technifyed

Overview

In 44 episodes, Adriene Hill teaches you Statistics! This course is based on the 2018 AP Statistics curriculum and introduces everything from basic descriptive statistics to data collection to hot topics in data analysis like Big Data and neural networks. By the end of the course, you will be able to:

*Identify questions that can be answered using statistics

*Describe patterns, trends, associations, and relationships in data both numerically and graphically

*Justify a conclusion using evidence from data, definitions, or statistical inference

*Apply statistical models to make inferences and predictions from data sets

*Understand how statistics are used broadly in the world and interpret their meaning, like in newspapers or scientific studies

Videos in this playlist 45

  1. Crash Course Statistics Preview
  2. What Is Statistics: Crash Course Statistics #1
  3. Mathematical Thinking: Crash Course Statistics #2
  4. Mean, Median, and Mode: Measures of Central Tendency: Crash Course Statistics #3
  5. Measures of Spread: Crash Course Statistics #4
  6. Charts Are Like Pasta - Data Visualization Part 1: Crash Course Statistics #5
  7. Plots, Outliers, and Justin Timberlake: Data Visualization Part 2: Crash Course Statistics #6
  8. The Shape of Data: Distributions: Crash Course Statistics #7
  9. Correlation Doesn't Equal Causation: Crash Course Statistics #8
  10. Controlled Experiments: Crash Course Statistics #9
  11. Sampling Methods and Bias with Surveys: Crash Course Statistics #10
  12. Science Journalism: Crash Course Statistics #11
  13. Henrietta Lacks, the Tuskegee Experiment, and Ethical Data Collection: Crash Course Statistics #12
  14. Probability Part 1: Rules and Patterns: Crash Course Statistics #13
  15. Probability Part 2: Updating Your Beliefs with Bayes: Crash Course Statistics #14
  16. The Binomial Distribution: Crash Course Statistics #15
  17. Geometric Distributions and The Birthday Paradox: Crash Course Statistics #16
  18. Randomness: Crash Course Statistics #17
  19. Z-Scores and Percentiles: Crash Course Statistics #18
  20. The Normal Distribution: Crash Course Statistics #19
  21. Confidence Intervals: Crash Course Statistics #20
  22. How P-Values Help Us Test Hypotheses: Crash Course Statistics #21
  23. P-Value Problems: Crash Course Statistics #22
  24. Playing with Power: P-Values Pt 3: Crash Course Statistics #23
  25. You Know I'm All About that Bayes: Crash Course Statistics #24
  26. Bayes in Science and Everyday Life: Crash Course Statistics #25
  27. Test Statistics: Crash Course Statistics #26
  28. T-Tests: A Matched Pair Made in Heaven: Crash Course Statistics #27
  29. Degrees of Freedom and Effect Sizes: Crash Course Statistics #28
  30. Chi-Square Tests: Crash Course Statistics #29
  31. P-Hacking: Crash Course Statistics #30
  32. The Replication Crisis: Crash Course Statistics #31
  33. Regression: Crash Course Statistics #32
  34. ANOVA: Crash Course Statistics #33
  35. ANOVA Part 2: Dealing with Intersectional Groups: Crash Course Statistics #34
  36. Fitting Models Is like Tetris: Crash Course Statistics #35
  37. Supervised Machine Learning: Crash Course Statistics #36
  38. Unsupervised Machine Learning: Crash Course Statistics #37
  39. Intro to Big Data: Crash Course Statistics #38
  40. Big Data Problems: Crash Course Statistics #39
  41. Statistics in the Courts: Crash Course Statistics #40
  42. Neural Networks: Crash Course Statistics #41
  43. War: Crash Course Statistics #42
  44. When Predictions Fail: Crash Course Statistics #43
  45. When Predictions Succeed: Crash Course Statistics #44

Advantages and disadvantages

Advantages

  • Free and complete: every lecture is in the playlist, in order.
  • Watch at 1.5x, skip what you know, rewatch what you don't.
  • 2,249,504 views, so help and notes are easy to find.
  • Completely free.
  • Self-paced: start any time.
  • A clear syllabus (45 videos) you can see before you start.
  • Start watching right away, no sign-up.

Disadvantages

  • No certificate, deadlines or graded work.
  • Quality and depth vary: check that the playlist is finished before you start.
  • No certificate.
  • No graded assignments or feedback.
  • Recorded in 2018: tools may have changed since.

Some points apply to every course of this kind; see how we rank.

Free

  • Free: Watch on YouTube, no account needed.
  • Certificate: None. Code or take notes along to make it stick.

Crash Course is in Tier 3: good universities, respected companies, nonprofits and well-known teachers of our institution ranking (78/100).

Similar courses

Compare these

Stanford University · YouTube

AI Luminaries

A curated collection of videos featuring top experts in artificial intelligence.

Free video20 videos, 15 hours

Stanford University · YouTube

Generative AI Program

-Learn more and enroll in the program: https://online.stanford.edu/programs/generative-ai-technology-business-and-society-program Recent advancements in generative AI are reshaping industries, pushing technological boundaries, and revolutionizing creative processes. In this fast-changing landscape…

Free video24 videos, 2 hours

Stanford University · YouTube

Artificial Intelligence

For more information about Stanford's Artificial Intelligence programs visit: https://stanford.io/ai Delve into the exciting world of Artificial Intelligence with insights and research from the world's top experts. This playlist offers a comprehensive journey, from foundational machine learning con…

Free video187 videos, 142 hours

Stanford University · YouTube

Stanford CS229: Machine Learning led by Andrew Ng | Autumn 2018

Led by Andrew Ng, this course provides a broad introduction to machine learning and statistical pattern recognition. Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (cluster…

Free video21 videos, 28 hours

Massachusetts Institute of Technology · YouTube

MIT 6.034 Artificial Intelligence, Fall 2010

View the complete course: http://ocw.mit.edu/6-034F10 Instructor: Patrick Winston In these lectures, Prof. Patrick Winston introduces the 6.034 material from a conceptual, big-picture perspective. Topics include reasoning, search, constraints, learning, representations, architectures, and probabili…

Free video30 videos, 24 hours

Stanford University · YouTube

Large Language Models (LLMs)

Explore the cutting-edge realm of Large Language Models (LLMs) with this expertly curated YouTube playlist. Featuring insights from industry leaders, educational content, and in-depth discussions, this selection highlights the latest advancements in LLM technology.

Free video26 videos, 42 hours

Enter your email and the official page opens. Phone is optional.