English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 5 Hours | 1.74 GB
Learn how to apply probability and statistics to real data science and business applications!
In this course we cover what you need to know about probability and statistics to succeed in business and the data science field!
This practical course will go over theory and implementation of statistics to real world problems. Each section has example problems, in course quizzes, and assessment tests.
We’ll start by talking about the basics of data, understanding how to examine it with measurements of central tendency, dispersion, and also building an understanding of how bivariate data sources can relate to each other.
Afterwards we’ll dive into probability , learning about combinations and permutations, as well as conditional probability and how to apply bayes theorem.
Then we’ll move on to discussing the most common distributions found in statistics, creating a solid foundation of understanding how to work with uniform, binomial, poisson, and normal distributions.
Up next we’ll talk about statistics, applying what we’ve learned so far to real world business cases, including hypothesis testing and the student’s T distribution.
We’ll end the course with 3 sections on advanced topics, such as ANOVA (analysis of variance), understanding regression analysis, and finally performing chi squared analysis.
The sections are modular and organized by topic, so you can reference what you need and jump right in!
Our course includes HD Video with clear explanations and high quality animations, we also include extensive case studies to show you how to apply this knowledge to the real world.
We’ll cover everything you need to know about statistics and probability to clearly tackle real world business and data science problems!
Including:
- Measurements of Data
- Mean, Median, and Mode
- Variance and Standard Deviation
- Co-variance and Correlation
- Permutations and Combinations
- Unions and Intersections
- Conditional Probability
- Bayes Theorem
- Binomial Distribution
- Poisson Distribution
- Normal Distribution
- Sampling
- Central Limit Theorem
- Hypothesis Testing
- T-Distribution Testing
- Regression Analysis
- ANOVA
- Chi Squared
- and much more!
What Will I Learn?
- Understand the basics of probability
- Be able to implement basic statistics
- Understand how to use various statistical distributions
- Apply statistical methods and hypothesis testing to business problems
- Understand how regression models work
- Implement one way and two way ANOVA
- Understand Chi Squared Tests
- Be able to understand different types of data
Table of Contents
Introduction
1 Course Overview Lecture – PLEASE DO NOT SKIP THIS
2 FAQ – Frequently Asked Questions
Data
3 What is Data
4 Measuring Data
5 Measurements of Central Tendency
6 Measurements of Dispersion
7 Measurements – Quartiles
8 Bi-variate Data and Covariance
9 Pearson Correlation Coefficient
10 Section Assessment – Data
Probability
11 What is Probability
12 Permutations
13 Combinations
14 Intersections Unions and Complements
15 Independent and Dependent Events
16 Conditional Probability
17 Addition and Multiplication Rules
18 Bayes Theorem
19 Section Assessment – Probability
Distributions
20 Introduction to Distributions
21 Uniform Distribution
22 Binomial Distribution
23 Poisson Distribution
24 Normal Distribution
25 Normal Distribution – Formulas and Z Scores
26 Section Assessment – Distributions
27 Optional Resource – Dash Scripts
Statistics
28 What is Statistics
29 Sampling
30 Central Limit Theorem
31 Standard Error
32 Hypothesis Testing
33 Hypothesis Testing Example Exercise 1
34 Hypothesis Testing Example Exercise 2
35 Type 1 and Type 2 Errors
36 Students T Distribution
37 Students T Distribution Example Exercise
38 Section Assessment – Statistics
Analysis of Variance (ANOVA)
39 Introduction to ANOVA
40 ANOVA – Analysis of Variance
41 F Distribution
42 Two Way ANOVA Overview
43 Two Way ANOVA Example Exercise
44 Two Way ANOVA with Replication
45 Section Assessment – ANOVA
Regression
46 Linear Regression
47 Regression Example
48 Multiple Regression
49 Section Assessment – Regression
Chi-Square Analysis
50 Chi-Square Analysis
51 Chi Squared Analysis – Exercise Example
52 Section Assessment – Chi Square Analysis
BONUS COUPONS FOR OTHER COURSE
53 Bonus Coupons for other Courses
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