Statistical Programming for Academic Projects
2 Days
·Cohort-based Course
Apply the best algorithms to your dataset to get significant conclusions and customize the visualizations for your paper submission.
Statistical Programming for Academic Projects
2 Days
·Cohort-based Course
Apply the best algorithms to your dataset to get significant conclusions and customize the visualizations for your paper submission.
Hosted by
Jesús López
Statistical Programmer Consultant | +100 PhDs Statistical Analysis Programmed
Course overview
Make the computer work for you, not against you.
A personalized training experience to identify the bad habits in programming that hold you back from finishing your projects and explain the best practices that will put you back on track to finish them.
Based on vast experience working with PhDs, we've designed a program with exercises that will help you solve the main issues you face while programming the stats of your papers.
Make the most of your time
01
You get frustrated repeatedly by asking Google and ChatGPT the same questions and getting confused about what you know and don't.
02
You lack the statistical intuition to interpret the results and the programming skills to visualize them concisely for paper submission.
03
You've got scripts everywhere, costing you valuable time whenever you want to reuse them. Instead, refactor for single script execution.
Get statistical reports done
Hands-on training
Learn through practice, not theoretical slides that induce you to assume you know what you don't; therefore, you get lost when it's time to program on your own.
Single script (template) to simulate statistical reports based on multiple conditions
Create a versatile script template to generate reports under different conditions, allowing precise benchmarking and replication.
Data visualizations personalized for paper submission
Learn to craft customized data visualizations for academic publications, enhancing the clarity and impact of your findings.
Intuition to determine which are the best algorithms to achieve the conclusions you need
Master selecting and applying the best statistical algorithms for robust and reliable conclusions.
Programming discipline to reason the code instead of copy-pasting by memorization
Understand coding principles and best practices to write clear, efficient, and maintainable code, reducing dependency on ad-hoc solutions.
Confidence in your statistical knowledge to discuss with peers
Gain the assurance to effectively communicate and defend your statistical analyses in academic discussions and peer reviews.
What’s included
Live sessions
Learn directly from Jesús López in a real-time, interactive format.
Lifetime access
Go back to course content and recordings whenever you need to.
Community of peers
Stay accountable and share insights with like-minded professionals.
Certificate of completion
Share your new skills with your employer or on LinkedIn.
Maven Guarantee
This course is backed by the Maven Guarantee. Students are eligible for a full refund up until the halfway point of the course.
Statistical Programming for Academic Projects
Course syllabus
3 live sessions • 30 lessons
Week 1
Jul
18
Session 1
Jul
19
Session 2
Jul
20
Session 3
Post-course
Programming Discipline
R vs Python
Data Visualization
Hypothesis Testing
Linear vs Mixed Models
Machine Learning
Reports
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