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Statistics and Hypothesis Testing for Data science
37,503 students
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.
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Course content
5 sections • 31 lectures • 4h 15m total length
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.
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!
Who this course is for:
- 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.