CIMP in Data Science

Data Science is an interdisciplinary field that uses scientific methods to extract knowledge from data

Data science is a broad field that harnesses many disciplines to derive insights and create value from data. The multi-disciplinary field involves mathematics, statistics, advanced algorithmic computing, data mining, artificial intelligence, machine learning, data engineering, data preparation, data visualization, and application of scientific method—all within the context of business domains to which it is applied. Through applied data science, organizations are able to predict and forecast, detect patterns and anomalies, make recommendations, optimize and automate processes, and much more. Data science allows analysts, decision makers and other business professionals to challenge assumptions, ask challenging questions, frame analytical opportunities, test hypothesis and deepen their understanding of how their customers, markets, assets and competitors behave.

The frequently used and often misunderstood term data scientist is not a one-size-fits-all role. Data science involves many personas including leaders and strategists, analytic modelers, statisticians, data analysts, and data engineers. The eLearningCurve curriculum for data science is designed to provide a foundation of fundamental concepts, cover the diverse needs of many data science personas, and continuously evolve with changes in this rapidly evolving field.

Sixteen online Data Science courses.

Each course is accompanied by a Certified Information Management Professional (CIMP) exam.

Click on the course links to explore course details and outlines, learn more about the exams, or watch free Sneak Peeks.

You can purchase the courses individually or enrol in one of our comprehensive Education Packages at a great discount.

 In order to meet CIMP requirements, you must complete Data Science Fundamentals and at least two other "core" courses.

Product Code

Courses (in alphabetical order)

CIMP Track

Click on the links for course details


SC-09 Analytical Modeling, Evaluation, and Deployment Best Practices  
SC-06 Artificial Intelligence Fundamentals C


Data Mining Concepts & Techniques


BA-03 Data Mining in R  
SC-01 Data Science Fundamentals F
SC-05 Data Understanding and Preparation for Data Science C
DQ-07 Diagnostic Analytics Using Statistical Process Control  


Framing and Planning Data Science Projects


SC-10 Fundamentals of Machine Learning  
SC-03 Hadoop Fundamentals  
BD-02 Introduction to NoSQL  


Prescriptive Analytics Using Simulation Models  
SC-04 Putting the Science in Data Science  
SC-07 Streaming Data: Concepts, Applications, & Technologies  

Our comprehensive information management curriculum

A sound foundation for you and your team



What is a Certified Information Management Professional?



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