Certificate in Data Analytics

This certificate in data analytics provides an overview of topics in statistics and their applications in a variety of fields. This certificate will present the basics of quantitative analysis and its increasing use in today's professional landscape. Learners are exposed to quantitative decision-making tools and techniques, which tie into real-world case studies. This course, offered by our accredited school partners, utilizes games, videos, interactive exercises, quizzes, real world case studies, and other engaging content to ensure rapid mastery of the content and direct application. Course videos and lessons focus on use of both Microsoft Excel and OpenOffice. This certificate will enhance skills in: Applying analytics in decision making Distinguishing good data from bad data Evaluating research techniques to yield the most accurate results Utilizing descriptive statistics in a variety of settings Creating a graphical representation of descriptive statistics Employing forecasting techniques Performing a regression analysis Making recommendations based on analytics Introduction to Data Analysis: Whatever your profession. Whatever your field. As a professional, and certainly as a leader, you will be asked to make a decision based on data. This course will introduce the different types of decisions made in an organizational setting, why quantitative analytics is important and how quality data can affect decision making. Since quantitative analytics is used in various settings, this course also offers insight into how research is used in different sectors and how it varies accordingly. From a management perspective, the course highlights appropriate methods on a case by case basis, and ways to ensure quality and accuracy through design. After completing this module, you should be able to: Explain why quantitative analysis and analytics is important in decision making Explain the types of decisions that can be made analytically in an organizational setting Describe different decision making models and tools Identify the fundamental concepts of measurement including levels of measurement, reliability and validity, errors, measurement and information bias Explain how quality data affects decision making (GIGO principle) Describe methods of ensuring the quality of data Evaluate techniques for ensuring accurate research design Describe how research is used in different settings: business, education, health care, the military, government, nonprofits Explain data management techniques including transforming data, recoding data, and handling missing data Apply appropriate decision making techniques to a specific case Data Analysis for Improving Organizational Performance: Organizational alignment around performance improvement requires effective leadership, communication, and visual tools to keep people engaged in the process and aware of progress updates. Organizations in both the public and private sectors often use tools and frameworks to support this kind of engagement. This course will explain some of these measures, describe the advantages and disadvantages of specific measurements and explain the relationship between assessment and strategy. After completing this module, you should be able to: Explain how performance measures are used in different settings Differentiate among various organizational performance measurements Describe the advantages and disadvantages of KPIs Describe the advantages and disadvantages of the Balanced Scorecard Describe the advantages and disadvantages of a Net Promoter Score Explain the relationship between performance assessment and organizational tactics and strategy Assess the validity of performance measures for an organization based on a brief case study Data Analysis in the Real World: How are data-driven decisions put into practice in the real world? How do these decisions differ when applied to different sectors, such as health care, education and government? This course will provide answers to these questions as well as recommendations for decisions based on data analytics for each sector. The course will begin with an introduction of Big Data and its implications and each section, case studies will bring the concepts to life. After completing this module, you should be able to: Explain the management implications of the use of business intelligence and knowledge management systems Define Big Data and describe its current uses for analysis and future potential and its implications Explain common analytics for business and quality improvement Recommend manufacturing business decisions based on data analytics Explain common analytics used in health care Recommend health care decisions based on data analytics Explain common analytics used in education Recommend educational decisions based in data analytics Explain common analytics used in government Recommend governmental decisions based on data analytics Statistical Process Control: When implemented with careful attention to collaborative data management and decision making, quality management can help deliver value and quality to customers and stakeholders. It can also enable data-driven decision making that helps organizations gain a competitive advantage in the marketplace. This course will introduce the basics of quality management, explaining the difference between quality control and quality assurance, providing methods for application of analysis, showing different applications of the Seven Basic Quality Tools. It all culminates in a brief case study, which illustrates the concepts covered. After completing this module, you should be able to: Describe principles that help guide quality management activities Use the Plan-Do-Check-Act cycle to coordinate work and implement change Explain the differences between quality control and quality assurance Create a SIPOC diagram to help visualize work as a process Explain the role that metrics and statistics play in measuring and controlling work processes Apply analysis and planning approaches to quality Explain how the Seven Basic Quality Tools are used to monitor and control quality processes Use the Seven Basic Quality Tools to process and sort non-numerical data Use the Seven Basic Quality Tools in combination to create powerful plans and solutions to quality problems Describe various quality management programs Employ quality management tools based on a brief case study Statistics as a Managerial Tool: Today, instinct is not enough to manage the flood of available data and the complexities of the business world. Statistics helps today's leaders make sense of these complexities, back-up their assertions, and feel confident about when to take the risks that lead to successful outcomes. This course examines statistics as a managerial tool. It also looks at common graphical representations of data and how these can be effective tools to explain situations and support persuasive arguments for a course of action. After completing this module, you should be able to: Describe how statistics are used in different settings Describe common problems with, and misuse of, statistics Identify criteria for evaluating statistics Explain the key fundamentals of probability and their real-world application Identify the fundamental concepts of descriptive statistics (populations and samples, measures of central tendency, measures of variability, measures of distribution) and their real-world application Select appropriate graphic methods for displaying descriptive statistics Explain the fundamental concepts of inferential statistics and their real-world application Evaluate a scenario in order to determine the appropriate statistic to use Apply fundamental statistics to a real-world situation Evaluate the appropriateness of statistics used Use statistics to identify the most appropriate decision alternative Translate statistical data into a graphical presentation based on a brief case study Tools of Data Analysis: There are a number of statistical tools and techniques that are commonly used by organizations to inform decision making. These tools span numerous business functions and support many different objectives. This course describes, evaluates, and analyzes different statistical techniques and their real-world limitations and benefits. The course features crossover analysis, break-even analysis, cluster analysis, decision analysis as well as an introduction to regression. After completing this module, you should be able to: Evaluate the usefulness of different statistical techniques and their real-world application Describe the various forecasting techniques and the benefits and limitations Describe the various types of regression analysis and their real-world application Analyze the results of a regression analysis Describe common problems with multiple regression Describe other statistical techniques and their real-world application Explain the advantages and disadvantages of various statistical techniques Choose a statistical technique based on a brief case study Enroll through one of our accredited university or college partners today!

Course modules: • Introduction to Data Analysis • Tools of Data Analysis • Data Analysis for Improving Organizational Performance • Data Analysis in the Real World • Statistical Process Control • Statistics as a Managerial Tool

All required reference materials are provided with this program. Technical requirements: Internet Connection • Broadband or High-Speed (DSL, Cable, Wireless) Hardware Requirements • Processor - 2GHz Processor or Higher • Memory - 1 GB RAM Minimum Recommended Software Requirements • Operating Systems - Windows 7, 8 or 10; Mac OS x 10 or higher • Microsoft Office 2007, 2010 or 2013 or a Word Processing application to save and open Microsoft Office formats (.doc, .docx, .xls, .xlsx, .ppt, .pptx) • Internet Browsers - Google Chrome is highly recommended • Cookies MUST be enabled • Pop-ups MUST be allowed (Pop-up Blocker disabled) • Adobe PDF Reader

This class is an independent-study course. Students will have all the resources needed to successfully complete the course within the online material. A student helpdesk is available for technical support during the course enrollment.