The Power of Statistics

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

What you will learn

Introduction to statistics

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.

Probability

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.

Sampling

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.

Confidence intervals

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.