Non-credit and Continuing Education
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Certificate in Data Analytics

ID : 5083   
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Language: English

Description: 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<

This course does not require any additional purchases of supplementary materials.

Class Details

Online 24/7

NA - Online

Start Date:upon registration



Schedule Information

Date(s) Class Days Times Location Instructor(s)
Online 24/7 N/A - Online

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