Building on the Foundation: Binomial & Normal Distribution, CRISP DM, Anova, Matrices, Coordinate Geometry, Calculus

## Description

Building on the Foundation:

In this course we continue to build your foundation on Data Science. In our Part 2 course you learned Probability, Descriptive Statistics, Data Visualization, Histogram, Boxplot & Scatter plot, Covariance & Correlation. In Part 3 we will help you learn Binomial & Normal Distribution, TOH, CRISP-DM, Anova, Matrices, Coordinate Geometry & Calculus.

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.

**Z**–**distribution** is used to help find probabilities and percentiles for regular normal **distributions** (X). It serves as the standard by which all other normal **distributions** are measured.

**Central limit theorem** (**CLT**) establishes that, in some situations, when independent random variables are added, their properly normalized sum tends toward a normal distribution (informally a bell curve) even if the original variables themselves are not normally distributed.

**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. **Hypothesis testing** is used to assess the plausibility of a **hypothesis** by using sample data. Such data may come from a larger population, or from a data-generating process.

Analysis of variance (**ANOVA**) is a collection of statistical models and their associated estimation procedures (such as the “variation” among and between groups) used to analyze the differences among group means in a sample. **ANOVA** was developed by statistician and evolutionary biologist Ronald Fisher.

**Basics** of Matrices, Coordinate Geometry, Calculus & Algebra

Through our **Four-part series** we will take you **step by step**, this course is our **third part** which will solidify your foundation.

## 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

## What you’ll learn

- You will understand the concept of Binomial Distribution using examples
- You will learn more on Continuous Random Variables
- You will learn what Normal Distribution is using examples
- You will understand the Z table Distribution
- You will get clarity on Central Limit Theorem – CLT and how CLT is used.
- You will understand what Decision Making is and how it is used
- You will learn on CRISP DM Framework
- You will learn the concept of Test of Hypothesis- TOH and Statistical methods
- You will learn what is Anova- Analysis of variance and its application
- You will also learn the basics of Matrices, Coordinate Geometry, Calculus & Algebra

## 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

100% FREE

## Binomial, Normal Distribution, Matrices for Data Science

**Expires on:**