**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