Statistics for Data Science and Business Analysis
4.4 /5- Offered byUDEMY
Statistics for Data Science and Business Analysis at UDEMY Overview
Duration | 5 hours |
Total fee | ₹599 |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Go to Website  |
Credential | Certificate |
Statistics for Data Science and Business Analysis at UDEMY Highlights
- Earn a Cerificate on successful completion
- Get Full Lifetime Access
- Compatible on Mobile and TV
Statistics for Data Science and Business Analysis at UDEMY Course details
- People who want a career in Data Science
- People who want a career in Business Intelligence
- Business analysts
- Business executives
- Individuals who are passionate about numbers and quant analysis
- Anyone who wants to learn the subtleties of Statistics and how it is used in the business world
- People who want to start learning statistics
- People who want to learn the fundamentals of statistics
- Understand the fundamentals of statistics
- Learn how to work with different types of data
- How to plot different types of data
- Calculate the measures of central tendency, asymmetry, and variability
- Calculate correlation and covariance
- Distinguish and work with different types of distributions
- Estimate confidence intervals
- Perform hypothesis testing
- Make data driven decisions
- Understand the mechanics of regression analysis
- Carry out regression analysis
- Use and understand dummy variables
- Understand the concepts needed for data science even with Python and R!
Statistics for Data Science and Business Analysis is a comprehensive course designed to provide participants with a solid foundation in statistical concepts and techniques essential for data-driven decision-making in both data science and business analysis contexts.
The course will then cover inferential statistics, where participants will learn how to draw conclusions and make predictions about a population based on sample data.
Throughout the course, practical examples, case studies, and hands-on exercises will reinforce learning and enable participants to apply statistical concepts to solve business problems and optimize decision-making processes.
Statistics for Data Science and Business Analysis at UDEMY Curriculum
Introduction
What does the course cover?
Download all resourcesSample or population data?
Understanding the difference between a population and a sample
The fundamentals of descriptive statistics
The various types of data we can work with
Levels of measurement
Categorical variables. Visualization techniques for categorical variables
Categorical variables. Visualization techniques. Exercise
Numerical variables. Using a frequency distribution table
Numerical variables. Using a frequency distribution table. Exercise
Histogram charts
Histogram charts. Exercise
Cross tables and scatter plots
Cross tables and scatter plots. Exercise
Measures of central tendency, asymmetry, and variability
The main measures of central tendency: mean, median and mode
Mean, median and mode. Exercise
Measuring skewness
Skewness. Exercise
Measuring how data is spread out: calculating variance
Variance. Exercise
Standard deviation and coefficient of variation
Standard deviation and coefficient of variation. Exercise
Calculating and understanding covariance
Covariance. Exercise
The correlation coefficient
Correlation coefficient
Practical example: descriptive statistics
Practical example
Practical example: descriptive statistics
Distributions
Introduction to inferential statistics
What is a distribution?
The Normal distribution
The standard normal distribution
Standard Normal Distribution. Exercise
Understanding the central limit theorem
Standard error
Estimators and estimates
Working with estimators and estimates
Confidence intervals - an invaluable tool for decision making
Calculating confidence intervals within a population with a known variance
Confidence intervals. Population variance known. Exercise
Confidence interval clarifications
Student's T distribution
Calculating confidence intervals within a population with an unknown variance
Population variance unknown. T-score. Exercise
What is a margin of error and why is it important in Statistics?
Confidence intervals: advanced topics
Calculating confidence intervals for two means with dependent samples
Confidence intervals. Two means. Dependent samples. Exercise
Calculating confidence intervals for two means with independent samples (part 1)
Confidence intervals. Two means. Independent samples (Part 1). Exercise
Calculating confidence intervals for two means with independent samples (part 2)
Confidence intervals. Two means. Independent samples (Part 2). Exercise
Calculating confidence intervals for two means with independent samples (part 3)
Practical example: inferential statistics
Practical example: inferential statistics
Practical example: inferential statistics
Hypothesis testing: Introduction
The null and the alternative hypothesis
Further reading on null and alternative hypotheses
Establishing a rejection region and a significance level
Type I error vs Type II error
Hypothesis testing: Let's start testing!
Test for the mean. Population variance known
Test for the mean. Population variance known. Exercise
What is the p-value and why is it one of the most useful tools for statisticians
Test for the mean. Population variance unknown
Test for the mean. Population variance unknown. Exercise
Test for the mean. Dependent samples
Test for the mean. Dependent samples. Exercise
Test for the mean. Independent samples (Part 1)
Test for the mean. Independent samples (Part 1)
Test for the mean. Independent samples (Part 2)
Test for the mean. Independent samples (Part 2). Exercise
Practical example: hypothesis testing
Practical example: hypothesis testing
Practical example: hypothesis testing
The fundamentals of regression analysis
Introduction to regression analysis
Correlation and causation
The linear regression model made easy
What is the difference between correlation and regression?
A geometrical representation of the linear regression model
A practical example - Reinforced learning
Subtleties of regression analysis
Decomposing the linear regression model - understanding its nuts and bolts
What is R-squared and how does it help us?
The ordinary least squares setting and its practical applications
Studying regression tables
Regression tables. Exercise
The multiple linear regression model
The adjusted R-squared
What does the F-statistic show us and why do we need to understand it?
Assumptions for linear regression analysis
OLS assumptions
A1. Linearity
A2. No endogeneity
A3. Normality and homoscedasticity
A4. No autocorrelation
A5. No multicollinearity
Dealing with categorical data
Dummy variables
Practical example: regression analysis
Practical example: regression analysis
Bonus lecture
Bonus lecture: Next steps
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