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Statistics And Hypothesis Testing For Data Science

"Mastering Data Analysis and Making Informed Decisions with Statistical Hypothesis Testing in Data Science".

Includes

  • course Chapters: 37
  • course Total Hours: 02:57 Hours

Features

  • course Full lifetime access
  • course Access on mobile
  • course Assignments
  • course Certificate of Completion
Overview
Course Description

"Statistics and Hypothesis Testing for Data Science" is a comprehensive course designed to equip learners with the fundamental statistical knowledge and data analysis skills essential for success in the field of data science.

The course begins by emphasizing the crucial role of statistics in deriving data-driven insights, laying the foundation for understanding and interpreting information effectively. Through practical examples and hands-on exercises, participants will develop proficiency in Python, a key tool for data manipulation and visualization in the industry.

Participants will learn to categorize data effectively, facilitating meaningful analysis. Key statistical measures such as mean, median, and mode will be covered, enabling learners to summarize data accurately. Additionally, concepts such as range, variance, and standard deviation will be explored to understand the variability inherent in datasets.

The course delves into understanding relationships between variables through correlation and covariance analysis. Techniques like quartiles and percentiles will be employed to grasp the shape and distribution of data, providing insights into its characteristics.

Participants will also learn to standardize data and calculate z-scores, essential for comparative analysis across different datasets. Probability theory will be introduced, starting with foundational concepts in set theory and progressing to practical applications, including Bayesian probability.

The course will enable learners to solve complex counting problems effortlessly and understand the role of random variables in probability calculations. Various probability distributions, such as the normal distribution and binomial distribution, will be explored, along with their real-world applications in data science scenarios.

Ultimately, the course aims to empower learners with the knowledge and skills necessary to analyze data effectively, make informed decisions, and apply statistical methods confidently in a data science context. Whether participants are beginners or seeking to deepen their statistical expertise, this course serves as a comprehensive gateway to mastering statistics for data science. Enroll now and embark on your journey to becoming a proficient data scientist!

What you'll 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.

Requirements

  • Access to a computer with internet connectivity.
  • A basic understanding of mathematics, including algebra and arithmetic.
  • Familiarity with fundamental concepts in data analysis and problem-solving.
  • A willingness to learn and engage with statistical concepts and Python programming.
  • Basic knowledge of Python is a plus but not mandatory.
Course Content
31 Lessons | 1 Downloadable material | 5 Quiz | 02:57 Hours
About the instructor
4.5 Instructor Rating
course

5 Courses

course

2+ Lesson