Introduction to statisrics and probability
WHAT YOU WILL LEARN
- Analyze, explain, and interpret data effectively.
- Understand the relationships and dependencies within the data, enabling accurate predictions.
- Gain proficiency in various methods of data analysis, including measures of central tendency (mean, median, mode) and measures of dispersion (variance, standard deviation).
- Develop a basic understanding of probability and Bayes' theorem.
- Acquire knowledge about rates, ratios, odds ratios, and screening tests.
DESCRIPTION
In this comprehensive statistics course:
- Students will acquire fundamental knowledge about statistics.
- Clear understanding of different types of data will be provided through examples, laying a crucial foundation for data analysis.
- The ability to analyze, explain, and interpret data will be developed.
- Pearson's correlation coefficient, scatter diagrams, and linear regression analysis will be covered, enabling students to comprehend relationships, dependencies, and make predictions.
- Various methods of data analysis will be explored, including measures of central tendency (mean, median, mode) and measures of dispersion (variance, standard deviation, coefficient of variation). Calculations for quartiles, skewness, and box plots will be covered.
- Clear insights into data shape will be gained through the study of skewness and box plots, crucial aspects of data analysis.
- A basic understanding of probability and an explanation of Bayes' theorem with simple examples will be provided.
- Students will develop a foundational understanding of discrete probability distributions like Binomial, Poisson, and continuous probability distributions such as normal distribution, supported by detailed examples.
- Exploration of rates, ratios, odds ratios, and screening tests will enhance knowledge.
- A thorough understanding of screening tests and confusion matrices with detailed examples will be provided.
- The course caters to those interested in advancing their careers in data science and machine learning.
Enroll now to build a solid foundation in statistics, essential for data analysis and scientific research.
CONTENT
Introduction: Data and Statistics
- Introduction
- Instructor Overview
- What is Statistics?
- Understanding Data
Summary Measures: Central Tendency
- Mean Calculation
- Median Analysis
- Mode Explanation
Summary Measures: Measures of Dispersion
- Variance and Standard Deviation
- Coefficient of Variation (CV)
Shape of Data: Measures of Skewness
- Quartiles and Quartile Deviation
- Box Plot Interpretation
- Understanding Skewness in Data
- Coefficient of Skewness Calculation
Correlation and Regression Analysis
- Correlation Coefficient
- Scatter Diagram for Correlation
- Regression Analysis Overview
- Regression Analysis Example
Probability and Bayes' Theorem
- Basics of Probability
- Explanation of Bayes' Theorem
Discrete Probability Distribution
- Binomial Distribution
- Poisson Distribution
Continuous Probability Distribution: Normal Distribution
- Normal Distribution Overview
- Examples of Normal Distribution
Rates, Ratios, Odds Ratio (OR)
- Rates, Ratios, Incidence, and Prevalence
- Understanding Odds and Odds Ratio
Screening Test and Confusion Matrix
- Overview of Screening Tests
- Interpretation of Confusion Matrix
- Detailed Analysis of Confusion Matrix
- Example of Screening Test
- Applying Bayes' Theorem to Screening Tests
Enroll now to grasp the fundamentals of data and statistics, setting the foundation for a comprehensive understanding of statistical analysis.
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