Course Includes
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Recorded Lessons: 16
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Recorded Hours: 2
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Duration:
2 days (Avg)
Course Features
Access on mobile
TDP Assessment Test
Top Skills Covered
Overview
Course Description
On the off chance that you are going for a profession as a Data Scientist or Business Analyst at that point looking over your statistics abilities is something you have to do.
In any case, it's only difficult to begin... Learning/re-adapting ALL of details just appears like an overwhelming undertaking.
That is precisely why we have made this course!
Here you will rapidly get the significant details learning for a Data Scientist or Analyst.
This isn't simply one more exhausting course on details.
This course is exceptionally pragmatic.
I have particularly included true models of business difficulties to demonstrate to you how you could apply this learning to help your vocation.
In the meantime you will ace points, for example, dispersions, the z-test, the Central Limit Theorem, theory testing, certainty interims, measurable criticalness and some more!
So what are you sitting tight for?
Select now and enable your profession!
Who this course is for:
- People working in any numerate field which requires data analysis
- People carrying out observational or experimental studies
- Any one who want to make career in Data Science
What you'll learn
- Understand what a Normal Distribution is
- Apply Hypothesis Testing for Means
- Understand standard deviations
- Apply the Central Limit Theorem
- Difference between continuous and discrete variables
- Use the Z-Score and Z-Tables
- what a sampling distribution is
Requirements
- Interest in Learning Statistical Modelling
- Just a basic knowledge of high school maths
- People working in any numerate field which requires data analysis
- People carrying out observational or experimental studies
- Any one who want to make career in Data Science
Course Content
16 Lessons | 2:00 Total hours
Distribution
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1. Introduction 00:01:51 -
2. Continues and Discrete Variables 00:04:30
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3. what is Distribution 00:10:31
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4. What is standard Deviation 00:04:15
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5. Skewness 00:03:39
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6. Kurtosis 00:03:34
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7. Mean, Median, Mode and Range 00:06:23
Central Limit Theorem
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1. What is populations and samples 00:03:00
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2. sampling distribution 00:04:17
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3. Law of Large Numbers 00:02:15
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4. Central Limit Theorem 00:08:34
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5. Z-Score 00:05:54
Hypothesis Testing
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1. Hypothesis Testing 00:02:08
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2. Formula's for Hypothesis Testing 00:02:52
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3. P-Value and critical Value 00:02:50
Number Summary
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1. Number Summary 00:05:38
Frequently asked questions
What is statistical modeling in data science?
Statistical modeling is the process of creating mathematical representations of real-world processes or systems using statistical techniques.
What topics will be covered in this course?
The course will cover topics such as probability theory, regression analysis, hypothesis testing, ANOVA (Analysis of Variance), time series analysis, and machine learning techniques relevant to statistical modeling.
What topics will be covered in this course?
The course will cover topics such as probability theory, regression analysis, hypothesis testing, ANOVA (Analysis of Variance), time series analysis, and machine learning techniques relevant to statistical modeling.
Who is this course designed for?
This course is designed for data scientists, analysts, and anyone interested in learning how to apply statistical methods to analyze data. It is suitable for individuals with a basic understanding of statistics and data analysis.
What topics will be covered in this course?
The course will cover topics such as probability theory, regression analysis, hypothesis testing, ANOVA (Analysis of Variance), time series analysis, and machine learning techniques relevant to statistical modeling.
Do I need prior knowledge of statistics to take this course?
While a basic understanding of statistics is beneficial, the course will start with fundamental concepts. Participants with varying levels of expertise can benefit from the content.