This is the third course 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.

The Power of Statistics

The Power of Statistics
This course is part of Google Advanced Data Analytics Professional Certificate

Instructor: Google Career Certificates
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What you'll learn
Explore and summarize a dataset
Use probability distributions to model data
Conduct a hypothesis test to identify insights about data
Perform statistical analyses using Python
Skills you'll gain
Tools you'll learn
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Build your Probability and Statistics expertise
- Learn new concepts from industry experts
- Gain a foundational understanding of a subject or tool
- Develop job-relevant skills with hands-on projects
- Earn a shareable career certificate from Google

There are 6 modules in this course
You’ll explore the role of statistics in data science and identify the difference between descriptive and inferential statistics. You’ll learn how descriptive statistics can help you quickly summarize a dataset and measure the center, spread, and relative position of data.
What's included
12 videos6 readings4 assignments3 ungraded labs2 plugins
12 videos•Total 55 minutes
- Introduction to Course 3•5 minutes
- Evan: Engage and connect•2 minutes
- Welcome to module 1•1 minute
- The role of statistics in data science•4 minutes
- Statistics in action: A/B testing•6 minutes
- Descriptive statistics versus inferential statistics•5 minutes
- Measures of central tendency•5 minutes
- Measures of dispersion•6 minutes
- Measures of position•7 minutes
- Alok: Statistics as the foundation of data-driven solutions•2 minutes
- Compute descriptive statistics with Python•10 minutes
- Wrap-up•1 minute
6 readings•Total 44 minutes
- Helpful resources and tips•8 minutes
- Course 3 overview•8 minutes
- Measures of central tendency: The mean, the median, and the mode •8 minutes
- Measures of dispersion: Range, variance, and standard deviation •8 minutes
- Measures of position: Percentiles and quartiles•8 minutes
- Glossary terms from module 1•4 minutes
4 assignments•Total 66 minutes
- Test your knowledge: The role of statistics in data science•6 minutes
- Test your knowledge: Descriptive statistics •6 minutes
- Test your knowledge: Calculate statistics with Python•4 minutes
- Module 1 challenge•50 minutes
3 ungraded labs•Total 100 minutes
- Annotated follow-along guide: Compute descriptive statistics with Python•20 minutes
- Activity: Explore descriptive statistics•60 minutes
- Exemplar: Explore descriptive statistics•20 minutes
2 plugins•Total 20 minutes
- Connect: Descriptive statistics•10 minutes
- [Turkish learners ONLY] Connect: Descriptive statistics - Türkçe•10 minutes
You’ll learn the basic rules for calculating probability for single events. Next, you’ll discover how data professionals use methods such as Bayes’ theorem to describe more complex events. Finally, you’ll learn how probability distributions such as the binomial, Poisson, and normal distribution can help you better understand the structure of data.
What's included
14 videos7 readings6 assignments3 ungraded labs4 plugins
14 videos•Total 81 minutes
- Welcome to module 2•2 minutes
- Objective versus subjective probability•5 minutes
- The principles of probability•5 minutes
- The basic rules of probability and events•6 minutes
- Conditional probability•6 minutes
- Discover Bayes' theorem•5 minutes
- The expanded version of Bayes’s theorem•6 minutes
- Introduction to probability distributions •6 minutes
- The binomial distribution•6 minutes
- The Poisson distribution•6 minutes
- The normal distribution•9 minutes
- Standardize data using z-scores•5 minutes
- Work with probability distributions in Python•10 minutes
- Wrap-up •2 minutes
7 readings•Total 56 minutes
- Fundamental concepts of probability•8 minutes
- The probability of multiple events•8 minutes
- Calculate conditional probability for dependent events•8 minutes
- Calculate conditional probability with Bayes's theorem•8 minutes
- Discrete probability distributions•8 minutes
- Model data with the normal distribution•8 minutes
- Glossary terms from module 2•8 minutes
6 assignments•Total 76 minutes
- Test your knowledge: Basic concepts of probability•6 minutes
- Test your knowledge: Conditional probability•6 minutes
- Test your knowledge: Discrete probability distributions•4 minutes
- Test your knowledge: Continuous probability distributions •6 minutes
- Test your knowledge: Probability distributions with Python•4 minutes
- Module 2 challenge•50 minutes
3 ungraded labs•Total 100 minutes
- Annotated follow-along guide: Work with probability distributions in Python•20 minutes
- Activity: Explore probability distributions•60 minutes
- Exemplar: Explore probability distributions•20 minutes
4 plugins•Total 40 minutes
- Connect: Basic concepts of probability•10 minutes
- [Turkish learners ONLY] Connect: Basic concepts of probability - Türkçe•10 minutes
- Categorize: Probability distributions•10 minutes
- [Turkish learners ONLY] Categorize: Probability distributions - Türkçe•10 minutes
Data professionals use smaller samples of data to draw conclusions about large datasets. You’ll learn about the different methods they use to collect and analyze sample data and how they avoid sampling bias. You’ll also learn how sampling distributions can help you make accurate estimates.
What's included
11 videos7 readings4 assignments3 ungraded labs2 plugins
11 videos•Total 60 minutes
- Welcome to module 3 •3 minutes
- Cliff: Value everyone's contributions•3 minutes
- Introduction to sampling •5 minutes
- The sampling process•6 minutes
- Compare sampling methods •6 minutes
- The impact of bias in sampling•6 minutes
- How sampling affects your data •9 minutes
- The central limit theorem •5 minutes
- The sampling distribution of the proportion•6 minutes
- Sampling distributions with Python •11 minutes
- Wrap-up •2 minutes
7 readings•Total 44 minutes
- The relationship between sample and population•8 minutes
- The stages of the sampling process •8 minutes
- Probability sampling methods•8 minutes
- Non-probability sampling methods•8 minutes
- Infer population parameters with the central limit theorem •4 minutes
- The sampling distribution of the mean•4 minutes
- Glossary terms from module 3 •4 minutes
4 assignments•Total 66 minutes
- Test your knowledge: Introduction to sampling•6 minutes
- Test your knowledge: Sampling distributions•6 minutes
- Test your knowledge: Work with sampling distributions in Python•4 minutes
- Module 3 challenge•50 minutes
3 ungraded labs•Total 100 minutes
- Annotated follow-along guide: Sampling distributions with Python•20 minutes
- Activity: Explore sampling•60 minutes
- Exemplar: Explore sampling•20 minutes
2 plugins•Total 20 minutes
- Identify: Sampling methods•10 minutes
- [Turkish learners ONLY] Identify: Sampling methods - Türkçe•10 minutes
You’ll explore how data professionals use confidence intervals to describe the uncertainty of their estimates. You'll learn how to construct and interpret confidence intervals, and how to avoid some common misinterpretations.
What's included
7 videos3 readings4 assignments3 ungraded labs
7 videos•Total 42 minutes
- Welcome to module 4•4 minutes
- Introduction to confidence intervals•6 minutes
- Interpret confidence intervals•8 minutes
- Construct a confidence interval for a proportion•7 minutes
- Construct a confidence interval for a mean•7 minutes
- Confidence intervals with Python•8 minutes
- Wrap-up•3 minutes
3 readings•Total 20 minutes
- Confidence intervals: Correct and incorrect interpretations •8 minutes
- Construct a confidence interval for a small sample size•8 minutes
- Glossary terms from module 4•4 minutes
4 assignments•Total 66 minutes
- Test your knowledge: Introduction to confidence Intervals•6 minutes
- Test your knowledge: Construct confidence intervals•6 minutes
- Test your knowledge: Work with confidence intervals in Python•4 minutes
- Module 4 challenge •50 minutes
3 ungraded labs•Total 100 minutes
- Annotated follow-along guide: Confidence intervals with Python•20 minutes
- Activity: Explore confidence intervals•60 minutes
- Exemplar: Explore confidence intervals•20 minutes
Hypothesis testing helps data professionals determine if the results of a test or experiment are statistically significant or due to chance. You’ll learn about the basic steps for any hypothesis test and how hypothesis testing can help you draw meaningful conclusions about data.
What's included
8 videos8 readings5 assignments3 ungraded labs
8 videos•Total 55 minutes
- Welcome to module 5 •3 minutes
- Elea: Keep learning in the ever-changing data space•3 minutes
- Introduction to hypothesis testing •11 minutes
- One-sample test for means•9 minutes
- Two-sample tests: Means•10 minutes
- Two-sample tests: Proportions•7 minutes
- Use Python to conduct a hypothesis test •10 minutes
- Wrap-up •2 minutes
8 readings•Total 56 minutes
- Differences between the null and alternative hypotheses•8 minutes
- Type I and type II errors •8 minutes
- Determine if data has statistical significance•8 minutes
- One-tailed and two-tailed tests•8 minutes
- A/B testing •8 minutes
- Experimental Design•4 minutes
- Case study: Ipsos: How a market research company used A/B testing to help advertisers create more effective ads •8 minutes
- Glossary terms from module 5 •4 minutes
5 assignments•Total 70 minutes
- Test your knowledge: Introduction to hypothesis testing•8 minutes
- Test your knowledge: One-sample tests•4 minutes
- Test your knowledge: Two-sample tests•4 minutes
- Test your knowledge: Hypothesis testing with Python•4 minutes
- Module 5 challenge •50 minutes
3 ungraded labs•Total 100 minutes
- Annotated follow-along guide: Use Python to conduct a hypothesis test•20 minutes
- Activity: Explore hypothesis testing•60 minutes
- Exemplar: Explore hypothesis testing•20 minutes
In this end-of-course project, you’ll use statistical methods such as hypothesis testing to analyze a workplace scenario dataset.
What's included
5 videos10 readings4 assignments6 ungraded labs
5 videos•Total 11 minutes
- Welcome to module 6 •2 minutes
- Sean: Showcase your talents to potential employers•2 minutes
- Introduction to your Course 3 end-of-course portfolio project•2 minutes
- End-of-course project wrap-up and tips for ongoing career success•3 minutes
- Course wrap-up •2 minutes
10 readings•Total 52 minutes
- Explore your Course 3 workplace scenarios•8 minutes
- Course 3 end-of-course portfolio project overview: Automatidata•8 minutes
- Activity exemplar: Create your Course 3 Automatidata project•4 minutes
- Course 3 end-of-course portfolio project overview: TikTok•8 minutes
- Activity Exemplar: Create your Course 3 TikTok project•4 minutes
- Course 3 end-of-course portfolio project overview: Waze•8 minutes
- Activity Exemplar: Create your Course 3 Waze project•4 minutes
- Course 3 glossary•2 minutes
- Reflect and connect with peers•2 minutes
- Get started on the next course•4 minutes
4 assignments•Total 130 minutes
- Activity: Create your Course 3 Automatidata project •30 minutes
- Activity: Create your Course 3 TikTok project •30 minutes
- Activity: Create your Course 3 Waze project •30 minutes
- Assess your Course 3 end-of-course project•40 minutes
6 ungraded labs•Total 240 minutes
- Activity: Course 3 Automatidata project lab•60 minutes
- Exemplar: Course 3 Automatidata project lab•20 minutes
- Activity: Course 3 TikTok project lab•60 minutes
- Exemplar: Course 3 TikTok project lab•20 minutes
- Activity: Course 3 Waze project lab•60 minutes
- Exemplar: Course 3 Waze project lab•20 minutes
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Frequently asked questions
Organizations of all types and sizes have business processes that generate massive volumes of data. Every moment, all sorts of information gets created by computers, the internet, phones, texts, streaming video, photographs, sensors, and much more. In the global digital landscape, data is increasingly imprecise, chaotic, and unstructured. As the speed and variety of data increases exponentially, organizations are struggling to keep pace.
Data science and advanced data analytics are part of a field of study that uses raw data to create new ways of modeling and understanding the unknown. To gain insights, businesses rely on data professionals to acquire, organize, and interpret data, which helps inform internal projects and processes. Data scientists and advanced data analysts rely on a combination of critical skills, including statistics, scientific methods, data analysis, and artificial intelligence.
A data professional is a term used to describe any individual who works with data and/or has data skills. At a minimum, a data professional is capable of exploring, cleaning, selecting, analyzing, and visualizing data. They may also be comfortable with writing code and have some familiarity with the techniques used by statisticians and machine learning engineers, including building models, developing algorithmic thinking, and building machine learning models.
Data professionals are responsible for collecting, analyzing, and interpreting large amounts of data within a variety of different organizations. The role of a data professional is defined differently across companies. Generally speaking, data professionals possess technical and strategic capabilities that require more advanced analytical skills such as data manipulation, experimental design, predictive modeling, and machine learning. They perform a variety of tasks related to gathering, structuring, interpreting, monitoring, and reporting data in accessible formats, enabling stakeholders to understand and use data effectively. Ultimately, the work of data professionals helps organizations make informed, ethical decisions.
Large volumes of data — and the technology needed to manage and analyze it — are becoming increasingly accessible. Because of this, there has been a surge in career opportunities for people who can tell stories using data, such as senior data analysts and data scientists. These professionals collect, analyze, and interpret large amounts of data within a variety of different organizations. Their responsibilities require advanced analytical skills such as data manipulation, experimental design, predictive modeling, and machine learning.
The Google Advanced Data Analytics Certificate on Coursera is designed to prepare learners for roles as entry-level data scientists and advanced-level data analysts.
During this certificate program, you’ll gain knowledge of tools and platforms like Jupyter Notebook, Kaggle, Python, Stack Overflow, and Tableau.
This certificate program assumes prior knowledge of foundational analytical principles, skills, and tools. To succeed in this certificate program, you should already know about key foundational aspects of data analysis, such as the data analysis process and data life cycle, databases and general database elements, programming language basics, and project stakeholders.
The content in this certificate program builds upon data analytics concepts taught in the Google Data Analytics Certificate. These include key foundational aspects of data analysis such as the data analysis process and data life cycle, databases and general database elements such as primary and foreign keys, SQL and programming language basics, and project stakeholders. If you haven’t completed that program or if you’re unsure whether you have the necessary prerequisites, you can take an ungraded assessment in Course 1 Module 1 of this certificate to evaluate your readiness.
You’ll learn job-ready skills through interactive content — like activities, quizzes, and discussion prompts — in under six months, with less than 10 hours of flexible study a week. Along the way, you’ll work through a curriculum designed by Google employees who work in the field, with input from top employers and industry leaders. You’ll even have the opportunity to complete end-of-course projects and a final capstone project that you can share with potential employers to showcase your data analysis skills. After you’ve graduated from the program, you’ll have access to career resources and be connected directly with employers hiring for open entry-level roles in data science and advanced roles in data analytics.
We highly recommend completing the seven courses in the order presented because the content in each course builds on information covered in earlier lessons.
To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
When you enroll in the course, you get access to all of the courses in the Certificate, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.
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