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The Power of Statistics
  • Level Expert
  • Duration 37 hours
  • Course by Google
  • Offered by

About

This is the fourth of seven courses in the Google Advanced Data Analytics Certificate. In this course, you’ll discover how data professionals use statistics to analyze data and gain important insights. You'll explore key concepts such as descriptive and inferential statistics, probability, sampling, confidence intervals, and hypothesis testing. You'll also learn how to use Python for statistical analysis and practice communicating your findings like a data professional. Google employees who currently work in the field will guide you through this course by providing hands-on activities that simulate relevant tasks, sharing examples from their day-to-day work, and helping you enhance your data analytics skills to prepare for your career. Learners who complete the seven courses in this program will have the skills needed to apply for data science and advanced data analytics jobs. This certificate assumes prior knowledge of foundational analytical principles, skills, and tools covered in the Google Data Analytics Certificate. By the end of this course, you will: -Describe the use of statistics in data science -Use descriptive statistics to summarize and explore data -Calculate probability using basic rules -Model data with probability distributions -Describe the applications of different sampling methods -Calculate sampling distributions -Construct and interpret confidence intervals -Conduct hypothesis tests

Modules

Get started with the course

    • 6 Videos
    • 2 Readings
    • 1 Quiz
Show info about module content

6 Videos

  • Introduction to Course 4
  • Evan: Engage and connect
  • Welcome to module 1
  • The role of statistics in data science
  • Statistics in action: A/B testing
  • Descriptive statistics versus inferential statistics

2 Readings

  • Helpful resources and tips
  • Course 4 overview

1 Quiz

  • Test your knowledge: The role of statistics in data science

Descriptive statistics

    • 4 Videos
    • 3 Readings
    • 1 Quiz
    • 1 Plugin
Show info about module content

4 Videos

  • Measures of central tendency
  • Measures of dispersion
  • Measures of position
  • Alok: Statistics as the foundation of data-driven solutions

3 Readings

  • Measures of central tendency: The mean, the median, and the mode
  • Measures of dispersion: Range, variance, and standard deviation
  • Measures of position: Percentiles and quartiles

1 Quiz

  • Test your knowledge: Descriptive statistics

Calculate statistics with Python

    • 1 Videos
    • 1 Quiz
    • 3 Lab
Show info about module content

3 Labs

  • Annotated follow-along guide: Compute descriptive statistics with Python
  • Activity: Explore descriptive statistics
  • Exemplar: Explore descriptive statistics

1 Videos

  • Compute descriptive statistics with Python

1 Quiz

  • Test your knowledge: Calculate statistics with Python

Review: Introduction to statistics

    • 1 Videos
    • 1 Readings
    • 1 Quiz
Show info about module content

1 Videos

  • Wrap-up

1 Readings

  • Glossary terms from module 1

1 Quiz

  • Module 1 challenge

Basic concepts of probability

    • 4 Videos
    • 2 Readings
    • 1 Quiz
Show info about module content

4 Videos

  • Welcome to module 2
  • Objective versus subjective probability
  • The principles of probability
  • The basic rules of probability and events

2 Readings

  • Fundamental concepts of probability
  • The probability of multiple events

1 Quiz

  • Test your knowledge: Basic concepts of probability

Conditional probability

    • 3 Videos
    • 2 Readings
    • 1 Quiz
    • 1 Plugin
Show info about module content

3 Videos

  • Conditional probability
  • Discover Bayes' theorem
  • The expanded version of Bayes’s theorem

2 Readings

  • Calculate conditional probability for dependent events
  • Calculate conditional probability with Bayes's theorem

1 Quiz

  • Test your knowledge: Conditional probability

Discrete probability distributions

    • 3 Videos
    • 1 Readings
    • 1 Quiz
Show info about module content

3 Videos

  • Introduction to probability distributions
  • The binomial distribution
  • The Poisson distribution

1 Readings

  • Discrete probability distributions

1 Quiz

  • Test your knowledge: Discrete probability distributions

Continuous probability distributions

    • 2 Videos
    • 1 Readings
    • 1 Quiz
    • 1 Plugin
Show info about module content

2 Videos

  • The normal distribution
  • Standardize data using z-scores

1 Readings

  • Model data with the normal distribution

1 Quiz

  • Test your knowledge: Continuous probability distributions

Probability distributions with Python

    • 1 Videos
    • 1 Quiz
    • 3 Lab
Show info about module content

3 Labs

  • Annotated follow-along guide: Work with probability distributions in Python
  • Activity: Explore probability distributions
  • Exemplar: Explore probability distributions

1 Videos

  • Work with probability distributions in Python

1 Quiz

  • Test your knowledge: Probability distributions with Python

Review: Probability

    • 1 Videos
    • 1 Readings
    • 1 Quiz
Show info about module content

1 Videos

  • Wrap-up

1 Readings

  • Glossary terms from module 2

1 Quiz

  • Module 2 challenge

Introduction to sampling

    • 6 Videos
    • 4 Readings
    • 1 Quiz
    • 1 Plugin
Show info about module content

6 Videos

  • Welcome to module 3
  • Cliff: Value everyone's contributions
  • Introduction to sampling
  • The sampling process
  • Compare sampling methods
  • The impact of bias in sampling

4 Readings

  • The relationship between sample and population
  • The stages of the sampling process
  • Probability sampling methods
  • Non-probability sampling methods

1 Quiz

  • Test your knowledge: Introduction to sampling

Sampling distributions

    • 3 Videos
    • 2 Readings
    • 1 Quiz
Show info about module content

3 Videos

  • How sampling affects your data
  • The central limit theorem
  • The sampling distribution of the proportion

2 Readings

  • Infer population parameters with the central limit theorem
  • The sampling distribution of the mean

1 Quiz

  • Test your knowledge: Sampling distributions

Work with sampling distributions in Python

    • 1 Videos
    • 1 Quiz
    • 3 Lab
Show info about module content

3 Labs

  • Annotated follow-along guide: Sampling distributions with Python
  • Activity: Explore sampling
  • Exemplar: Explore sampling

1 Videos

  • Sampling distributions with Python

1 Quiz

  • Test your knowledge: Work with sampling distributions in Python

Review: Sampling

    • 1 Videos
    • 1 Readings
    • 1 Quiz
Show info about module content

1 Videos

  • Wrap-up

1 Readings

  • Glossary terms from module 3

1 Quiz

  • Module 3 challenge

Introduction to confidence intervals

    • 3 Videos
    • 1 Readings
    • 1 Quiz
Show info about module content

3 Videos

  • Welcome to module 4
  • Introduction to confidence intervals
  • Interpret confidence intervals

1 Readings

  • Confidence intervals: Correct and incorrect interpretations

1 Quiz

  • Test your knowledge: Introduction to confidence Intervals

Construct confidence intervals

    • 2 Videos
    • 1 Readings
    • 1 Quiz
Show info about module content

2 Videos

  • Construct a confidence interval for a proportion
  • Construct a confidence interval for a mean

1 Readings

  • Construct a confidence interval for a small sample size

1 Quiz

  • Test your knowledge: Construct confidence intervals

Work with confidence intervals in Python

    • 1 Videos
    • 1 Quiz
    • 3 Lab
Show info about module content

3 Labs

  • Annotated follow-along guide: Confidence intervals with Python
  • Activity: Explore confidence intervals
  • Exemplar: Explore confidence intervals

1 Videos

  • Confidence intervals with Python

1 Quiz

  • Test your knowledge: Work with confidence intervals in Python

Review: Confidence intervals

    • 1 Videos
    • 1 Readings
    • 1 Quiz
Show info about module content

1 Videos

  • Wrap-up

1 Readings

  • Glossary terms from module 4

1 Quiz

  • Module 4 challenge

Hypothesis testing

    • 3 Videos
    • 2 Readings
    • 1 Quiz
Show info about module content

3 Videos

  • Welcome to module 5
  • Elea: Keep learning in the ever-changing data space
  • Introduction to hypothesis testing

2 Readings

  • Differences between the null and alternative hypotheses
  • Type I and type II errors

1 Quiz

  • Test your knowledge: Introduction to hypothesis testing

One-sample tests

    • 1 Videos
    • 1 Readings
    • 1 Quiz
Show info about module content

1 Videos

  • One-sample test for means

1 Readings

  • Determine if data has statistical significance

1 Quiz

  • Test your knowledge: One-sample tests

Two-sample tests

    • 2 Videos
    • 4 Readings
    • 1 Quiz
Show info about module content

2 Videos

  • Two-sample tests: Means
  • Two-sample tests: Proportions

4 Readings

  • One-tailed and two-tailed tests
  • A/B testing
  • Experimental Design
  • Case study: Ipsos: How a market research company used A/B testing to help advertisers create more effective ads

1 Quiz

  • Test your knowledge: Two-sample tests

Hypothesis testing with Python

    • 1 Videos
    • 1 Quiz
    • 3 Lab
Show info about module content

3 Labs

  • Annotated follow-along guide: Use Python to conduct a hypothesis test
  • Activity: Explore hypothesis testing
  • Exemplar: Explore hypothesis testing

1 Videos

  • Use Python to conduct a hypothesis test

1 Quiz

  • Test your knowledge: Hypothesis testing with Python

Review: Introduction to hypothesis testing

    • 1 Videos
    • 1 Readings
    • 1 Quiz
Show info about module content

1 Videos

  • Wrap-up

1 Readings

  • Glossary terms from module 5

1 Quiz

  • Module 5 challenge

Apply your skills to a workplace scenario

    • 3 Videos
    • 1 Readings
Show info about module content

3 Videos

  • Welcome to module 6
  • Sean: Showcase your talents to potential employers
  • Introduction to your Course 4 end-of-course portfolio project

1 Readings

  • Explore your Course 4 workplace scenarios

Automatidata scenario

    • 2 Readings
    • 1 Quiz
    • 2 Lab
Show info about module content

2 Labs

  • Activity: Course 4 Automatidata project lab
  • Exemplar: Course 4 Automatidata project lab

2 Readings

  • Course 4 end-of-course portfolio project overview: Automatidata
  • Activity Exemplar: Create your Course 4 Automatidata project

1 Quiz

  • Activity: Create your Course 4 Automatidata project

TikTok scenario

    • 2 Readings
    • 1 Quiz
    • 2 Lab
Show info about module content

2 Labs

  • Activity: Course 4 TikTok project lab
  • Exemplar: Course 4 TikTok project lab

2 Readings

  • Course 4 end-of-course portfolio project overview: TikTok
  • Activity Exemplar: Create your Course 4 TikTok project

1 Quiz

  • Activity: Create your Course 4 TikTok project

Waze scenario

    • 2 Readings
    • 1 Quiz
    • 2 Lab
Show info about module content

2 Labs

  • Activity: Course 4 Waze project lab
  • Exemplar: Course 4 Waze project lab

2 Readings

  • Course 4 end-of-course portfolio project overview: Waze
  • Activity Exemplar: Create your Course 4 Waze project

1 Quiz

  • Activity: Create your Course 4 Waze project

End-of-course portfolio project wrap-up

    • 1 Videos
    • 1 Readings
    • 1 Quiz
Show info about module content

1 Videos

  • End-of-course project wrap-up and tips for ongoing career success

1 Readings

  • Course 4 glossary

1 Quiz

  • Assess your Course 4 end-of-course project

Course review: The Power of Statistics

    • 1 Videos
    • 2 Readings
Show info about module content

1 Videos

  • Course wrap-up

2 Readings

  • Reflect and connect with peers
  • Get started on the next course

Auto Summary

"The Power of Statistics" is an expert-level course in the Google Advanced Data Analytics Certificate series, offered by Coursera. Guided by Google professionals, it delves into descriptive and inferential statistics, probability, sampling, and hypothesis testing, with practical Python applications. Suitable for those with foundational analytics knowledge, this course spans 2220 hours and is available via Starter and Professional subscriptions, preparing learners for advanced data science roles.

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