“Mastering Data Analysis and Making Informed Decisions with Statistical Hypothesis Testing in Data Science”.
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.
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!
English
language
Content
Introduction to Statistics
Introduction to Statistics and its importance
Explain the role of statistics in data analysis
Introduction to Python for Statistical Analysis
Introduction to Descriptive Statistics
Types of Data
Measures of Central Tendency
Measures of Spread
Measures of Dependence
Measures of Shape and Position
Measures of Standard Scores
Introduction to Basic and Conditional Probability
Introduction to Basic Probability
Introduction to Set Theory
Introduction to Conditional Probability
Introduction to Bayes Theorem
Introduction to Permutations and Combinations
Introduction to Random Variables
Introduction to Probability Distribution Functions
Introduction to Inferential Statistics
Introduction to Normal Distribution
Introduction to Skewness and Kurtosis
Introduction to Statistical Transformations
Introduction to Sample and Population Mean
Introduction to Central Limit Theorem
Introduction to Bias and Variance
Introduction to Maximum Likelihood Estimation
Introduction to Confidence Intervals
Introduction to Correlations
Introduction to Sampling Methods
Introduction to Hypothesis Testing
1. Fundamentals of Hypothesis Testing
Introduction to T Tests
Introduction to Z Tests
Introduction to Chi Squared Tests
Introduction to Anova Tests