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
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The Power of Statistics
本课程是 Google Advanced Data Analytics 专业证书 的一部分
位教师：Google Career Certificates
93,751 人已注册
包含在 中
您将学到什么
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
您将获得的技能
- 类别：Data ScienceData Science
- 类别：Statistical ProgrammingStatistical Programming
- 类别：Sampling (Statistics)Sampling (Statistics)
- 类别：Statistical AnalysisStatistical Analysis
- 类别：Statistical InferenceStatistical Inference
- 类别：Statistical MethodsStatistical Methods
- 类别：Probability & StatisticsProbability & Statistics
- 类别：ProbabilityProbability
- 类别：Probability DistributionProbability Distribution
- 类别：AnalyticsAnalytics
- 类别：Advanced AnalyticsAdvanced Analytics
- 类别：Descriptive StatisticsDescriptive Statistics
- 类别：StatisticsStatistics
- 类别：Statistical Hypothesis TestingStatistical Hypothesis Testing
- 类别：Data AnalysisData Analysis
- 类别：A/B TestingA/B Testing
您将学习的工具
- 类别：Python ProgrammingPython Programming
要了解的详细信息
可分享的证书
添加到您的领英档案
作业
27 项作业
授课语言：英语（English）
91% of learners achieved a positive career outcome
积累 Probability and Statistics 领域的专业知识
本课程是 Google Advanced Data Analytics 专业证书 专项课程的一部分
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该课程共有6个模块
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.
涵盖的内容
12个视频6篇阅读材料4个作业3个非评分实验室2个插件
12个视频•总计55分钟
- Introduction to Course 3•5分钟
- Evan: Engage and connect•2分钟
- Welcome to module 1•1分钟
- The role of statistics in data science•4分钟
- Statistics in action: A/B testing•6分钟
- Descriptive statistics versus inferential statistics•5分钟
- Measures of central tendency•5分钟
- Measures of dispersion•6分钟
- Measures of position•7分钟
- Alok: Statistics as the foundation of data-driven solutions•2分钟
- Compute descriptive statistics with Python•10分钟
- Wrap-up•1分钟
6篇阅读材料•总计44分钟
- Helpful resources and tips•8分钟
- Course 3 overview•8分钟
- Measures of central tendency: The mean, the median, and the mode •8分钟
- Measures of dispersion: Range, variance, and standard deviation •8分钟
- Measures of position: Percentiles and quartiles•8分钟
- Glossary terms from module 1•4分钟
4个作业•总计66分钟
- Module 1 challenge•50分钟
- Test your knowledge: The role of statistics in data science•6分钟
- Test your knowledge: Descriptive statistics •6分钟
- Test your knowledge: Calculate statistics with Python•4分钟
3个非评分实验室•总计100分钟
- Annotated follow-along guide: Compute descriptive statistics with Python•20分钟
- Activity: Explore descriptive statistics•60分钟
- Exemplar: Explore descriptive statistics•20分钟
2个插件•总计20分钟
- Connect: Descriptive statistics•10分钟
- [Turkish learners ONLY] Connect: Descriptive statistics - Türkçe•10分钟
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.
涵盖的内容
14个视频7篇阅读材料6个作业3个非评分实验室4个插件
14个视频•总计81分钟
- Welcome to module 2•2分钟
- Objective versus subjective probability•5分钟
- The principles of probability•5分钟
- The basic rules of probability and events•6分钟
- Conditional probability•6分钟
- Discover Bayes' theorem•5分钟
- The expanded version of Bayes’s theorem•6分钟
- Introduction to probability distributions •6分钟
- The binomial distribution•6分钟
- The Poisson distribution•6分钟
- The normal distribution•9分钟
- Standardize data using z-scores•5分钟
- Work with probability distributions in Python•10分钟
- Wrap-up •2分钟
7篇阅读材料•总计56分钟
- Fundamental concepts of probability•8分钟
- The probability of multiple events•8分钟
- Calculate conditional probability for dependent events•8分钟
- Calculate conditional probability with Bayes's theorem•8分钟
- Discrete probability distributions•8分钟
- Model data with the normal distribution•8分钟
- Glossary terms from module 2•8分钟
6个作业•总计76分钟
- Module 2 challenge•50分钟
- Test your knowledge: Basic concepts of probability•6分钟
- Test your knowledge: Conditional probability•6分钟
- Test your knowledge: Discrete probability distributions•4分钟
- Test your knowledge: Continuous probability distributions •6分钟
- Test your knowledge: Probability distributions with Python•4分钟
3个非评分实验室•总计100分钟
- Annotated follow-along guide: Work with probability distributions in Python•20分钟
- Activity: Explore probability distributions•60分钟
- Exemplar: Explore probability distributions•20分钟
4个插件•总计40分钟
- Connect: Basic concepts of probability•10分钟
- [Turkish learners ONLY] Connect: Basic concepts of probability - Türkçe•10分钟
- Categorize: Probability distributions•10分钟
- [Turkish learners ONLY] Categorize: Probability distributions - Türkçe•10分钟
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.
涵盖的内容
11个视频7篇阅读材料4个作业3个非评分实验室2个插件
11个视频•总计60分钟
- Welcome to module 3 •3分钟
- Cliff: Value everyone's contributions•3分钟
- Introduction to sampling •5分钟
- The sampling process•6分钟
- Compare sampling methods •6分钟
- The impact of bias in sampling•6分钟
- How sampling affects your data •9分钟
- The central limit theorem •5分钟
- The sampling distribution of the proportion•6分钟
- Sampling distributions with Python •11分钟
- Wrap-up •2分钟
7篇阅读材料•总计44分钟
- The relationship between sample and population•8分钟
- The stages of the sampling process •8分钟
- Probability sampling methods•8分钟
- Non-probability sampling methods•8分钟
- Infer population parameters with the central limit theorem •4分钟
- The sampling distribution of the mean•4分钟
- Glossary terms from module 3 •4分钟
4个作业•总计66分钟
- Module 3 challenge•50分钟
- Test your knowledge: Introduction to sampling•6分钟
- Test your knowledge: Sampling distributions•6分钟
- Test your knowledge: Work with sampling distributions in Python•4分钟
3个非评分实验室•总计100分钟
- Annotated follow-along guide: Sampling distributions with Python•20分钟
- Activity: Explore sampling•60分钟
- Exemplar: Explore sampling•20分钟
2个插件•总计20分钟
- Identify: Sampling methods•10分钟
- [Turkish learners ONLY] Identify: Sampling methods - Türkçe•10分钟
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.
涵盖的内容
7个视频3篇阅读材料4个作业3个非评分实验室
7个视频•总计42分钟
- Welcome to module 4•4分钟
- Introduction to confidence intervals•6分钟
- Interpret confidence intervals•8分钟
- Construct a confidence interval for a proportion•7分钟
- Construct a confidence interval for a mean•7分钟
- Confidence intervals with Python•8分钟
- Wrap-up•3分钟
3篇阅读材料•总计20分钟
- Confidence intervals: Correct and incorrect interpretations •8分钟
- Construct a confidence interval for a small sample size•8分钟
- Glossary terms from module 4•4分钟
4个作业•总计66分钟
- Module 4 challenge •50分钟
- Test your knowledge: Introduction to confidence Intervals•6分钟
- Test your knowledge: Construct confidence intervals•6分钟
- Test your knowledge: Work with confidence intervals in Python•4分钟
3个非评分实验室•总计100分钟
- Annotated follow-along guide: Confidence intervals with Python•20分钟
- Activity: Explore confidence intervals•60分钟
- Exemplar: Explore confidence intervals•20分钟
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.
涵盖的内容
8个视频8篇阅读材料5个作业3个非评分实验室
8个视频•总计55分钟
- Welcome to module 5 •3分钟
- Elea: Keep learning in the ever-changing data space•3分钟
- Introduction to hypothesis testing •11分钟
- One-sample test for means•9分钟
- Two-sample tests: Means•10分钟
- Two-sample tests: Proportions•7分钟
- Use Python to conduct a hypothesis test •10分钟
- Wrap-up •2分钟
8篇阅读材料•总计56分钟
- Differences between the null and alternative hypotheses•8分钟
- Type I and type II errors •8分钟
- Determine if data has statistical significance•8分钟
- One-tailed and two-tailed tests•8分钟
- A/B testing •8分钟
- Experimental Design•4分钟
- Case study: Ipsos: How a market research company used A/B testing to help advertisers create more effective ads •8分钟
- Glossary terms from module 5 •4分钟
5个作业•总计70分钟
- Module 5 challenge •50分钟
- Test your knowledge: Introduction to hypothesis testing•8分钟
- Test your knowledge: One-sample tests•4分钟
- Test your knowledge: Two-sample tests•4分钟
- Test your knowledge: Hypothesis testing with Python•4分钟
3个非评分实验室•总计100分钟
- Annotated follow-along guide: Use Python to conduct a hypothesis test•20分钟
- Activity: Explore hypothesis testing•60分钟
- Exemplar: Explore hypothesis testing•20分钟
In this end-of-course project, you’ll use statistical methods such as hypothesis testing to analyze a workplace scenario dataset.
涵盖的内容
5个视频10篇阅读材料4个作业6个非评分实验室
5个视频•总计11分钟
- Welcome to module 6 •2分钟
- Sean: Showcase your talents to potential employers•2分钟
- Introduction to your Course 3 end-of-course portfolio project•2分钟
- End-of-course project wrap-up and tips for ongoing career success•3分钟
- Course wrap-up •2分钟
10篇阅读材料•总计52分钟
- Explore your Course 3 workplace scenarios•8分钟
- Course 3 end-of-course portfolio project overview: Automatidata•8分钟
- Activity exemplar: Create your Course 3 Automatidata project•4分钟
- Course 3 end-of-course portfolio project overview: TikTok•8分钟
- Activity Exemplar: Create your Course 3 TikTok project•4分钟
- Course 3 end-of-course portfolio project overview: Waze•8分钟
- Activity Exemplar: Create your Course 3 Waze project•4分钟
- Course 3 glossary•2分钟
- Reflect and connect with peers•2分钟
- Get started on the next course•4分钟
4个作业•总计130分钟
- Assess your Course 3 end-of-course project•40分钟
- Activity: Create your Course 3 Automatidata project •30分钟
- Activity: Create your Course 3 TikTok project •30分钟
- Activity: Create your Course 3 Waze project •30分钟
6个非评分实验室•总计240分钟
- Activity: Course 3 Automatidata project lab•60分钟
- Exemplar: Course 3 Automatidata project lab•20分钟
- Activity: Course 3 TikTok project lab•60分钟
- Exemplar: Course 3 TikTok project lab•20分钟
- Activity: Course 3 Waze project lab•60分钟
- Exemplar: Course 3 Waze project lab•20分钟
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自 2020开始学习的学生
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学生评论
4.8
894 条评论
- 5 stars
87.13%
- 4 stars
10.17%
- 3 stars
1.45%
- 2 stars
0.55%
- 1 star
0.67%
显示 3/894 个
B
BT
5·
已于 Apr 12, 2023审阅
it's very good course ever, and extreme defficult for me as well. thank you Google and Coursera team for producing a good course like this
T
TP
5·
已于 Sep 19, 2023审阅
Exceptional! I've learned so much about statistics with such a clarity, and how they are being practiced in real life. Thank you, instructor!
D
DA
5·
已于 Dec 13, 2024审阅
It was quite a technical course and got harder along the way. However the course content made catching up with the technical courses highlighted in this course easier.
常见问题
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.