[100% Off] Statistics And Hypothesis Testing For Data Science
“Mastering Data Analysis and Making Informed Decisions with Statistical Hypothesis Testing in Data Science”.
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.
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:
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Delve into the world of data-driven insights and discover how statistics plays a pivotal role in shaping our understanding of information.
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Equip yourself with the essential Python skills required for effective data manipulation and visualization.
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Learn to categorize data, setting the stage for meaningful analysis.
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Discover how to summarize data with measures like mean, median, and mode.
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Explore the variability in data using concepts like range, variance, and standard deviation.
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Understand relationships between variables with correlation and covariance.
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Grasp the shape and distribution of data using techniques like quartiles and percentiles.
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Learn to standardize data and calculate z-scores.
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Dive into probability theory and its practical applications.
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Lay the foundation for probability calculations with set theory.
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Explore the probability of events under certain conditions.
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Uncover the power of Bayesian probability in real-world scenarios.
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Solve complex counting problems with ease.
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Understand the concept of random variables and their role in probability.
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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!
Author(s): Meritshot Academy
