# Full Data Science Course: From Zero to Hero

Learn and build projects with Inferential Statistics

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

What you’ll 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.

Course Content

• Introduction & Getting Started –> 2 lectures • 5min.
• Key Terminology –> 6 lectures • 12min.
• Distributions: key properties and theorems –> 13 lectures • 17min.
• Hypothesis Testing and Confidence Intervals –> 5 lectures • 10min.
• Section I Summary –> 1 lecture • 1min.
• PART 2: Exercises: Hypothesis Testing –> 2 lectures • 6min.
• PART 2: Exercises: Confidence Intervals –> 1 lecture • 4min.
• PART 2: Exercises: Find k, E(X) and V(X) from Functions –> 1 lecture • 7min.
• Summary –> 1 lecture • 1min.

Requirements

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)

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