In this course you will learn about statistics and probability by solving practical problem. Along the way you will solve statistics and probability practice questions and learn their concepts.
⭐ Table of Contents ⭐
⌨️ (0:00) Statistics Example: Sample and Population
⌨️ (1:07) Statistics Example: Representative sample
⌨️ (3:10) Statistics Example: Representative sample 2
⌨️ (4:40) Statistics Example: Sampling methods
⌨️ (8:58) Statistics Example: Dot plot
⌨️ (11:59) Statistics Example: Frequency table
⌨️ (13:02) Statistics Example: Frequency distribution
⌨️ (15:43) Statistics Example: Grouped frequency distribution
⌨️ (18:57) Statistics Example: Histogram
⌨️ (21:05) Statistics Example: Bar chart
⌨️ (23:26) Statistics Example: Stem and leaf plot
⌨️ (26:02) Statistics Example: Scatter plot
⌨️ (29:51) Statistics Example: Median
⌨️ (31:55) Statistics Example: Weighted average
⌨️ (35:03) Statistics Example: Mode
⌨️ (35:45) Statistics Example: Range
⌨️ (36:52) Statistics Example: Standard deviation
⌨️ (39:33) Statistics Example: Regression line
⌨️ (41:09) Statistics Example: Statistical prediction
⌨️ (43:50) Statistics Example: Linear regression
⌨️ (50:46) Statistics Example: The empirical rule
⌨️ (51:39) Statistics Example: The empirical rule 2
⌨️ (53:19) Statistics Example: The empirical rule 3
⌨️ (57:19) Statistics Example: Z-scores
⌨️ (58:50) Statistics Example: Z-scores 2
⌨️ (1:00:45) Statistics Example: Margin of error
⌨️ (1:00:45) Probability - sample space
⌨️ (1:05:11) Probability - sample space 2
⌨️ (1:06:07) Probability example - rolling a die
⌨️ (1:07:43) Probability example - drawing a card
⌨️ (1:08:31) Probability example - picking a co0kie
⌨️ (1:09:41) Probability example - empirical probability
⌨️ (1:10:32) Empirical probability example 2
⌨️ (1:11:36) Empirical probability example 3
⌨️ (1:13:25) Probability example - Demonstrating the law of large number
⌨️ (1:15:50) Probability example - Or with disjoint event
⌨️ (1:18:05) Probability example - Or with disjoint event 2
⌨️ (1:20:17) Probability example - Or with non-disjoint event
⌨️ (1:22:44) Probability example - Or with non-disjoint event 2
⌨️ (1:25:23) Probability example - not hearts
⌨️ (1:26:37) Probability example - not correct answer for multiple choice
⌨️ (1:27:48) Probability example - not female
⌨️ (1:28:47) Probability example - independent events
⌨️ (1:30:48) Probability example - coins and dice
⌨️ (1:31:58) Probability example - And with independent events
⌨️ (1:33:34) Probability example - And with independent events 2
⌨️ (1:34:56) Probability example - And with dependent events
⌨️ (1:36:26) Probability with M&Ms
⌨️ (1:43:40) Conditional probability with contingency table
⌨️ (1:45:36) Fundamental counting principle
⌨️ (1:46:41) Fundamental counting principle 2
⌨️ (1:47:27) Fundamental counting principle 3
⌨️ (1:48:17) include factorials
⌨️ (1:49:51) Probability example - permutations
⌨️ (1:52:39) Probability example - permutations 2
⌨️ (1:54:30) Probability example - combinations
⌨️ (1:56:38) Probability example - combinations 2
⌨️ (1:57:23) Probability example - combinations 3
⌨️ (1:58:03) Probability with the fundamental counting principle
⌨️ (1:59:30) Probability with combinations
⌨️ (2:01:22) Probability with combinations 2
⌨️ (2:03:47) Probability with permutations
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Take your data science and statistics knowledge to the next level with the latest addition to our data science course offerings: Conditional Probability.
In this course, you’ll learn about the basics of conditional probability and then dig into more advanced concepts like Bayes’s Theorem and Naive Bayes algorithms. As you learn, you’ll be using your Python skills to put theory into practice and build a working knowledge of these critical statistics concepts.
Ready to start learning? Click the button below to dive into Conditional Probability, or scroll down to learn more about this new course.
Conditional Probability is an area of probability theory that’s concerned with — as the name suggests — measuring the probability of a particular event occurring based on certain conditions.
In this course, which builds off of the Probability Fundamentals course that precedes it, we’ll start with some lessons on foundational concepts like the conditional probability formula, the multiplication rule, statistical dependence and independence, and more.
From there, we’ll look at Bayes’ Theorem and how it can be used to calculate probabilities. We’ll examine prior and posterior probability distributions. Then we’ll dig in and apply some of these statistical concepts by learning about the Naive Bayes algorithm, a common statistical tool employed by data scientists.
Finally, you’ll put all your new knowledge into practice in a new guided project that challenges you to build an SMS spam filter using a data set of over 5,000 messages by employing a Naive Bayes algorithm.
By the end of the course, you’ll feel comfortable assigning probabilities to events based on conditions using the rules of conditional probability. You’ll know when these events have statistical dependence (or not) on other events. You’ll be able to assign probabilities based on prior knowledge using Bayes’s theorem.
And of course you’ll have built a cool SMS spam filter that makes use of a Naive Bayes algorithm (and your Python programming skills)!
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