Full Data Science Course- From Zero to Hero

Full Data Science Course: From Zero to Hero, available at $19.99, with 32 lectures, and has 4 subscribers.

You will learn about Learn the Main Concepts of Inferential Statistics Calculate Parameters using Advanced Statistics Techniques Reach conclusions using Hypothesis Testing Calculate Confidence Intervals Calculate ANOVAS: 1-Way and 2-Way Calculate Estimators using MME, MLE, OLS This course is ideal for individuals who are Beginner Data Science students and professionals It is particularly useful for Beginner Data Science students and professionals.

Enroll now: Full Data Science Course: From Zero to Hero

Summary

Title: Full Data Science Course: From Zero to Hero

Price: $19.99

Number of Lectures: 32

Number of Published Lectures: 32

Number of Curriculum Items: 32

Number of Published Curriculum Objects: 32

Original Price: $22.99

Quality Status: approved

Status: Live

What You Will Learn

Learn the Main Concepts of Inferential Statistics

Calculate Parameters using Advanced Statistics Techniques

Reach conclusions using Hypothesis Testing

Calculate Confidence Intervals

Calculate ANOVAS: 1-Way and 2-Way

Calculate Estimators using MME, MLE, OLS

Who Should Attend

Beginner Data Science students and professionals

Target Audiences

Beginner Data Science students and professionals

In this course, you will take the first step in your Data Science journey by learning Inferential Statistics.

Data Science Professionals in Machine Learning, Artificial Intelligence, and all professionals in several fields like Finance, Psychology, and the Medical Field, all require an understanding of Statistics. It is the core language of all these fields when it comes to Data Analysis.

You will be able to understand and master Machine Learning concepts when you understand the key foundations behind them. These come from mastering: Statistics and Mathematical Modelling.

Course Outline:

1. Master the Inferential Statistics Terminology and Concepts

Random Variables, Random Samples, the 4 types of Data, NOIR, Experiments vs Trials and Events.

2. Master the Discrete and Continuous Distributions and their Sub-Functions so you can know when and how to use them

Binomial, Bernoulli, Negative Binomial, Geometric, Poisson, Exponential, Uniform, Normal, T-Student, Chi-Squared, and F-Distribution.

3. Master Conversions from any Distribution to the Normal Distribution

From N to Z, from T to Z, from Chi-Squared to Z

4. Learn how to conduct Hypothesis Tests

1-Tailed and 2-Tailed, how to use any Statistical Table, Find Critical Values, compare to calculated test statistics, and make Conclusions.

5. Learn how to indicate conclusions based on percentages.

6. Learn how to build Confidence Intervals for a Population Parameter

7. Learn how to calculate Population Estimators using Advanced Statistics Techniques 

Ordinary Least Squares (OLS), Method of Moments Estimator (MME), and Maximum Likelihood Estimator(MLE)

Course Curriculum

Chapter 1: Introduction & Getting Started

Lecture 1: Introduction

Lecture 2: What is the purpose of Statistics?

Chapter 2: Key Terminology

Lecture 1: Population vs Sample

Lecture 2: Inferential Approaches: Estimation

Lecture 3: Inferential Approaches: Hypothesis Testing

Lecture 4: Population Parameters and Sample Statistics

Lecture 5: Data Types(Qualitative and Quantitative), Samples

Lecture 6: Trials, Experiments, Events, Independence and Likelihood

Chapter 3: Distributions: key properties and theorems

Lecture 1: What is a distribution?

Lecture 2: Discrete: Binomial and Bernoulli

Lecture 3: Discrete: Negative Binomial and Geometric

Lecture 4: Discrete: Poisson

Lecture 5: Continuous: Exponential

Lecture 6: Continuous: Uniform

Lecture 7: Continuous: Normal and Central Limit Theorem

Lecture 8: Standardization Z-Score

Lecture 9: T-Student Distribution

Lecture 10: When to use N vs T-Student Distribution

Lecture 11: Continuous: Chi-Squared

Lecture 12: F-Distribution and ANOVA

Lecture 13: Probability Functions Revisited: Tables, PMF, PDF and CDF

Chapter 4: Hypothesis Testing and Confidence Intervals

Lecture 1: What is an Hypothesis test, procedures and errors

Lecture 2: Estimators and Main Techniques

Lecture 3: Estimators: OLS

Lecture 4: Estimators: MLE

Lecture 5: Estimators: MME

Chapter 5: Section I Summary

Lecture 1: Summary of Section I

Chapter 6: PART 2: Exercises: Hypothesis Testing

Lecture 1: 1-WAY ANOVA Exercises

Lecture 2: 2-WAY ANOVA Exercises

Chapter 7: PART 2: Exercises: Confidence Intervals

Lecture 1: How to build a confidence interval

Chapter 8: PART 2: Exercises: Find k, E(X) and V(X) from Functions

Lecture 1: find k, E(X) and Var(X)

Chapter 9: Summary

Lecture 1: Course Summary

Instructors

Diogo Marques
Independent Financial Advisor at Grupo Marques Seguros Vida

Rating Distribution

1 stars: 0 votes

2 stars: 0 votes

3 stars: 0 votes

4 stars: 0 votes

5 stars: 0 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!