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What you'll learn?
- Understand the Fundamentals of Statistics
- Understand the basics of Probability
- Learn how to work with Different Types of Data
- Distinguish and work with Different Types of Distributions
- Apply statistical methods and hypothesis testing to business problems
- Understand all the concepts needed for data science
- Understand the working of Regression Analysis
- mplement one way and two way ANOVA
- Learn Chi-Square Analysis
Course Overview
If you are starting a career in Data Science or Business Analysis, then this course will help you to Built a Strong Foundation
This course is Very Practical, Easy to Understand and Every Concept is explained with an example
I have specifically included real-world examples to show, how you could apply this knowledge to boost YOUR career
We'll cover everything you need to know about statistics and probability for Data Science and Business Analysis!
Including:
1) Levels of Measurement
2) Measures of Central Tendency
3) Population and Sample
4) Population Standard Variance
5) Quartiles and IQR
6) Permutations,Combinations
7) Intersection, Union and Complement
8) Independent and Dependent Events
9) Conditional Probability
10) Bayes’ Theorem
11) Uniform Distribution, Binomial Distribution
12) Poisson Distribution, Normal Distribution, Skewness
13) Standardization and Z Score
14) Central Limit Theorem
15) Hypothesis Testing, Type I and Type II Error
16) Students T-Distribution
17) ANOVA - Analysis of Variance
18) F Distribution
19) Linear Regression and much more...
So what are you waiting for?
Enroll now and empower your career!
Pre-requisites
- Basic knowledge of high school Mathematics
Target Audience
- People who want to pursue their career in Data Science
- Business Intelligence
- Business analysis
- Anyone who wants to learn Statistics and how to use it in Business World
Curriculum 39 Lectures 04:45:44
-
Section 1 : Unpublished Section
-
Section 2 : Statistic Basics
- Lecture 1 :
- Data
- Lecture 2 :
- Levels of Measurement
- Lecture 3 :
- Measures of Central Tendency
- Lecture 4 :
- Population and Sample
- Lecture 5 :
- Measures of Dispersion
- Lecture 6 :
- Quartiles and IQR
-
Section 3 : Probability
- Lecture 1 :
- Introduction to Probability
- Lecture 2 :
- Permutations
- Lecture 3 :
- Combinations
- Lecture 4 :
- Intersection, Union and Complement
- Lecture 5 :
- Independent and Dependent Events
- Lecture 6 :
- Conditional Probability
- Lecture 7 :
- Addition and Multiplication Rules
- Lecture 8 :
- Bayes’ Theorem
-
Section 4 : Distribution
- Lecture 1 :
- Introduction to Distribution
- Lecture 2 :
- Uniform Distribution
- Lecture 3 :
- Binomial Distribution
- Lecture 4 :
- Poisson Distribution
- Lecture 5 :
- Skewness
- Lecture 6 :
- Standardization and Z Score
-
Section 5 : Central Limit Theorem
- Lecture 1 :
- Central Limit Theorem
-
Section 6 : Hypothesis Testing
- Lecture 1 :
- Hypothesis Testing and Hypothesis Formulation
- Lecture 2 :
- Important Concepts in Hypothesis Testing
- Lecture 3 :
- Exercise 1
- Lecture 4 :
- Exercise 2
- Lecture 5 :
- Type I and Type II Error
- Lecture 6 :
- Students T-Distribution
- Lecture 7 :
- Exercises on Students T-Distribution
-
Section 7 : ANOVA - Analysis of Variance
- Lecture 1 :
- ANOVA - Analysis of Variance
- Lecture 2 :
- F Distribution
- Lecture 3 :
- One-Way ANOVA
- Lecture 4 :
- Two-Way ANOVA
- Lecture 5 :
- Two-Way ANOVA Exercise
- Lecture 6 :
- Two-Way ANOVA with Replication
-
Section 8 : Regression Analysis
- Lecture 1 :
- Linear Regression
- Lecture 2 :
- Exercise on Linear Regression
- Lecture 3 :
- Multiple Regression
-
Section 9 : Chi-Square Analysis
- Lecture 1 :
- Chi-Square Test
Our learners work at
Frequently Asked Questions
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