‘Learn MySQL and Statistics for Data Science and Business Analytics’ is a comprehensive course that includes more than 300 lectures and real-world projects and examples. This course is designed for individuals who want to learn how to use SQL and MySQL for data science and statistics.

The course material is developed by highly qualified engineers who have worked for major corporations like Microsoft, Facebook, and Google. The instructor of this course is a senior developer and chief data scientist, who has worked on numerous projects involving artificial intelligence and expert systems. This course is perfect for those interested in a career in data science using MySQL, machine learning, marketing analysis, business analysis, or business intelligence. The course is usually available for INR 2,899 on Udemy but you can click now to get 85% off and get Learn MySQL and Statistics for Data Science and Business Analytics Course for INR 449.

Who all can opt for this course?

  • Those who are interested in learning SQL and programming for data science
  • Programmers just starting out
  • Novice data scientists
  • Beginners in MySQL databases for data science
  • Anyone needs to learn statistics from the scratch

Course Highlights

Key HighlightsDetails
Registration LinkApply Now!
PriceINR 449 (INR 2,89985% off
Duration14.5 hours
Student Enrollment7,156 students
InstructorMahmoud Ali https://www.linkedin.com/in/mahmoudali
Topics CoveredSQL, MySQL, Introduction to Data Science & Data Analytics, Aggregate Functions, etc.
Course LevelBeginner
Total Student Reviews543

Learning Outcomes

  • Using MySQL and SQL for data science
  • Write sophisticated SQL queries that span several tables
  • Databases that are relational versus others that are not
  • Learn SQL programming
  • Using realistic simulation programs to sample distribution and provide technical answers
  • Level of confidence and confidence interval
  • Recognize and use the various distribution types
  • Descriptive and inferential statistics with a selection of key tests and examples
  • The mean t-test on one sample
  • T-test using two sample means
  • How can I determine the P value manually and directly?
  • Alternative hypothesis and null hypothesis Recognize what the P value is
  • Data kinds and Why Should We Study Them
  • What causes a Type 1 error?
  • Alpha and Type One Error Relationship
  • Are the normal and t distributions related to one another?
  • What does “double-edged statistics” mean?
  • Statistical significance versus practical significance, and how to learn it more and more

Course Content

S.No.Module (Duration)Topics
1.Course orientation (11 minutes)Download the Course Syllabus and contents
What will you get from this course?
Bookmark and Access course slides, codes, apps and projects
How to learn and get most of the course
Download Cornell notes and learn how to use it.
Which part of the course matched to me?
2.An Introduction to SQL – MySQL – Data Science (01 hour 23 minutes)Available Jobs for data analysis or data science.
Comparison between SQL, Python, and R language
Understand Why MySQL for Data analysis?
Learning programming for data analysis is something easy or complex to learn it
A high-level overview of course projects
The SQL language is the romance language of data.
Relational databases versus non-relational databases
Understand what is DBMS.
What is after data analysis?
MySQL advantages and disadvantages
MySQL installation on a windows machine
install MySQL on a MacOSx machine
3.Your first MySQL Activity (09 minutes)Learn how to Start, and stop MySQL server using Command Prompt
Learn how to Import external databases to MySQL server
4.App 2 ( Pele versus Maradona ) Who was better? (20 minutes)Learn how to create a database and tables, columns, and insert data
Use MySQL query to know which scored goals per game over 50%
5.App 3 ( MySQL data types ) (11 minutes)Understand data types and the difference between Float and Decimal
6.App 4 ( Select Statement in MySQL ) (08 minutes)Understand how select statement works and the order to retrieve data
7.App 5 ( MySQL alter table ) (15 minutes)Understand how to add, delete, rename and modify MySQL tables
8.App 6 ( primary and foreign key ) (27 minutes)The idea behind foreign and primary keys in MySQL database
Learn how to create relational tables using primary and foreign key
9.App 7 ( MySQL where clause ) (03 minutes)Understand MySQL where clause
10.App 8 ( ORDER BY clause ) (04 minutes)Sorting table columns Ascending Order or Descending order
11.App 9 (MySQL Logical operators AND, OR) (11 minutes)Understand logical operators in MySQL like AND, OR, EQUAL, NOT EQUAL
12.App 10 ( IN operator ) (06 minutes)Learn how to use a range of values using MySQL IN operator
13.App 11 (15 minutes)Understand the Date variable and CAST from number to date
14.App 12 ( BETWEEN operator ) (04 minutes)Find payments whose amount is between two values using the BETWEEN operator
15.App 13 ( Limit clause ) (09 minutes)Using LIMIT clause syntax with two arguments
16.App 14 ( Joins ) (11 minutes)Use JOIN clause to find ONLY common fruits types
17.App 15 ( joins ) (16 minutes)Continue to use JOIN clause to find common fruits types
18.MySQL aggregate functions (16 minutes)Understand what is the benefit of Aggregate Functions in data analysis.
19.Project 1 (09 minutes)What do you need before you start this project?
MySQL Business Case of DVD rental store
20.Project 2 (21 minutes)Part1: Investigate MySQL DB to help students in debt crisis
Part 2: Investigate MySQL DB to help students in debt crisis
21.Project 3 (15 minutes)Which State in the USA Has The Worst Drivers?
22.Bonus 1 : Introduction to NOSQL ( MongoDB ) (30 minutes)Understand Why Mongo DB is in data analytics?
Understand What is the best language used for MongoDB?
Advantages and disadvantages of MongoDB
Install the MongoDB server on a windows machine
Install MongoDB on MacOSX
Learn to write your first MongoDB query using JavaScript
23.Project 4: data analysis project without programming (08 minutes)Which state in the USA Has The Worst Drivers?
24.Project 5: data analysis project without programming (14 minutes)Statistical Analysis of the Work of Bob Ross
25.Refresher: Data analysis introduction (05 minutes)Refresher part
Refresher: Is programming for data science easy or hard?
Is programming for data science easy or hard?
Refresher: Collection of important questions related to data science
Refresher: Be patient for interview questions
Be patient for interview questions
Refresher: People are panicking about robot jobs
26.Refresher: Data Analytics – Careers and robot jobs (01 minute)Refresher part
Refresher: High demand for hiring data analysis engineer
Robot jobs
Data scientist is the sexiest job
Robot jobs
hire data analysis or data science engineer
Learning programming
27.Refresher: Statistics for data analysis – Example about programming and big data (08 minutes)Refresher part
Refresher: Run your first SQL command without any previous experience
Refresher: Note the difference between SQL and English language
Refresher: What is big data?
Refresher: Professional answer about what is big data.
Refresher: What are OVERLOADS in big data?
Understand 3’Vs ( Volume, variety, velocity ) in Big data
28.Refresher : What is after data analysis ? (04 minutes)Refresher: What is after data analysis?
Refresher: Your data is your treasure
29.Refresher : Start Descriptive statistics (02 minutes)Refresher: Our strategy to learn practical statistics
Refresher: Four main things in practical statistics
30.Refresher : Comparison between inferential and descriptive statistics (09 minutes)Simplified viewpoint about descriptive and inferential statistics
Refresher: Data before and after descriptive statistics
Refresher: Conclusions between inferential and descriptive statistics
Refresher: Population and sample in inferential statistics
31.Refresher : FAQ about descriptive statistics (04 minutes)Refresher: What will we learn in descriptive statistics?
Refresher: Statistics between Lie and trustworthy
Refresher: Waitress should be friendly or friendlier?
32.Refresher: Data types (21 minutes)Refresher: Introduction about data types
Refresher: the benefit of data types
Refresher: Categorical data types
Refresher: Data types ( continuous vs discrete )
Refresher: Difference between numerical and categorical data
Refresher: Quizzes and examples about data types
Refresher: The summary about data types
33.Refresher: Center of numerical data (14 minutes)Refresher: introduction about data center
Refresher: Characteristics of numerical data
Refresher: Categorical data characteristics considered to be limited
Refresher: Example about characteristics of categorical data
Refresher: What are measures of center ?
Refresher: Examples of mean
Refresher: Examples of median
Refresher: Examples of mode
I’m confused between mean , median and mode
34.Refresher: Why center of the data is very important ? (05 minutes)Refresher: introduction about a lot of ways to find center of the data
Refresher: Mode , Median and mean in Financial application , our life Society
Refresher: Final review about central tendency
35.Refresher: Data dispersion and spread (17 minutes)Refresher: basics of data dispersion and spread
Refresher: Measures of spread
Refresher: What is range ?
Refresher: What is interquartile range ?
Refresher: What is 5 number summary ?
Refresher: Example about 5 number summary
Refresher: 5 number summary with range and interquartile range
Refresher: Remember why we need 5 number summary ?
Refresher: Box plot with 5 number summary
36.Refresher: Which one is better ? Standard deviation or range ? (29 minutes)Refresher: Concepts about standard deviation , range and 5 number summary
Refresher: The idea about how to represent your data ?
Refresher: Example about using graphs with 5 number summary
Refresher: Benefits of using box plot with histogram
Refresher: What is standard deviation ?
Refresher: Example about standard deviation
Refresher: Direct methods to calculate standard deviation
Refresher: Physical meaning of standard deviation
Refresher: a tricky question about standard deviation
Refresher: Example about center of data and standard deviation
Refresher: Standard deviation with financial analysis
Refresher: Choose between standard deviation and range to measure data spread
37.Refresher: Data shape (17 minutes)Refresher: Introduction about data shape
Refresher: Fast review about what we learned about data aspects
Refresher: Important questions related to data shape
Refresher: Symmetric versus skewed distribution
Refresher: Example about symmetric distribution
Refresher: What is Gaussian and bell curve distribution ?
Refresher: Why normal distribution ?
Refresher: Normal versus standard normal distribution
Refresher: Left and right skewed distribution
Refresher: Remember what we studied about data shape
38.Refresher: Outlier (19 minutes)Refresher: Introduction about outlier
Refresher: Fast review about what we learned
Refresher: What do we mean by outlier ?
Refresher: What is our rule of thump to find outlier ?
Refresher: Examples to find outlier
Refresher: What can i do with outlier ?
Refresher: What can i do if i removed outlier ?
Refresher: What professional people do with outlier ?
39.Refresher: Normal distribution lesson 1 (21 minutes)Refresher: Introduction about normal distribution
Refresher: Four main things in data analysis
Refresher: Simplified viewpoint about normal distribution
Refresher: Why normal distribution called Gaussian or bell curve distribution ?
Refresher: Why normal distribution is symmetric distribution ?
Refresher: Difference between normal distribution and standard normal
Refresher: What is the benefit of Z table ?
Refresher: There is an issue if you do not have standard normal distribution
40.Refresher: Normal distribution lesson 2 (17 minutes)Refresher: Why we need to convert normal to standard normal distribution ?
Refresher: Examples about convert normal to standard normal
Refresher: FAQ related to normal distribution
41.Start Inferential statistics ( Sampling distribution ) (12 minutes)Introduction about sampling distribution
The difference between population parameters and sample statistics
Why we need sample statistics ?
Find the mean length of all fishes in the sea
Solution one : Mr. Genie will help you find the length of all fishes
Why we need Mr. Genie ?
What is our final distribution we get it from Mr. Genie ?
Mr. Genie has power to offer TRUE population parameters ( not estimation )
Solution two : sampling distribution instead of Mr. Genie
The idea of sampling distribution
42.Continue Sampling distribution (20 minutes)Introduction about what will we learn
Overview about sampling distribution simulation tool parts
What is our final goal from this simulation tool ?
How to take a sample for all fishes in the sea ?
Let’s start working with simulation tool
Comparison between population parameters and sampling distribution
The idea of central limit theorem
Summary about population parameters and sampling distribution estimators
Example : Help fisher man to catch Tuna fishes with length over 1 meter
How to find sampling mean in our example ?
43.Confidence interval and level first lesson (29 minutes)Introduction about the importance of confidence interval
What do we mean by “confidence” ?
Collection of important questions related to Confidence interval
Simplified viewpoint about confidence interval and confidence level
Difference between 99 and 95 confidence level
Important definitions about confidence interval in simulation tool
What is sample error ?
Start simulation of 95 confidence interval
What is success rate ?
What is failures ?
What is the difference between confidence level and success rate ?
What happens if we changed confidence level from 95 to 99 ?
Important tips and questions in this lesson
44.Confidence interval second lesson (35 minutes)Introduction about using confidence interval in real life scenarios
Make a decision based on High width or low width confidence interval
Trade-off between useless information and high risk in confidence interval
What effects the width of confidence interval ?
Effect of standard deviation on confidence interval
Relationship between standard error and margin of error in confidence interval
Quick Review about what we learned in confidence interval
What do we mean by we are “lucky” or “not lucky” ?
Review about our answers in confidence interval
45.Student’s t distribution (24 minutes)Introduction
Collection of important questions about t distribution
Why did we call t distribution by “student” ?
What did Mr. William tell us about his findings ?
Comparison between t distribution and normal distribution
degree of freedom with t distribution
What do we mean by degree of freedom ?
The benefit of degree of freedom with t distribution
What do you think if DF > 30 ?
t score and t table
Do not use t distribution for higher values of degree of freedom
Calculate t value directly using t calculator
Remember when to use t or z distribution ?
Summary about confidence interval with t and z distribution
46.Examples about confidence interval (26 minutes)Example 1 : Margin of error with confidence interval 99%
Repeat the same example with CL = 95%
What is the lower limit point of CL = 90% ?
Repeat the same example with sample standard deviation
Final results with t calculator
Premier league football scorers with confidence interval
Final results with t calculator
Two important equations for Z and T distributions
47.Use Excel to calculate confidence interval (06 minutes)Introduction about using Excel to find confidence interval
Step 1 : Make sure that Data analysis tool pack is installed in your Excel
Get all your results about confidence interval from one click only
Comparison between manual method results and excel sheet results
The end of Confidence interval and what is next ?
48.Inferential Statistics : TAKE YOUR BREATH BEFORE HYPOTHESIS TESTING (35 minutes)Introduction about hypothesis testing
Understand H0 and H1
Principles in hypothesis testing like H0 , H1 and P value
P value is the most important thing we should focus on it
Mini story part 1 to understand what is P value ?
Complaint against Cola factory owner
analysis with H0 and H1 about Cola drink to see if the complaint is a fake
Understand how to find H0 and H1 from Cola drinks
on what basis Ibrahim will be innocent or guilty ?
What happened to Ibrahim in the court ?
Ibrahim asked his sister Sarah to help him
What is Sarah’s idea to save her brother Ibrahim ?
First results from Sarah about water percentage in Excel sheet
First trial from Sarah is not valid because we need strong evidence
Another trial from Sarah using P value to get evidence
Alpha and type one error
Comparing between Alpha and P value
Why Sarah is happy if P value greater than Alpha ?
P value with weak and strong evidence .
After Sarah is happy , remember what is P value ?
Sarah asked her brother about profit share from Cola sales
Questions about what we learned from mini story part 1 ?
49.Calculate P value manual method (15 minutes)Introduction and steps to calculate P value
Step 1 : Find H0 and H1
Step 2 : Find Sample Size , SD and mean of sample
Fast review about how to get sample mean and SD from Excel file
Step 3: Find Degree of freedom
Step 4 : Choose between t or z distribution
Step 5 : Calculate t or Z value
Step 6 : find P value from t calculator or t table
Step 7 : Make a decision
50.Use Excel to calculate P value (10 minutes)Prepare your excel sheet with sample results
Write your P value equation in Excel file
Make a decision by comparing Alpha and P value
Question about one and two tail
51.Mini story part 2 ( Two tailed t test ) (31 minutes)For the second time Ibrahim asked his sister Sarah to help him
One tail versus two tail test
The summary about one tail and two tail test
Starting Mini story part 2
Ibrahim i seeking about increase Cola sales in winter
Sarah was surprised when Ibrahim asked her to increase sales in winter
Sarah told her brother about offer free coupons for Cola drinks
Sarah is using two samples mean t test to prove idea of free cola coupons
Important things we should understand it when calculate P value in Excel
What is null hypothesis for two samples mean t test ?
What is alternative hypothesis ?
Use Excel file with two samples mean t test
P value from t calculator versus P value from Excel
Ibrahim does not understand the final results from the Excel file
Examples of what we learned in hypothesis testing.
Simple practice about t distribution table with a different confidence level
t value for one and two tail 95% and 90% confidence interval
52.Understanding two tail test results in Excel (08 minutes)Explain all results from the Excel file about the two samples’ mean t-test
The important graph to tell us about our evidence strength
Ibrahim is happy now after his sister explained the excel file results
Ibrahim gives Sarah some money to avoid disputes with her
53.Practical Significance and Statistical Significance (04 minutes)Choose between practical significance or statistical significance.
Statistics is a liar or trustworthy?
54.Bonus 2 (Machine Learning Introduction) (16 minutes)Gentle introduction to Machine Learning
Create your first Machine Learning algorithm
Understand Machine Learning testing and training data set
Understand why we need to test data.
Understand machine learning predictions about the future

Resources Required

No prior experience is necessary

Featured Review

Michael Nazarkovsky (5/5): The instructor ran over delicate and very important issues for null hypothesis testing for two-tail p-value. As a result, the subject is not elucidated well. However, I want to put 5-stars because Mahmoud answered all my questions after the course and did his best, as a tutor. He is a true professional. In general, the course is very ok for the beginners, however, a new stuff I learned too. It is advisable to upgrade the course with such important topics and techniques like normality tests (D’Agostino-Pearson, Kolmogorov, Shapiro-Wilk etc), ANOVA, homoscedasticity (Levene’s test) etc. Thanks, Mahmud!


  • Hemesh Ashok Sawakar (5/5): It was excellent course and I appreciate the efforts of tutor Mr Mehmud for this well structured course.
  • Elon Obam (5/5): Mysql for data analysis projects and the analysis of Bob ros work using MySQL is excellent work
  • Eddie Burns (5/5): course material and course questions from all data science community are awesome , like the questions about statistics for data science
  • Kristina Barker (5/5): excellent example about how to deal with inferential statistics data in statistics for data science


  • Vonn Napoleon J. (3/5): I left with more questions and answers. I felt at times videos were split up for no reason. Things were explained quickly and the story for hypothesis testing was weird.
  • Francis I. (2.5/5): confused .. thought this course was going to start from the scratch. i had to google null and alternative hypothesis because it wasn’t explained
  • Prabhjinder S. (2/5): Content is not very structured unfortunately 🙁
  • Islam Izatullayev (1/5): From the explanation I have expected other thing rather than I have seen in course.

About the Author

The instructor of this course is Mahmoud Ali who is a System Analyst, Consultant, and AI Engineer. With a 4.4 instructor rating and 543 reviews on Udemy, he offers 1 course and has taught 7,156 students so far.

  • Ali is an artificial intelligence professional with experience in the mobility, medical, retail, and automotive industries.
  • He received education and training from experienced academics in Munich, Germany, after which he made the decision to share what he had learned by developing courses for Udemy.
  • Students can see from his courses how he gives skilled step-by-step tutoring in the field of data science by fusing his real-world expertise and academic background in physics and mathematics.
  • His emphasis on intuitive explanations is one of his teaching strengths, so students can be confident that they will fully comprehend even the most challenging subjects.

Comparison Table

ParametersMySQL – Statistics for Data Science & Business AnalyticsData Analysis & Statistics: Practical Course for BeginnersSVM for Beginners: Support Vector Machines in R Studio
OffersINR 455 (INR 2,899) 85% offINR 455 (INR 3,499) 87% offINR 455 (INR 3,499) 87% off
Duration14.5 hours7.5 hours5 hours
Rating4.4 /54.2 /54.5 /5
Student Enrollments7,15660,66656,196
InstructorsMahmoud AliJacek KulakStart-Tech Academy
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