What is data science and advanced data analytics?

<text variant="body1">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. <text variant="body1">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.

What do data professionals do?

<text variant="body1">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. <text variant="body1">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.

Why start a career in data science or advanced data analytics?

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.

Which jobs will this certificate help me prepare for?

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.

What tools and platforms are taught in the curriculum?

During this certificate program, you’ll gain knowledge of tools and platforms like Jupyter Notebook, Kaggle, Python, Stack Overflow, and Tableau.

Why enroll in the Google Advanced Data Analytics Certificate?

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.

Do I need to take the course in a certain order?

We highly recommend completing the seven courses in the order presented because the content in each course builds on information covered in earlier lessons.

When will I have access to the lectures and assignments?

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.

What will I get if I subscribe to this Certificate?

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.

The Power of Statistics

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The Power of Statistics

This course is part of Google Advanced Data Analytics Professional Certificate

Instructor: Google Career Certificates

93,648 already enrolled

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6 modules

Gain insight into a topic and learn the fundamentals.

894 reviews

Advanced level

Designed for those already in the industry

Flexible schedule

3 weeks at 10 hours a week

Learn at your own pace

98%

Most learners liked this course

6 modules

Gain insight into a topic and learn the fundamentals.

894 reviews

Advanced level

Designed for those already in the industry

Flexible schedule

3 weeks at 10 hours a week

Learn at your own pace

98%

Most learners liked this course

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

Python Programming

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

27 assignments

Taught in English

91% of learners achieved a positive career outcome

See how employees at top companies are mastering in-demand skills

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Build your Probability and Statistics expertise

This course is part of the Google Advanced Data Analytics Professional Certificate

When you enroll in this course, you'll also be enrolled in this Professional Certificate.

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

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.

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 eight 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

Module details

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 videosTotal 55 minutes

Introduction to Course 35 minutes
Evan: Engage and connect2 minutes
Welcome to module 11 minute
The role of statistics in data science4 minutes
Statistics in action: A/B testing6 minutes
Descriptive statistics versus inferential statistics5 minutes
Measures of central tendency5 minutes
Measures of dispersion6 minutes
Measures of position7 minutes
Alok: Statistics as the foundation of data-driven solutions2 minutes
Compute descriptive statistics with Python10 minutes
Wrap-up1 minute

6 readingsTotal 44 minutes

Helpful resources and tips8 minutes
Course 3 overview8 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 quartiles8 minutes
Glossary terms from module 14 minutes

4 assignmentsTotal 66 minutes

Test your knowledge: The role of statistics in data science6 minutes
Test your knowledge: Descriptive statistics 6 minutes
Test your knowledge: Calculate statistics with Python4 minutes
Module 1 challenge50 minutes

3 ungraded labsTotal 100 minutes

Annotated follow-along guide: Compute descriptive statistics with Python20 minutes
Activity: Explore descriptive statistics60 minutes
Exemplar: Explore descriptive statistics20 minutes

2 pluginsTotal 20 minutes

Connect: Descriptive statistics10 minutes
[Turkish learners ONLY] Connect: Descriptive statistics - Türkçe10 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 videosTotal 81 minutes

Welcome to module 22 minutes
Objective versus subjective probability5 minutes
The principles of probability5 minutes
The basic rules of probability and events6 minutes
Conditional probability6 minutes
Discover Bayes' theorem5 minutes
The expanded version of Bayes’s theorem6 minutes
Introduction to probability distributions 6 minutes
The binomial distribution6 minutes
The Poisson distribution6 minutes
The normal distribution9 minutes
Standardize data using z-scores5 minutes
Work with probability distributions in Python10 minutes
Wrap-up 2 minutes

7 readingsTotal 56 minutes

Fundamental concepts of probability8 minutes
The probability of multiple events8 minutes
Calculate conditional probability for dependent events8 minutes
Calculate conditional probability with Bayes's theorem8 minutes
Discrete probability distributions8 minutes
Model data with the normal distribution8 minutes
Glossary terms from module 28 minutes

6 assignmentsTotal 76 minutes

Test your knowledge: Basic concepts of probability6 minutes
Test your knowledge: Conditional probability6 minutes
Test your knowledge: Discrete probability distributions4 minutes
Test your knowledge: Continuous probability distributions 6 minutes
Test your knowledge: Probability distributions with Python4 minutes
Module 2 challenge50 minutes

3 ungraded labsTotal 100 minutes

Annotated follow-along guide: Work with probability distributions in Python20 minutes
Activity: Explore probability distributions60 minutes
Exemplar: Explore probability distributions20 minutes

4 pluginsTotal 40 minutes

Connect: Basic concepts of probability10 minutes
[Turkish learners ONLY] Connect: Basic concepts of probability - Türkçe10 minutes
Categorize: Probability distributions10 minutes
[Turkish learners ONLY] Categorize: Probability distributions - Türkçe10 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 videosTotal 60 minutes

Welcome to module 3 3 minutes
Cliff: Value everyone's contributions3 minutes
Introduction to sampling 5 minutes
The sampling process6 minutes
Compare sampling methods 6 minutes
The impact of bias in sampling6 minutes
How sampling affects your data 9 minutes
The central limit theorem 5 minutes
The sampling distribution of the proportion6 minutes
Sampling distributions with Python 11 minutes
Wrap-up 2 minutes

7 readingsTotal 44 minutes

The relationship between sample and population8 minutes
The stages of the sampling process 8 minutes
Probability sampling methods8 minutes
Non-probability sampling methods8 minutes
Infer population parameters with the central limit theorem 4 minutes
The sampling distribution of the mean4 minutes
Glossary terms from module 3 4 minutes

4 assignmentsTotal 66 minutes

Test your knowledge: Introduction to sampling6 minutes
Test your knowledge: Sampling distributions6 minutes
Test your knowledge: Work with sampling distributions in Python4 minutes
Module 3 challenge50 minutes

3 ungraded labsTotal 100 minutes

Annotated follow-along guide: Sampling distributions with Python20 minutes
Activity: Explore sampling60 minutes
Exemplar: Explore sampling20 minutes

2 pluginsTotal 20 minutes

Identify: Sampling methods10 minutes
[Turkish learners ONLY] Identify: Sampling methods - Türkçe10 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 videosTotal 42 minutes

Welcome to module 44 minutes
Introduction to confidence intervals6 minutes
Interpret confidence intervals8 minutes
Construct a confidence interval for a proportion7 minutes
Construct a confidence interval for a mean7 minutes
Confidence intervals with Python8 minutes
Wrap-up3 minutes

3 readingsTotal 20 minutes

Confidence intervals: Correct and incorrect interpretations 8 minutes
Construct a confidence interval for a small sample size8 minutes
Glossary terms from module 44 minutes

4 assignmentsTotal 66 minutes

Test your knowledge: Introduction to confidence Intervals6 minutes
Test your knowledge: Construct confidence intervals6 minutes
Test your knowledge: Work with confidence intervals in Python4 minutes
Module 4 challenge 50 minutes

3 ungraded labsTotal 100 minutes

Annotated follow-along guide: Confidence intervals with Python20 minutes
Activity: Explore confidence intervals60 minutes
Exemplar: Explore confidence intervals20 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 videosTotal 55 minutes

Welcome to module 5 3 minutes
Elea: Keep learning in the ever-changing data space3 minutes
Introduction to hypothesis testing 11 minutes
One-sample test for means9 minutes
Two-sample tests: Means10 minutes
Two-sample tests: Proportions7 minutes
Use Python to conduct a hypothesis test 10 minutes
Wrap-up 2 minutes

8 readingsTotal 56 minutes

Differences between the null and alternative hypotheses8 minutes
Type I and type II errors 8 minutes
Determine if data has statistical significance8 minutes
One-tailed and two-tailed tests8 minutes
A/B testing 8 minutes
Experimental Design4 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 assignmentsTotal 70 minutes

Test your knowledge: Introduction to hypothesis testing8 minutes
Test your knowledge: One-sample tests4 minutes
Test your knowledge: Two-sample tests4 minutes
Test your knowledge: Hypothesis testing with Python4 minutes
Module 5 challenge 50 minutes

3 ungraded labsTotal 100 minutes

Annotated follow-along guide: Use Python to conduct a hypothesis test20 minutes
Activity: Explore hypothesis testing60 minutes
Exemplar: Explore hypothesis testing20 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 videosTotal 11 minutes

Welcome to module 6 2 minutes
Sean: Showcase your talents to potential employers2 minutes
Introduction to your Course 3 end-of-course portfolio project2 minutes
End-of-course project wrap-up and tips for ongoing career success3 minutes
Course wrap-up 2 minutes

10 readingsTotal 52 minutes

Explore your Course 3 workplace scenarios8 minutes
Course 3 end-of-course portfolio project overview: Automatidata8 minutes
Activity exemplar: Create your Course 3 Automatidata project4 minutes
Course 3 end-of-course portfolio project overview: TikTok8 minutes
Activity Exemplar: Create your Course 3 TikTok project4 minutes
Course 3 end-of-course portfolio project overview: Waze8 minutes
Activity Exemplar: Create your Course 3 Waze project4 minutes
Course 3 glossary2 minutes
Reflect and connect with peers2 minutes
Get started on the next course4 minutes

4 assignmentsTotal 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 project40 minutes

6 ungraded labsTotal 240 minutes

Activity: Course 3 Automatidata project lab60 minutes
Exemplar: Course 3 Automatidata project lab20 minutes
Activity: Course 3 TikTok project lab60 minutes
Exemplar: Course 3 TikTok project lab20 minutes
Activity: Course 3 Waze project lab60 minutes
Exemplar: Course 3 Waze project lab20 minutes

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Google Career Certificates

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3 stars
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2 stars
0.55%
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Reviewed on Oct 20, 2023

This is but a good introduction to statistics. Not what I expected in an advanced course, but still good for beginners or as a refresh.

Reviewed on Sep 19, 2023

Exceptional! I've learned so much about statistics with such a clarity, and how they are being practiced in real life. Thank you, instructor!

Reviewed on Dec 13, 2024

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.

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