Data Analytics

Analyze data to make more informed business decisions.

Have you heard the term big data? According to Forbes, “all companies are data businesses now”. You will gain the skills needed to drive value and revenue from your companies data and help you drive your career forward.

Who:  Working professionals interested in developing and enhancing their data analysis skillset will benefit from this 3-course track, which prepares students to collect, analyze, and take action on data from a variety of sources.

Why: Businesses around the world rely on quality data analytics for success. The International Institute for Analytics predicts that, “More companies will attempt to drive value and revenue from their data. Businesses using data will see $430 billion in productivity benefits over their competition not using data by 2020.” But this shift to data benefits more than just companies, Glassdoor.com reports the national average salary for a data analyst is $76, 419 as of January 2018, and the field is hotter than ever with new jobs opportunities opening up every day.

With this program you will

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Get where you want to be

Aspire to be a manager or supervisor one day? This certificate program is a great way to lay the groundwork for evolving your career.

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Get there faster

Time is money. So our courses are offered in accelerated seven-week sessions.

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Build skills you’ll use today

With a curriculum that’s always current and a faculty of industry experts, you’ll develop valued skills you’ll use in your career right now.

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Learn how you want & when you can

Seamlessly balance your education with the rest of your life. Choose how you want to learn each week—in class or online in real time.

Courses

    • MBA-733 Statistical Modeling for Managers Using R and SAS

      The purpose of this course is to prepare MBA students for a career that requires dissemination of statistical information and it provides the foundation for statistical analysis. In this course, we will explore basic descriptive statistics and exploratory data analysis in both Excel and R Studio with emphasis on R Studio (students will be given starter code that they will then modify for their needs prior coding experience is not mandatory).

      Course Objectives:

      1. Students will experiment with a variety of data sets for the purpose of performing Exploratory Data Analysis as a precursor to modeling
      2. Students will apply and then interpret descriptive statistics in SAS, R, and Excel
      3. Students will apply the knowledge from EDA and descriptive statistics to develop a variety of statistical models
      4. Students will create business questions relevant to a given data set, perform EDA, and provide a statistical report of the data with a recommendation and next steps
    • MBA-743 Data Visualization and Business Intelligence for Managers

      Data visualization is foundational to analytics. The best models in the world can be rendered useless if no one can understand them. The students will learn the best practices of data visualization and will gain hands on experience with creating dashboards and stories with data in Excel, Power Point, Power BI, and Tableau. This is a project-based class.

      Course Objectives:

      1. Students will learn to evaluate data visualization and report preparation in Power Point and Tableau
      2. Students will create and evaluate basic static graphs in Excel, Tableau, and Power Point
      3. Students will choose a data set to experiment with and create a dynamic dashboard in Tableau, or Excel for the purpose of storytelling; they will interpret the data and explain the results to their classmates
    • MBA-753 Applied Statistics with Case Studies

      The purpose of the course is to prepare students for leadership positions where they are managing data scientists, business analysts, statistician and the like or in enhancing their current skills in order to grow within their field.  Course content includes, sampling, linear regression, probability, discrete probability distributions, sample size calculations, parameter estimation including confidence intervals, hypothesis testing, one and two-sample statistical inference, chi-square tests and ANOVA, and an introduction to some basic machine learning techniques.

      Course Objectives:

      1. Describe the goals of various statistical methodologies conceptually
      2. Apply statistical techniques in the context of everyday life and further studies in their discipline
      3. Use inferential statistics to make valid judgments based on the data available and defend their position
      4. Select the appropriate algorithms to analyze a particular problem
      5. Students will explore a variety of data sets and they will be provided snippets of code and they will be required complete the coding in order to execute modeling for the purpose of interpretation