Statistics for Data Science and Business Analysis (Udemy – Vietsub and Engsub)

About Course

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Course Introduction: “Statistics for Data Science and Business Analysis”
Are you aiming for a career as a
Marketing Analyst, Business Intelligence Analyst, Data Analyst, or Data Scientist
? Do you need strong
quantitative skills
to stand out in the field?
📊
Statistics for Data Science and Business Analysis
is the perfect starting point!
This course covers
Descriptive & Inferential Statistics, Hypothesis Testing, and Regression Analysis
, equipping you with the statistical knowledge needed for real-world applications. Plus, it includes
Excel templates
to make learning even more hands-on.
What makes this course unique?

✔
Easy to understand
– No overwhelming jargon, just clear explanations

✔
Comprehensive & practical
– Learn concepts you can apply immediately

✔
Data-driven & to the point
– Focus on real-world business scenarios

✔
Plenty of exercises & resources
– Reinforce your learning with practical applications

By the end of this course, you’ll confidently analyze data, interpret statistical results, and make data-driven business decisions. 🚀
What you’ll learn:
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!

Link gốc:

https://www.udemy.com/course/statistics-for-data-science-and-business-analysis/

Time Course:
5 hours (92 lectures + Documents)

Instructor
: 365 Careers
Total Weight:
2.59 GB
** Note
:

Chú ý:

Course Content

09 – Practical example inferential statistics

001 Practical example inferential statistics.mp4

10:05

17 – Practical example regression analysis

16 – Dealing with categorical data

15 – Assumptions for linear regression analysis

14 – Subtleties of regression analysis

13 – The fundamentals of regression analysis

12 – Practical example hypothesis testing

11 – Hypothesis testing Let’s start testing!

10 – Hypothesis testing Introduction

01 – Introduction

08 – Confidence intervals advanced topics

07 – Estimators and estimates

06 – Distributions

05 – Practical example descriptive statistics

04 – Measures of central tendency, asymmetry, and variability

03 – The fundamentals of descriptive statistics

02 – Sample or population data

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About Course

Course Content

09 – Practical example inferential statistics

001 Practical example inferential statistics.mp4

17 – Practical example regression analysis

001 Practical example regression analysis.mp4

16 – Dealing with categorical data

001 Dummy variables.mp4

15 – Assumptions for linear regression analysis

001 OLS assumptions.mp4

003 A1. Linearity.mp4

005 A2. No endogeneity.mp4

007 A3. Normality and homoscedasticity.mp4

009 A4. No autocorrelation.mp4

011 A5. No multicollinearity.mp4

14 – Subtleties of regression analysis

001 Decomposing the linear regression model – understanding its nuts and bolts.mp4

003 What is R-squared and how does it help us.mp4

005 The ordinary least squares setting and its practical applications.mp4

007 Studying regression tables.mp4

010 The multiple linear regression model.mp4

012 The adjusted R-squared.mp4

014 What does the F-statistic show us and why do we need to understand it.mp4

13 – The fundamentals of regression analysis

001 Introduction to regression analysis.mp4

003 Correlation and causation.mp4

005 The linear regression model made easy.mp4

007 What is the difference between correlation and regression.mp4

009 A geometrical representation of the linear regression model.mp4

011 A practical example – Reinforced learning.mp4

12 – Practical example hypothesis testing

001 Practical example hypothesis testing.mp4

11 – Hypothesis testing Let’s start testing!

001 Test for the mean. Population variance known.mp4

003 What is the p-value and why is it one of the most useful tools for statisticians.mp4

005 Test for the mean. Population variance unknown.mp4

007 Test for the mean. Dependent samples.mp4

009 Test for the mean. Independent samples (Part 1).mp4

011 Test for the mean. Independent samples (Part 2).mp4

10 – Hypothesis testing Introduction

001 The null and the alternative hypothesis.mp4

004 Establishing a rejection region and a significance level.mp4

006 Type I error vs Type II error.mp4

01 – Introduction

001 What does the course cover.mp4

08 – Confidence intervals advanced topics

001 Calculating confidence intervals for two means with dependent samples.mp4

003 Calculating confidence intervals for two means with independent samples (part 1).mp4

005 Calculating confidence intervals for two means with independent samples (part 2).mp4

007 Calculating confidence intervals for two means with independent samples (part 3).mp4

07 – Estimators and estimates

001 Working with estimators and estimates.mp4

003 Confidence intervals – an invaluable tool for decision making.mp4

005 Calculating confidence intervals within a population with a known variance.mp4

007 Confidence interval clarifications.mp4

008 Student’s T distribution.mp4

010 Calculating confidence intervals within a population with an unknown variance.mp4

012 What is a margin of error and why is it important in Statistics.mp4

06 – Distributions

001 Introduction to inferential statistics.mp4

002 What is a distribution.mp4

004 The Normal distribution.mp4

006 The standard normal distribution.mp4

009 Understanding the central limit theorem.mp4

011 Standard error.mp4

05 – Practical example descriptive statistics

001 Practical example.mp4

04 – Measures of central tendency, asymmetry, and variability

001 The main measures of central tendency mean, median and mode.mp4

003 Measuring skewness.mp4

006 Measuring how data is spread out calculating variance.mp4

008 Standard deviation and coefficient of variation.mp4

011 Calculating and understanding covariance.mp4

013 The correlation coefficient.mp4

03 – The fundamentals of descriptive statistics

001 The various types of data we can work with.mp4

003 Levels of measurement.mp4

005 Categorical variables. Visualization techniques for categorical variables.mp4

008 Numerical variables. Using a frequency distribution table.mp4

011 Histogram charts.mp4