The Power of Statistics
- Offered byCoursera
The Power of Statistics at Coursera Overview
Duration | 33 hours |
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
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