The Power of Statistics

Google via Coursera

Coursera based on 780 ratings

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Overview

This is the fourth of seven courses in the Google Advanced Data Analytics Certificate. In this course, youll 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...

Syllabus

Introduction to statistics

Youll explore the role of statistics in data science and identify the difference between descriptive and inferential statistics. Youll learn how descriptive statistics can you quickly summarize a dataset and measure the center, spread, and relative position of data.

Probability

Youll learn the basic rules for calculating probability for single events. Next, youll discover how data professionals use methods such as Bayes theorem to describe more complex events. Finally, youll learn how probability distributions such as the binomial, Poisson, and normal distribution can you better understand the structure of data.

Sampling

Data professionals use smaller samples of data to draw conclusions about large datasets. Youll learn about the different methods they use to collect and analyze sample data and how they avoid sampling bias. Youll also learn how sampling distributions can you make accurate estimates.

Confidence intervals

Youll 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.

Introduction to hypothesis testing

Hypothesis testings data professionals determine if the results of a test or experiment are statistically significant or due to chance. Youll learn about the basic steps for any hypothesis test and how hypothesis testing can you draw meaningful conclusions about data.