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Statistics Foundations for Data Science (Part2)

Theory and Practice with Python

Takuma Kimura

Summary

Price
£50 inc VAT
Or £16.67/mo. for 3 months...
Study method
Online, On Demand What's this?
Duration
4.6 hours · Self-paced
Qualification
No formal qualification
Certificates
  • Reed courses certificate of completion - Free

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Overview

Welcome to Statistics Foundations for Data science and Analytics.

This course is designed for beginners who are interested in statistical analysis and data science. And anyone who is not a beginner but wants to go over from the basics is also welcome! This course consists of three levels. When you complete all levels, you master undergraduate level statistics.

Curriculum

6
sections
65
lectures
4h 35m
total

    • 3: What is Probability? 02:54
    • 4: Calculate Probability 03:38
    • 5: Combination & Probability 04:05
    • 6: Statistical Independence 05:00
    • 7: What is Conditional Probability? 04:39
    • 8: Conditional Probability & Statistical Independence 02:32
    • 9: Multiplication Theorem 02:05
    • 10: Simpson's Paradox 03:37
    • 11: Conditional Probability in Python 05:16
    • 12: Bayes' Theorem 09:01
    • 13: Bayes Theorem in Python 03:15
    • 14: Quiz. Probability 10:00

    • 15: Random Variable 02:23
    • 16: Discrete Probability Distribution 02:13
    • 17: Continuous Probability Distribution 01:52
    • 18: Probability Density Function 03:43
    • 19: Cumulative Distribution Function 03:28
    • 20: Expected Value of Random Variables 10:03
    • 21: Variance of Random Variables 05:39
    • 22: Find Variance from Expected Value 01:46
    • 23: Additivity of Variance 04:34
    • 24: Quiz. Random Variable & Probability Distribution 04:00

    • 25: Normal Distribution 04:20
    • 26: Standard Normal Distribution 01:55
    • 27: Standard Normal Distribution Table 06:00
    • 28: Skewness & Kurtosis 03:00
    • 29: Normal Distribution with Python 01:35
    • 30: Binomial Distribution 06:27
    • 31: Expected Value f Binomial Distribution 06:00
    • 32: Variance of Binomial Distribution 04:50
    • 33: Binomial Distribution with Python 04:01
    • 34: Poisson Distribution 08:08
    • 35: Expected Value of Poisson Distribution 02:31
    • 36: Variance of Poisson Distribution 03:48
    • 37: Examples of Poisson Distribution 02:54
    • 38: Poisson Distribution with Python 03:25
    • 39: Normal, Binomial, & Poisson Distribution 11:00

    • 40: Geometric Distribution 03:21
    • 41: Expected Value of Geometric Distribution 03:25
    • 42: Variance of Geometric Distribution 04:16
    • 43: Geometric Distribution with Python 03:20
    • 44: Exponential Distribution 03:42
    • 45: Expected Value of Exponential Distribution 02:12
    • 46: Variance of Exponential Distribution 01:57
    • 47: Memorylessness 04:28
    • 48: Exponential Distribution with Python 02:43
    • 49: Discrete Uniform Distribution 03:56
    • 50: Continuous Uniform Distribution 04:59
    • 51: Uniform Distribution with Python 01:16
    • 52: Joint Probability Distribution 03:40
    • 53: Quiz. Geometric, Exponential, & Uniform Distribution 10:00

    • 54: Population & Sample 05:15
    • 55: Complete Survey & Sampling Survey 03:25
    • 56: Probability Sampling & Non-probability Sampling 04:27
    • 57: Probability Sampling Methods 05:33
    • 58: Random Sampling with Python 02:36
    • 59: Law of Large Numbers 02:38
    • 60: Law of Large Numbers with Python 02:48
    • 61: Central Limit Theorem 04:46
    • 62: Central Limit Theorem with Python 01:22
    • 63: Experimental & Observational Studies 07:29
    • 64: Fisher's Principle 03:15
    • 65: Sampling 08:00

Course media

Description

This course is a comprehensive program for learning the basics of statistics. Here, the basic means undergraduate level.

This course is the first chapter of the whole program. The whole program consists of the following three chapters.

Part 1 (Published, Not included in this course)

  1. What is Statistics?
  2. Representative Value
  3. Variability & Relative Position
  4. Data Visualization
  5. Permutation, Combination, & Set

Part 2 (This course)

  1. Probability Theory
  2. Random Variables & Distribution
  3. Probability Distribution Part 1
  4. Probability Distribution Part 2
  5. Sampling

Part 3 (Not included in this course)

  1. Estimation
  2. Hypothesis Testing
  3. Correlation & Regression
  4. Multiple Regression
  5. ANOVA

These chapters and modules cover theory and basic Python coding. Even if you do not have Python coding experience, I believe they are easy to follow for you. But this program is not a Python course, so how to install Python and construct environment is not covered in this course.

This course is designed for beginners, but by completing all three chapters, you will master undergraduate level statistics.

I’m looking forward to seeing you in this course!

Who is this course for?

Anyone who want to start learning statistics.

Anyone who is not a beginner but wants to go over from the basics is also welcome.

Requirements

None

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Certificates

Reed courses certificate of completion

Digital certificate - Included

Will be downloadable when all lectures have been completed

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Legal information

This course is advertised on reed.co.uk by the Course Provider, whose terms and conditions apply. Purchases are made directly from the Course Provider, and as such, content and materials are supplied by the Course Provider directly. Reed is acting as agent and not reseller in relation to this course. Reed's only responsibility is to facilitate your payment for the course. It is your responsibility to review and agree to the Course Provider's terms and conditions and satisfy yourself as to the suitability of the course you intend to purchase. Reed will not have any responsibility for the content of the course and/or associated materials.

FAQs

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