Curso avanzado de estadística no paramétrica con R y Python

Master Non-Parametric Statistics with R & Python: Data Analysis & Hypothesis Testing

Unlock powerful data analysis techniques with our comprehensive course on non-parametric statistics using R and Python. This course equips you with essential skills for data analysis, hypothesis testing, and group comparisons – highly sought-after abilities in today's job market (check out LinkedIn job postings!).

What You'll Learn:

• Understanding Non-Parametric Statistics: Discover why non-parametric statistics are crucial when data doesn't meet the assumptions of traditional parametric statistics. Learn when and how to apply these methods for greater flexibility and accuracy.
• Key Non-Parametric Tests: Gain practical expertise in a wide range of tests, including:
  • Anderson-Darling Test: Assess normality.
  • Shapiro-Wilks Test: Another normality test.
  • Levene's Test: Evaluate equality of variances.
  • Mann-Whitney U Test: Compare two independent groups.
  • Kruskal-Wallis Test: Compare three or more independent groups.
  • Wilcoxon Signed-Rank Test: Compare two related samples.
  • Friedman Test: Compare three or more related samples.
  • Spearman's Rank Correlation: Calculate non-parametric correlation.
• Real-World Applications: See how non-parametric methods are used in diverse fields:
  • Analyzing ranked data (e.g., movie ratings).
  • Comparing surgical techniques.
  • Evaluating changes in business metrics (e.g., store revenue).
• Advantages of Non-Parametric Methods: Understand the benefits of using non-parametric statistics, including:
  • Fewer assumptions, leading to broader applicability.
  • Increased robustness in the face of data violations.
  • Simplicity in certain scenarios.
• Addressing Parametric Assumptions: Learn to identify and handle situations where parametric assumptions are not met.
• Reporting Results: Master the art of presenting statistical results clearly and effectively, both numerically and visually.

Why Choose This Course?

• Hands-on Learning: Get practical experience with R and Python from day one.
• Complete Code: Access all source code from the start, including code templates for your own analyses.
• High-Quality Video Lectures: Benefit from hours of clear, concise video explanations.
• Supportive Community: Join a private group to ask questions and collaborate with fellow students.
• Supplementary Materials: Practice with exercises, tasks, and additional resources.

Who is this course for?

• Students seeking to expand their statistics knowledge.
• Engineers looking to enhance their data analysis skills.
• Aspiring data scientists and machine learning professionals.
• Anyone wanting to gain a deeper understanding of data analysis when data isn't "perfect."

Ready to become a top-tier data analyst? Enroll now and let's get started!