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Certificate in Data Analytics (IT-DATANA-01)

$495.00

Course 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.

Outline

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

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

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

Duration

5 months

Hours

30

Audience

This suite is designed for adult learners who are interested in data analytics.

Certification

Successful Completion Requirement for IACET CEU: Learners must achieve an average test score of at least 70% to meet the minimum successful completion requirement and qualify to receive IACET CEU credit. Learners will have three attempts at all graded assessments.

Language

English

Instructor

The Data Analytics program is supported by Educational Mentors. Our Educational Mentors have practical industry experience in the subject they mentor. Educational Mentors reviews student work, student progress, and interacts with students as needed. They respond to any questions or concerns you might have, as well as encouraging and motivating you to succeed.

Requirements

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

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