Data Science For Business Professionals

Course Code: CN-DSBIZ

The ability to identify and respond to changing trends is a hallmark of a successful business. Whether those trends are related to customers and sales, or to regulatory and industry standards, businesses are wise to keep track of the variables that can affect the bottom line. In today's business landscape, data comes from numerous sources and in diverse forms. By leveraging data science concepts and technologies, businesses can mold all of that raw data into information that facilitates decisions to improve and expand the success of the business.

  • Duration: Half Day
  • Level: Fundamentals
  • Technology: Data Science
  • Delivery Method: Classroom or Virtual
  • Training Credits: NA

This course is designed primarily for:

- Business leaders and decision makers, including C-level executives, project managers, HR leaders, Marketing and Sales leaders, and technical sales consultants, who want to increase their knowledge of and familiarity with concepts surrounding data science.

- Anyone who want to know more about basic data science concepts are also candidates for this course.  

- Candidates preparing for the CertNexus DSBIZ (Exam DSZ-110) credential.

To ensure your success in this course, you should have a working knowledge of general business concepts and practices. You should also have a basic understanding of information technology (IT) resources and systems, including networks, computers, and other digital devices used in an enterprise setting.

This associated exam (#DSZ-110) will certify that the candidate has foundational knowledge of Data Science concepts, technologies, and algorithms and applications.

Upon completion of this course, delegates will receive a MIE attendance certificate. 

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Modules

- What is Data Science? 

- Types of Data

- The Data Science Lifecycle

- Data Acquisition and Preparation 

- Data Modeling and Visualization 

- Data Science Roles

- Benefits of Data Science 

- Challenges of Data Science

- Business Use Cases for Data Science