine the overall business process of an organization or a project with the goal to understand i the business context where hidden internal and external value is to be identified and captured, and ii exactly what the selected data mining method does. The following courses offered by other Metropolitan College departments are some of the elective courses allowed with advisor approval:.

This course expands upon the foundations of finance theory with interdisciplinary approaches from statistical physics and machine learning. The course equips the students with the Python tools to tackle a broad range of problems in quantitative financial analysis and combines the study of relevant financial concepts with computational implementations. Students will learn to use packages like Numpy, Pandas, Statsmodels and Scikit, which are commonly used in research and in the industry.

This course covers the concepts of the object-oriented approach to software design and development using Python. It includes a detailed discussion of programming concepts starting with the fundamentals of data types, control structures methods, classes, arrays and strings, and proceeding to advanced topics such as inheritance and polymorphism, creating user interfaces, exceptions and streams.

Upon completion of this course students will be able to apply software engineering principles to design and implement Python applications that can be used in with analytics and big data.

This course provides an overview of the statistical tools most commonly used to process, analyze, and visualize data.

Topics include simple linear regression, multiple regression, logistic regression, analysis of variance, and survival analysis. These topics are explored using the statistical package R, with a focus on understanding how to use and interpret output from this software as well as how to visualize results.

In each topic area, the methodology, including underlying assumptions and the mechanics of how it all works along with appropriate interpretation of the results, are discussed. Concepts are presented in context of real world examples. This course provides a theoretical yet modern presentation of database topics ranging from Data and Object Modeling, relational algebra and normalization to advanced topics such as how to develop Web-based database applications.

Other topics covered - relational data model, SQL and manipulating relational data; applications programming for relational databases; physical characteristics of databases; achieving performance and reliability with database systems; object- oriented database systems.

Refer to your Department for further details. Students learn the latest relational and object-relational tools and techniques for persistent data and object modeling and management. Students design and implement a database system as a term project. Only one of these courses can be counted towards degree requirements. Students will learn major Python tools and techniques for data analysis.

There are weekly assignments and mini projects on topics covered in class. These assignments will help build necessary statistical, visualization and other data science skills for effective use of data science in a variety of applications including finance, text processing, time series analysis and recommendation systems.

In addition, students will choose a topic for a final project and present it on the last day of class. Or, instructor's consent. Assistant Professor, Administrative Sciences. Master Lecturer, Administrative Sciences.

Learn More. Please visit the BU MET admissions page for details on how to apply, financial assistance, tuition and fees, requirements for international students, and more. MS in Applied Business Analytics. Apply Now Request Information. Request Information Stay connected! Learn more about our program. Harris Assistant Professor, Administrative Sciences. Competitive Tuition Our part-time rates are substantially lower than those of the traditional, full-time residential programs yet provide access to the same high-quality BU education.

Comprehensive Financial Assistance Our services include scholarships , graduate loans, and payment plans. Get Started Please visit the BU MET admissions page for details on how to apply, financial assistance, tuition and fees, requirements for international students, and more. Apply Now.


Graduate Certificate in Management Analytics

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Data engineer ($92,) · IT project manager ($88,) · Database administrator or database architect ($73,) · Computer systems analyst ($67,) · Data analyst.

Master of Management Analytics

Employers need skilled individuals who can translate big data into recommendations for profitable actions. The Master of Science in Business Analytics degree program provides students with the practical and theoretical knowledge needed to pursue careers involving a wide variety of data science, data engineering and data analytics roles within a number of business domains. It thus ensures that students master essential skills in business analytics to meet the growing needs of 21 st -century businesses. Recent graduates have found jobs such as:. Please take note of all application deadlines and visit the Apply Now webpage to begin the application process. The University of Texas at Dallas respects your right to privacy. By submitting this form, you consent to receive emails and calls from a representative of the University. We have received your request for more information. Our admissions team will contact you soon to share details about pursuing your academic goals at UT Dallas. Master of Science in Business Analytics.

MSc Management & Analytics

management and analytics degree

Experience » M. Meet M. I have gained hands-on practice using the most up-to-date analytical tools and have completed various data-driven technical projects that are relevant to real-word business problems. Business analytics, which combines statistics, information technology and business coursework to assist in business decision making, differs from data science, which focuses more on the technical aspects and includes computer programming.

Data is king in today's digital economy.

MS in Business Analytics and Project Management

Get the tools to take on one of the greatest management challenges in the age of digital technology and artificial intelligence: become an expert in analytics and data science. The Institute for Data Valorization IVADO brings together industry professionals and academic researchers to develop expertise in the fields of data science, optimization operational research and artificial intelligence. This is evidenced by examples of recently proposed supervised projects. Are you looking to develop skills in analytical and quantitative techniques to support decision-making in an international context? Spend a term at one of the institutions in the prestigious QTEM network and earn your QTEM certification in quantitative technology in economics and management. Learn about the program and eligibility criteria.

Master of Business Analytics

Begin your application today by entering the Graduate Admissions Portal. Submit your application by:. This program admits only once a year. Apply early to be considered for competitive assistantships that provide financial assistance. Email Searra Lippard , academic program specialist, for more information about the admissions process. Graduate Programs Office toll free gradprograms business. Surging growth in digital information means businesses are seeking graduates who can transform this raw data into trusted analysis used to develop new business strategies. New jobs for business analytic professionals are expected to exceed 35, in the next three years, an increase of more than 15 percent, according to a recent report supported by IBM and the Business Higher Education Forum.

The online data science and marketing analytics degree program will build a thorough Organizational Behavior and Human Resources Management.

Master in Data Analytics & Management

As we welcome students, staff and visitors back to campus, we require those attending Melbourne Business School in person to be fully vaccinated. Our degree for aspiring data professionals, with a focus on personal skills as well as technical expertise. To succeed in this environment, our Master of Business Analytics teaches students to become trilingual — fluent in the languages of technology, mathematics and business. Through an intensive one-year program, you will learn how to define and structure business problems, use data to provide insight and communicate those insights to senior leaders.

The demand for people technically skilled in business analytics and data science is relentless as companies compete in a digital world. Our STEM designation assures that you will be equipped with a strong analytical skill set that can help advance your career in a science and technology-driven world. Experience the full life cycle of a business analytics project. Your MSBA coursework is always grounded in a real-world business context and focuses on three areas: collecting, cleaning, visualizing and analyzing data; using statistical, machine-learning and optimization tools; leveraging data and analysis to drive business decisions and drive value.

From social networks to mobile devices, and from real-time sensors to purchase transactions, the pace of data generation is growing exponentially. The Carlson School's Master of Science in Business Analytics MSBA program teaches students how to harvest, process, and analyze data to answer important questions and solve real business problems in an increasingly data-driven world.

These three graduate level courses will count toward both the undergraduate and graduate degree programs. Students will develop key competencies that enable them to perform core tasks applying data analytic tools and techniques to any business function. These include analyzing market strategies, synthesizing and managing timely flow of current business intelligence to support recommendations for action, identifying, analyzing and communicating industry, technology or geographic trends with business strategy implications, and generating reports summarizing business, financial or economic data for review key stakeholders. Students may be able to obtain both degrees in an expedited period, typically in five years. Students must formally apply and be admitted to the Department of Management before beginning their graduate coursework. Undergraduate students who meet the following criteria may apply to this program:. Students participating in this program will typically take three of the following graduate level courses during their senior year:.

Degree info for International students. Start date February August. Campus City West. Duration 1.

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