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SP-EN Statistical Programming for Academic Papers

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.

This course includes

3 interactive live sessions

Lifetime access to course materials

30 in-depth lessons

Direct access to instructor

Projects to apply learnings

Guided feedback & reflection

Private community of peers

Course certificate upon completion

Maven Satisfaction Guarantee

This course is backed by Maven’s guarantee. You can receive a full refund within 14 days after the course ends, provided you meet the completion criteria in our refund policy.

Course syllabus

Expand all modules

  • Week 1

    Jul 18—Jul 20

    Events

    • Jul

      18

      Session 1

      Thu, Jul 18, 8:00 PM - 11:00 PM UTC

    • Jul

      19

      Session 2

      Fri, Jul 19, 8:00 PM - 11:00 PM UTC

    • Jul

      20

      Session 3

      Sat, Jul 20, 3:00 PM - 6:00 PM UTC

  • Post-Course

    Modules

    • Programming Discipline

    • R vs Python

    • Data Visualization

    • Hypothesis Testing

    • Linear vs Mixed Models

    • Machine Learning

    • Reports

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