Individuals who have a basic statistical and analytical background. It is best suited for those with the capability of leveraging applied science with R. Participants should be able to utilize these skill sets to conduct data visualization, data analysis, and statistical/predictive models, besides, present final reports by different R functions and packages.
Steps to take exam
- Visit our certification page: https://www.ideassn.org/certificate/
- Register at ideas for purchasing certification. (If you already have an account, you can skip this step)
- Purchase the certification test you want to take.
- During your purchasing process, please clarify the time at which you want to take the exam in ADDITIONAL INFORMATION BOX. (the duration should no longer than 1 MONTH)
- For example, you can write: 2/2/2019-3/2/2019, then you will receive a link which is continuously activated during this time, and you can pick any time to start your exam.
- The system will send you a confirmation mail once you finish the order
- After the order confirmation, you will get the link via email to access the test (the link is activated during your preferred test duration.)
- This certification test could be taken anytime and anywhere, but you may only take it once, and the time duration for the exam is 2hr.
Pass the Test and Get Certification
- You will receive your grade via email within 1 week of submitting your test.
- You need to get at least 90% to pass this test.
- Congratulations! You will get our certification if you pass the test.
Exam duration – 2 hour
- 1 hour technical questions
- 1 hour project challenge
Basic Theory: Statistics, Probability, Data Mining, Data Processing, Data Visualization, Machine Learning modeling
Tools and Software Requirements: R
Prerequisite Skills: Ability to utilize R to generate data visualization reports through implementing data control, data processing, data cleaning, analysis, modeling and related tasks.
- Foundations of R (data types, data frames, loops, basic graphs)
- Data Manipulation Packages (dplyr, tidyr)
- Data Visualization Packages(ggplot2, Rshiny)
- Basic Machine Learning (Linear Regression, Logistic Regression, Decision Tree, Random Forest…)
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