‘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 Highlights | Details |
---|---|

Registration Link | Apply Now! |

Price | INR 449 (INR 2,899) 85% off |

Duration | 14.5 hours |

Rating | 4.4/5 |

Student Enrollment | 7,156 students |

Instructor | Mahmoud Ali https://www.linkedin.com/in/mahmoudali |

Topics Covered | SQL, MySQL, Introduction to Data Science & Data Analytics, Aggregate Functions, etc. |

Course Level | Beginner |

Total Student Reviews | 543 |

## 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!

## Pros

**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

## Cons

**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

Parameters | MySQL – Statistics for Data Science & Business Analytics | Data Analysis & Statistics: Practical Course for Beginners | SVM for Beginners: Support Vector Machines in R Studio |
---|---|---|---|

Offers | INR 455 (85% off | INR 455 (87% off | INR 455 (87% off |

Duration | 14.5 hours | 7.5 hours | 5 hours |

Rating | 4.4 /5 | 4.2 /5 | 4.5 /5 |

Student Enrollments | 7,156 | 60,666 | 56,196 |

Instructors | Mahmoud Ali | Jacek Kulak | Start-Tech Academy |

Register Here | Apply Now! | Apply Now! | Apply Now! |

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