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
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.