Statistics and Hypothesis Testing for Data science

Description

Welcome to "Statistics and Hypothesis Testing for Data Science" – a comprehensive Udemy course that will empower you with the essential statistical knowledge and data analysis skills needed for success in the world of data science.

Here's what you'll learn:

• Delve into the world of data-driven insights and discover how statistics plays a pivotal role in shaping our understanding of information.

• Equip yourself with the essential Python skills required for effective data manipulation and visualization.

• Learn to categorize data, setting the stage for meaningful analysis.

• Discover how to summarize data with measures like mean, median, and mode.

• Explore the variability in data using concepts like range, variance, and standard deviation.

• Understand relationships between variables with correlation and covariance.

• Grasp the shape and distribution of data using techniques like quartiles and percentiles.

• Learn to standardize data and calculate z-scores.

• Dive into probability theory and its practical applications.

• Lay the foundation for probability calculations with set theory.

• Explore the probability of events under certain conditions.

• Uncover the power of Bayesian probability in real-world scenarios.

• Solve complex counting problems with ease.

• Understand the concept of random variables and their role in probability.

• Explore various probability distributions and their applications.

  
  

This course will empower you with the knowledge and skills needed to analyze data effectively, make informed decisions, and apply statistical methods in a data science context. Whether you're a beginner or looking to deepen your statistical expertise, this course is your gateway to mastering statistics for data science. Enroll now and start your Journey!

What You Will Learn!

• Fundamental concepts and importance of statistics in various fields.
• How to use statistics for effective data analysis and decision-making.
• Introduction to Python for statistical analysis, including data manipulation and visualization.
• Different types of data and their significance in statistical analysis.
• Measures of central tendency, spread, dependence, shape, and position.
• How to calculate and interpret standard scores and probabilities.
• Key concepts in probability theory, set theory, and conditional probability.
• Understanding Bayes' Theorem and its applications.
• Permutations, combinations, and their role in solving real-world problems.
• Practical knowledge of various statistical tests, including t-tests, chi-squared tests, and ANOVA, for hypothesis testing and inference.

Who Should Attend!

• Students or professionals in various fields, including business, science, social sciences, and healthcare, who want to enhance their data analysis skills.
• Data analysts, researchers, and scientists seeking to strengthen their statistical foundations and Python programming skills.
• Anyone interested in gaining a deeper understanding of statistical concepts and their practical applications.
• Beginners with no prior statistical knowledge but with a curiosity to learn and apply statistical methods.
• Professionals looking to advance their career by acquiring valuable statistical and data analysis skills.
• Individuals preparing for standardized tests or exams that include statistical and data analysis components.

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