MySQL - Statistics for Data Science & Business Analytics

SQL - MySQL for Data Analytics - Beginners - Statistics for Data Science - MySQL for Data Analysis - with 25 projects

4.31 (553 reviews)

Udemy

platform

English

language

Data Science

What you will learn

SQL - MySQL for Data science.

Write complex SQL queries across multiple tables.

Relational databases versus non relational databases.

Learn how to code in SQL

Sampling distribution with practical simulation apps and answering of important technical questions.

Confidence level and Confidence interval.

Distinguish and work with different types of distributions .

Inferential and Descriptive statistics with collection of important quizzes and examples .

One sample mean t test .

Two sample means t test .

How to calculate P value using manual and direct method ?

What is after data analysis ?

Null hypothesis and alternative hypothesis .

Understand What is P value ?

Data types and Why we need to study data types ?

What is Type one error ?

Relationship between Type one error and Alpha ( non confident probability )

Is Normal distribution and t distributions are cousins ?

Projects like Estimation of goals in premier league ( using confidence interval ) , and more .. and more to learn it

What is "double edged sword of statistics" ?

Practical significance versus statistical significance , and more and more to learn it

Course Gallery

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Comidoc Review

Our Verdict

MySQL - Statistics for Data Science & Business Analytics" offers an engaging and comprehensive introduction to MySQL and its role in data analysis and statistics. The course benefits from a knowledgeable and dedicated instructor who is responsive to students' needs while utilizing relatable examples to teach critical concepts. However, potential learners should be aware that the material may be too elementary for some advanced data scientists, and the sheer quantity of short videos might come across as excessive. Nonetheless, this course provides an exceptional resource for those seeking a solid grasp of MySQL in the context of data science, particularly for beginners.

What We Liked

The course provides a solid foundation in MySQL and its application in data science, covering topics such as null hypothesis testing, sampling distribution, and confidence intervals.
Instructor Mahmoud Ali is commended for his professionalism, attentiveness, and clear explanations, especially when addressing complex topics and answering student questions.
Real-world examples and projects, like estimating premier league goals using confidence intervals, help illustrate the practical application of MySQL in data analysis.
The course covers a wide range of important statistical concepts, such as understanding P values, t and z distributions, type one errors, and the differences between population and sample means.

Potential Drawbacks

Some students initially struggled with certain topics like sampling distribution and the difference between sample mean and sampling means.
The course content may be considered too basic by some more advanced data scientists, as noted by a few reviewers.
A few students expressed concern about the seemingly excessive number of short videos, which they found unnecessary.