# The Power of Statistics

- Offered byCoursera

## The Power of Statistics at Coursera Overview

Duration | 33 hours |

Start from | Start Now |

Total fee | Free |

Mode of learning | Online |

Difficulty level | Advanced |

Official Website | Explore Free Course |

Credential | Certificate |

## The Power of Statistics at Coursera Highlights

- Flexible deadlines Reset deadlines in accordance to your schedule.
- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Coursera Labs Includes hands on learning projects. Learn more about Coursera Labs External Link
- Advanced Level
- Approx. 33 hours to complete
- English Subtitles: English

## The Power of Statistics at Coursera Course details

- 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

## The Power of Statistics at Coursera Curriculum

**Introduction to statistics**

Introduction to Course 4

Welcome to week 1

The role of statistics in data science

Statistics in action: A/B testing

Descriptive statistics versus inferential statistics

Measures of central tendency

Measures of dispersion

Measures of position

Alok: Statistics as the foundation of data-driven solutions

Compute descriptive statistics with Python

Evan: Engage and connect

Wrap-up

Helpful resources and tips

Course 4 overview

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

Follow-along instructions: Introduction to statistics with Python

Glossary terms from week 1

Test your knowledge: The role of statistics in data science

Test your knowledge: Descriptive statistics

Test your knowledge: Calculate statistics with Python

Weekly challenge 1

**Probability**

Welcome to week 2

Objective versus subjective probability

The principles of probability

The basic rules of probability and events

Conditional probability

Discover Bayes' theorem

The expanded version of Bayes’s theorem

Introduction to probability distributions

The binomial distribution

The Poisson distribution

The normal distribution

Standardize data using z-scores

Work with probability distributions in Python

Wrap-up

Fundamental concepts of probability

The probability of multiple events

Calculate conditional probability for dependent events

Calculate conditional probability with Bayes's theorem

Discrete probability distributions

Model data with the normal distribution

Follow-along instructions: Basic concepts of probability with Python

Glossary terms from week 2

Test your knowledge: Basic concepts of probability

Test your knowledge: Conditional probability

Test your knowledge: Discrete probability distributions

Test your knowledge: Continuous probability distributions

Test your knowledge: Probability distributions with Python

Weekly challenge 2

**Sampling**

Welcome to week 3

Introduction to sampling

The sampling process

Compare sampling methods

The impact of bias in sampling

How sampling affects your data

The central limit theorem

The sampling distribution of the proportion

Sampling distributions with Python

Wrap-up

The relationship between sample and population

The stages of the sampling process

Probability sampling methods

Non-probability sampling methods

Infer population parameters with the central limit theorem

The sampling distribution of the mean

Follow-along instructions: Sampling distributions with Python

Glossary terms from week 3

Test your knowledge: Introduction to sampling

Test your knowledge: Sampling distributions

Test your knowledge: Work with sampling distributions in Python

Weekly challenge 3

**Confidence intervals**

Welcome to week 4

Introduction to confidence intervals

Interpret confidence intervals

Construct a confidence interval for a proportion

Construct a confidence interval for a mean

Confidence intervals with Python

Wrap-up

Confidence intervals: Correct and incorrect interpretations

Construct a confidence interval for a small sample size

Follow-along instructions: Confidence intervals with Python

Glossary terms from week 4

Test your knowledge: Introduction to confidence Intervals

Test your knowledge: Construct confidence intervals

Test your knowledge: Work with confidence intervals in Python

Weekly challenge 4

**Introduction to hypothesis testing**

Welcome to week 5

Elea: Keep learning in the ever-changing data space

Introduction to hypothesis testing

One-sample test for means

Two-sample tests: Means

Two-sample tests: Proportions

Use Python to conduct a hypothesis test

Wrap-up

Differences between the null and alternative hypotheses

Type I and type II errors

Determine if data has statistical significance

One-tailed and two-tailed tests

A/B testing with Python

Experimental Design

Case study: Ipsos: How a market research company used A/B testing to help advertisers create more effective ads

Follow-along instructions: Use Python to conduct a hypothesis test

Glossary terms from week 5

Test your knowledge: Introduction to hypothesis testing

Test your knowledge: One-sample tests

Test your knowledge: Two-sample tests

Test your knowledge: Hypothesis testing with Python

Weekly challenge 5

**Course 4 end-of-course project**

Welcome to week 6

Sean: Showcase your talents to potential employers

Introduction to Course 4 end-of-course portfolio project

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

Course wrap-up

Course 4 end-of-course portfolio project overview: Automatidata

Activity Exemplar: Create your Course 4 Automatidata project

Course 4 glossary

Get started on the next course

Activity: Create your Course 4 Automatidata project

Assess your Course 4 end-of-course project

## The Power of Statistics at Coursera Admission Process

## Important Dates

## Other courses offered by Coursera

### Student Forum

**Anything you would want to ask experts?**