Online Course free
Intro to Revenue Analytics

Improve your business acumen: Acquire revenue analytics skills to drive topline growth and informed decision-making

4.9

808 reviews on
2308 students already have enrolled
  • Institute of Analytics
  • The Association of Data Scientists
  • E-Learning Quality Network
  • European Agency for Higher Education and Accreditation
  • Global Association of Online Trainers and Examiners

Skill level:

Basic

Duration:

1 hour
  • Lessons (1 hour)

CPE credits:

1.5
CPE stands for Continuing Professional Education and represents the mandatory credits a wide range of professionals must earn to maintain their licenses and stay current with regulations and best practices. One CPE credit typically equals 50 minutes of learning. For more details, visit NASBA's official website: www.nasbaregistry.org

Accredited:

certificate

What You Learn

  • Understand key revenue concepts and metrics.
  • Calculate and analyze revenue growth and retention.
  • Improve your understanding of the revenue generation cycle.
  • Learn the basics of pricing and its role in business strategy.
  • Leverage customer success analytics to boost retention.

Topics & tools

revenue analyticspricing analyticscustomer success analyticstheorybusiness skills

Your instructor

Course OVERVIEW

Description

CPE Credits: 1.5 Field of Study: Specialized Knowledge
Delivery Method: QAS Self Study
Are you looking for a beginner-friendly revenue analytics course? Would you like to improve your revenue analytics skills in just a few hours? If so, then this is the revenue analytics training for you. Acquire the business skills analysts need in their early days on the job. Understand how businesses create value for customers, measure revenue growth and retention, analyze a company’s pricing, and become familiar with the revenue streams a company could use to generate income. Moreover, this course allows you to dissect the revneue model to identify the levers that can propel growth. This includes identifying key KPIs, crucial pricing and customer success analytics. How do you generate new sales, how do you increase retention? This revenue analytics course is ideal for aspiring analysts—bridging the gap between technical skills and successful job performance with solid business acumen. It’s one thing to know how to retrieve data from a database and write SQL or Python code to compute calculations, but as a successful analyst, you should be able to analyze data critically through a business lens. If you wish to use data analysis to create business value and drive your company’s sales up, this training will help you build a solid foundation and perform at a high level from day one. What sets this Intro to Revenue Analytics course apart from others? 1. Exceptional Content Quality Enjoy structured learning and a carefully crafted revenue analytics course. The curriculum distills the practical experience of the instructor and teaches you the revenue analytics skills that will allow you to perform at a high level in any corporation. 2. Expert Instructor Sudhir Buddhavarapu—a seasoned Silicon Valley veteran with experience in big enterprises and start-ups, with solid academic qualifications — has served in several leadership roles in the analytics space, ranging from product, marketing, business and revenue analytics. His deep industry knowledge and passion for sharing his knowledge makes him an ideal instructor for this course. 3. Certificate of Achievement Complete the course and pass the exam to earn a verifiable certificate of achievement—a testament to your dedication and hard work. Click the 'Buy Now' button to embark on an incredible revenue analytics learning journey and invest in a career-changing opportunity.

Prerequisites

  • No prior experience or knowledge is required. We’ll start from the basics and gradually build your understanding. Everything you need is included in the course.

Advanced preparation

  • None

Curriculum

44 lessons 10 exercises 1 exam

Free preview

Course introduction

1.1 Course introduction

3 min

Why this course

1.2 Why this course

2 min

Course structure

1.3 Course structure

1 min

Key concepts related to revenue

2.3 Key concepts related to revenue

3 min

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9 in 10

of our graduates landed a new AI & data job

after enrollment

96%

of our students recommend

365 Data Science.

$29,000

average salary increase

after moving to an AI and data science career

ACCREDITED certificates

Craft a resume and LinkedIn profile you’re proud of—featuring certificates recognized by leading global institutions.

Earn CPE-accredited credentials that showcase your dedication, growth, and essential skills—the qualities employers value most.

  • Institute of Analytics
  • The Association of Data Scientists
  • E-Learning Quality Network
  • European Agency for Higher Education and Accreditation
  • Global Association of Online Trainers and Examiners
A LinkedIn profile mockup on a mobile screen showing Parker Maxwell, a Certified Data Analyst, with credentials from 365 Data Science listed under Licenses & Certification. A 365 Data Science Certificate of Achievement awarded to Parker Maxwell for completing the Data Analyst career track, featuring accreditation badges and a gold “Verified Certificate” seal.

How it WORKS

  • Lessons
  • Exercises
  • Projects
  • Practice Exams
  • AI Mock Interviews

Lessons

Learn through short, simple lessons—no prior experience in AI or data science needed.

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Exercises

Reinforce your learning with mini recaps, hands-on coding, flashcards, fill-in-the-blank activities, and other engaging exercises.

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Projects

Tackle real-world AI and data science projects—just like those faced by industry professionals every day.

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Practice Exams

Track your progress and solidify your knowledge with regular practice exams.

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AI Mock Interviews

Prep for interviews with real-world tasks, popular questions, and real-time feedback.

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Student REVIEWS

A collage of student testimonials from 365 Data Science learners, featuring profile photos, names, job titles, and quotes or video play icons, showcasing diverse backgrounds and successful career transitions into AI and data science roles.