English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 68 lectures (16h 7m) | 2.91 GB

Learn about probability, statistics, and more using the mathematics that are foundational to the field of data science.

Welcome to the best online course for learning about the Math behind the field of Data Science!

Working together for the first time ever, Krista King and Jose Portilla have combined forces to deliver you a best in class course experience in how to use mathematics to solve real world data science problems. This course has been specifically designed to help you understand the mathematical concepts behind the field of data science, so you can have a first principles level understanding of how to use data effectively in an organization.

Often students entering the field of data science are confused on where to start to learn about the fundamental math behind the concepts. This course was specifically designed to help bridge that gap and provide students a clear, guided path through the complex and interesting world of math used in the field of data science. Designed to balance theory and application, this is the ultimate learning experience for anyone wanting to really understand data science.

Why choose this course?

Combined together, Krista and Jose have taught over 3.2 million students about data science and mathematics and their joint expertise means you’ll be able to get the best and clearest mathematical explanations from Krista with framing about real world data science applications from Jose. At the end of each section is a set of practice problems developed from real-world company situations, where you can directly apply what you know to test your understanding.

What’s covered in this course?

- In this course, we’ll cover:
- Understanding Data Concepts
- Measurements of Dispersion and Central Tendency
- Different ways to visualize data
- Permutations
- Combinatorics
- Bayes’ Theorem
- Random Variables
- Joint Distributions
- Covariance and Correlation
- Probability Mass and Density Functions
- Binomial, Bernoulli, and Poisson Distributions
- Normal Distribution and Z-Scores
- Sampling and Bias
- Central Limit Theorem
- Hypothesis Testing
- Linear Regression
- and much more!

What you’ll learn

- Understand core concepts about data quality and quantity
- Learn about how to measure data with statistics
- Discover how to visualize data with a variety of plot types
- Use combinatorics to calculate permutations and combinations of objects
- Understand the key ideas in using probability to solve problems
- Learn how to use data distributions with real world data
- Discover the powerful insights from the normal distribution
- Use sampling and the central limit theorem
- Understand hypothesis testing on sample groups
- Cover the basics of linear regression

## Table of Contents

**Introduction**

1 Welcome to the Course Important Info in this Lecture

2 Course Overview and Curriculum

**Core Data Concepts**

3 Introduction to Core Data Concepts

4 Measurements of Central Tendency Mean Median and Mode

5 Measurements of Dispersion Variance and Standard Deviation

6 Quartiles and IQR

**Visualizing Data**

7 Introduction to Visualizing Data

8 Scatter Plots

9 Line Plots

10 Distribution Plots Histograms

11 Categorical Plots Bar Plots

12 CategoricalDistribution Plots Box and Whisker Plots

13 Other Plot Types Violin Plot KDE Plot

14 Common Plot Pitfalls

**Combinatorics**

15 Introduction to Combinatorics

16 Factorials

17 Permutations

18 Combinations

19 Combinatorics Practice Problem Set and Answers

**Probability**

20 Introduction to Probability

21 Probability Law of Large Numbers Experimental vs Expected

22 The Addition Rule Union and Intersection Venn Diagrams

23 Conditional Probability Independent and Dependent

24 Bayes Theorem

25 Discrete Probability

26 Transforming Random Variables

27 Combinations of Random Variables

28 Probability Practice Problem Set and Answers

**Joint Distributions**

29 Introduction to Joint Distributions

30 Covariance

31 Pearson Correlation Coefficient

32 Joint Distribution Practice Problem Set and Answers

**Data Distributions**

33 Introduction to Data Distributions

34 Probability Mass Functions

35 Discrete Uniform Distribution Dice Roll

36 Probability Density Functions

37 Continuous Uniform Distribution Voltage

38 Cumulative Distribution Functions

39 Binomial Distribution

40 Bernoulli Distribution

41 Poisson Distribution

42 Data Distributions Practice Problem Set and Answers

**The Normal Distribution**

43 Introduction to The Normal Distribution

44 Mean Variance and Standard Deviation

45 Normal Distribution

46 Standard Normal Distribution

47 ZScores

48 Normal Distribution Practice Problem Set and Answers

**Sampling**

49 Introduction to Sampling

50 Sampling and Bias

51 The Central Limit Theorem

52 The Students tDistribution

53 Confidence Interval for the Mean

54 Sampling Practice Problem Set and Answers

**Hypothesis Testing**

55 Introduction to Hypothesis Testing

56 Inferential Statistics and Hypotheses

57 Significance Level and Type I and II Errors

58 Test Statistics for One and TwoTailed Tests

59 The pValue and Rejecting the Null

60 AB Testing

61 Hypothesis Testing Practice Problem Set and Answers

**Regression**

62 Introduction to Regression

63 Scatterplots and Regression

64 Correlation Coefficient and the Residual

65 Coefficient of Determination and the RMSE

66 ChiSquare Tests

67 ANOVA

68 Regression Practice Problem Set and Answers

Resolve the captcha to access the links!