Statistics for Business Analytics and Data Science A-Z™

Learn The Core Stats For A Data Science Career. Master Statistical Significance, Confidence Intervals And Much More!

4.43 (12509 reviews)

Udemy

platform

English

language

Data Science

What you will learn

Understand what a Normal Distribution is

Understand standard deviations

Explain the difference between continuous and discrete variables

Understand what a sampling distribution is

Understand the Central Limit Theorem

Apply the Central Limit Theorem in practice

Apply Hypothesis Testing for Means

Apply Hypothesis Testing for Proportions

Use the Z-Score and Z-Tables

Use the t-Score and t-Tables

Understand the difference between a normal distribution and a t-distribution

Understand and apply statistical significance

Create confidence intervals

Understand the potential pitfalls of overusing p-Values

Course Gallery

Statistics for Business Analytics and Data Science A-Z™ – Screenshot 1 — Screenshot 1 – Statistics for Business Analytics and Data Science A-Z™

Statistics for Business Analytics and Data Science A-Z™ – Screenshot 2 — Screenshot 2 – Statistics for Business Analytics and Data Science A-Z™

Statistics for Business Analytics and Data Science A-Z™ – Screenshot 3 — Screenshot 3 – Statistics for Business Analytics and Data Science A-Z™

Statistics for Business Analytics and Data Science A-Z™ – Screenshot 4 — Screenshot 4 – Statistics for Business Analytics and Data Science A-Z™

Loading charts...

Comidoc Review

Our Verdict

Statistics for Business Analytics and Data Science A-Z™ offers a solid foundation in essential statistical methods, catering specifically to those pursuing data science careers. While there are areas for improvement mainly related to explanation clarity and structure, the overall quality of instruction and focus on practical applications make it a worthwhile course to consider. Keep in mind that you might need additional resources to reinforce your learning.

What We Liked

Comprehensive coverage of key statistical concepts relevant to business analytics and data science
Engaging teaching style and clear explanations from an experienced instructor
Strong emphasis on practical applications, helping learners understand the relevance of statistical methods
Live examples and exercises that enhance understanding and reinforce learning
User-friendly course structure, making complex topics easier to grasp

Potential Drawbacks

Occasional unclear explanations requiring supplementary learning materials
Lack of slide preparation and delivery professionalism in some instances
Limited elaboration on certain concepts like hypothesis framing and null hypothesis rejection
Incomplete or missing information necessitating external research to continue with the course