Statistics & Applied Data Science Business Data Analysis

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Genre: eLearning | MP4 | Video: h264, 1280x720 | Audio: aac, 44100 Hz
Language: English | VTT | Size: 6.61 GB | Duration: 9 hours

Data analysis FAQ related to interview questions in your career .

What you'll learn

Four main things you should know them in data analysis and business analysis
HYPOTHESIS TESTING
Normal distribution and standard normal in details using Z table .
Sampling distribution with practical simulation apps and answering of important technical questions .
Confidence level and Confidence interval .
What is t distribution ? ( with projects )
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 ?
TWO PROJECTS related to hypothesis testing
Null hypothesis and alternative hypothesis .
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
Ice Cola example with student's t distribution .
Help fisherman to catch Tuna using sampling distribution
What is "double edged sword of statistics" ?
Is programming something mandatory to learn data analysis and business analysis ?
Which programming languages i should start with it ?
Practical significance versus statistical significance , and more to learn .. and more to learn it
Requirements
Definitely no experience is required
I will start from level ZERO and gradually step by step i will make you in advanced level .
All i need from you is to be patient when learning and i'm sure you will love statistics and apply it in your real life
Description
270+ video lectures include real life practical projects and examples for people need to learn statistics for Machine learning and Data Analysis .
Do you like jobs in data analysis and data science ?
Do you like jobs in Machine learning ?
Do you like jobs in Marketing analyst ?
Do you like jobs in Business analysis and business intelligence ?
All of these jobs above need learning STATISTICS .
Who prepared this course material ?
This course material is prepared from highly experienced engineers worked in a leader companies like Microsoft , Facebook and google .
After hard working from five months ago we created +270 Lectures/Articles to cover everything related to practical statistics .
In no time with simple and easy way you will learn and love statistics .
We stress in this course to make it very spontaneous to make all students love statistics .
Who's teaching you in this course ?
My name is Mahmoud , i'm senior consultant and system analyst engineer i worked for many projects related to expert Systems and artificial intelligence .
Also i worked as Tutor and consultant trainer with a leader international companies located in USA and UK .
I spent over five months of hard working to create +270 lectures/Articles with high quality to make all students enjoy and love statistics .
I'm sharing a lot of practical experience from my own work with you in this course .
What is my final goal after my students enroll in this course ?
My final goal is to make all students and engineers love practical statistics .
My big challenge in this course is to make it professional course at the same time it should be very easy and simple for all People .
Therefore you will notice that i used a lot of graphics and imaginary ideas to make you LOVE STATISTICS .
What is course contents ?
Starting with FAQ related to interview questions in your career .
What is after data analysis ?
Descriptive statistics with collection of important quizzes and examples .
Normal distribution and standard normal in details using Z table .
Sampling distribution with practical simulation apps and answering of important technical questions .
Confidence level and Confidence interval .
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Code:

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