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Online
₹ 499 3,199
Quick facts
particular | details | |
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Medium of instructions
English
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Mode of learning
Self study
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Mode of Delivery
Video and Text Based
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Course overview
The Statistics for Data Science and Business Analysis online course is a 5 hours on-demand video course offered by Udemy. The course includes 28 articles that cover a wide range of topics related to business analytics and data science. The course helps students to understand the fundamentals of statistics.
The Statistics for Data Science and Business Analysis training covers different types of data and provides a basic grasp to the candidates for working with various types of data. The training teaches to plot various types of data and calculate the measure of asymmetry, tendency, variability, covariance, and correlation.
The Statistics for Data Science and Business Analysis syllabus includes a brief introduction of different types of distribution, their differences, and work. The course teaches the basic concept of python, R, and data science as well as making students capable of making data-driven decisions. The course provides dummy variables to understand and carry out regression analysis.
The highlights
- Course provider Udemy
- Certificate of completion
- Access on mobile and TV
- 5 hours on-demand video
- 98 downloadable resources
- Full lifetime access
- 28 articles
Program offerings
- Lectures
- Downloadable resources
- Online learning
- Certification
- Articles
Course and certificate fees
Fees information
Statistics for Data Science and Business Analysis Fee
Course | Fee in INR |
Statistics for Data Science and Business Analysis | Rs. 3199 |
certificate availability
certificate providing authority
Eligibility criteria
Certification Qualifying Details
After completing the course content and successfully finishing the syllabus, candidates will get Statistics for Data Science and Business Analysis certification.
What you will learn
After completing the Statistics for Data Science and Business Analysis online training, students will learn about the fundamentals and practical examples of descriptive statistics along with advanced topics such as confidence intervals and practical examples of inferential statistics. Candidates will also learn about measures of asymmetry, tendency, variability, and various types of distributors.
Who it is for
- People who want a career in data science and business intelligence.
- Business analysts, business executives, and individuals who are passionate about quant analysis and numbers.
- Anyone who wants to learn about statistics, subtleties and fundamentals of statistics.
Admission details
To get admission to the Statistics for Data Science and Business Analysis online certification course from Udemy, follow the steps mentioned below:
Step 1. Open the official Udemy course page by following the link below.
(https://www.udemy.com/course/statistics-for-data-science-and-business-analysis/)
Step 2. Click on the ‘Buy Now’ button to further proceed.
Step 3. Signup by filling in personal details.
Step 4. Pay the fee amount and start learning.
The syllabus
Introduction
Sample or population data?
- Population vs sample
- Understanding the difference between a population and a sample
The fundamentals of descriptive statistics
- The various types of data we can work with
- Types of data
- Levels of measurement
- Levels of measurement
- Categorical variables. Visualization techniques for categorical variables
- Categorical variables. Visualization Techniques
- Categorical variables. Visualization techniques. Exercise
- Numerical variables. Using a frequency distribution table
- Numerical variables. Using a frequency distribution table
- Numerical variables. Using a frequency distribution table. Exercise
- Histogram charts
- Histogram charts
- Histogram charts. Exercise
- Cross tables and scatter plots
- 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
- Skewness. Exercise
- Measuring how data is spread out: calculating variance
- Variance. Exercise
- Standard deviation and coefficient of variation
- Standard deviation
- Standard deviation and coefficient of variation. Exercise
- Calculating and understanding covariance
- Covariance. Exercise
- The correlation coefficient
- Correlation
- 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
- The standard normal distribution
- Standard Normal Distribution. Exercise
- Understanding the central limit theorem
- The central limit theorem
- Standard error
- Standard error
Estimators and estimates
- Working with estimators and estimates
- Estimators and estimates
- Confidence intervals - an invaluable tool for decision making
- Confidence intervals
- 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?
- Margin of error
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
Hypothesis testing: Introduction
- The null and the alternative hypothesis
- Further reading on null and alternative hypotheses
- Null vs alternative
- Establishing a rejection region and a significance level
- Rejection region and significance level
- Type I error vs Type II error
- 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
- p-value
- 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)
- Test for the mean. Independent samples (Part 2). Exercise
Practical example: hypothesis testing
- Practical example: hypothesis testing
The fundamentals of regression analysis
- Introduction to regression analysis
- Introduction
- Correlation and causation
- The linear regression model made easy
- The linear regression model
- What is the difference between correlation and regression?
- Correlation vs regression
- A geometrical representation of the linear regression model
- 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
- Decomposition
- R-squared
- The ordinary least squares setting and its practical applications
- The ordinary least squares setting and its practical applications
- Studying regression tables
- Studying regression tables
- Regression tables. Exercise
- The multiple linear regression model
- The multiple linear regression model
- The adjusted R-squared
- 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
- OLS assumptions
- A1. Linearity
- A1. Linearity
- A2. No endogeneity
- A2. No endogeneity
- A3. Normality and homoscedasticity
- A3. Normality and homoscedasticity
- A4. No autocorrelation
- A4. No autocorrelation
- A5. No multicollinearity
- A5. No multicollinearity
Dealing with categorical data
- Dummy variables
Practical example: regression analysis
- Practical example: regression analysis
Bonus lecture
- Bonus lecture: Next steps
How it helps
Candidates pursuing Statistics for Data Science and Business Analysis course will be benefited in the following ways:
- Candidates will learn about the basics of statistics and various types of data.
- Students will get a course completion certificate after completing the course.
- Understand the principles of data science of R and Python.
FAQs
Udemy offers the Statistics for Data Science and Business Analysis course.
The duration of the Statistics for Data Science and Business Analysis program is 5 hours.
There are 98 downloadable resources available in the Statistics for Data Science and Business Analysis training.
The Statistics for Data Science and Business Analysis online training consists of 28 articles.