Probability and statistics are essential for people who work with Data, especially in Data Analysis and or Data Science. During this course you will be introduced to the basics, such as what is a random variable, probability of its occurrence and probability mass/density function. Next, more complex topics. You will be able to perform exploratory data analysis to draw insights from the raw data. In addition to this, you will understand the math behind A/B testing using statistical hypothesis testing framework, that will help with decision making.
- Intro to Probabilistic Thinking;
- Distributions of Random Variables;
- Main Characteristics of Distribution;
- Quantities of Information; Outliers in the Data;
- Approximation Results and Confidence Intervals;
- Significance Testing, Part 1: Inference for a Mean, Inference for a Proportion;
- Significance Testing, Part 2: Power of the Test, Effect Size;
- Extra Day for Q&A and More Practice.
You will learn:
- What are the types of random variables and how they are distributed;
- How to use Bayes’s rule to find the probability of event occurrence;
- How to calculate main characteristics of a distribution, such as mean, median and variance;
- How to detect outliers in the data and how to deal with them;
- What are confidence intervals and why do we need them;
- What is a p-value and effect size of the test;
- How to perform a null hypothesis significance testing.
This is exactly for you if:
- You are looking for a career change into Data Science/Analysis;
- You need to boost your stats knowledge and skills;
- You want to know how to run A/B tests;
- You are already working in a Data Science field and want to systematize your knowledge.