Binomial, Normal Distribution, Matrices for Data Science
Building on the Foundation: Binomial & Normal Distribution, CRISP DM, Anova, Matrices, Coordinate Geometry, Calculus.
What you’ll learn
- You will understand the concept of Binomial Distribution using examples
Requirements
- No prior experience is required. We will start from the very basics. You will benefit by going through our part 2 course which lays the foundation
Description
Building on the Foundation:
You will learn the following concepts with examples in this course:
Normal distribution describes continuous data which have a symmetric distribution, with a characteristic ‘bell’ shape.
Binomial distribution describes the distribution of binary data from a finite sample. Thus it gives the probability of getting r events out of n trials.
Decision making: You can calculate the probability that an event will happen by dividing the number of ways that the event can happen by the number of total possibilities. Probability can help you to make better decisions, such as deciding whether or not to play a game where the outcome may not be immediately obvious.
CRISP–DM is a cross-industry process for data mining. The CRISP–DM methodology provides a structured approach to planning a data mining project. It is a robust and well-proven methodology.
Hypothesis testing is an act in statistics whereby an analyst tests an assumption regarding a population parameter. Such data may come from a larger population, or from a data-generating process.
Who this course is for:
- The course is also ideal for beginners, as it starts from the fundamentals and gradually builds up your skills
- You should take this course if you want to become a Data Scientist or if you want to learn about the field