Online courses directory (209)
The Introduction to Data Science class will survey the foundational topics in data science, namely: * Data Manipulation * Data Analysis with Statistics and Machine Learning * Data Communication with Information Visualization * Data at Scale -- Working with Big Data The class will focus on breadth and present the topics briefly instead of focusing on a single topic in depth. This will give you the opportunity to sample and apply the basic techniques of data science. This course is also a part of our Data Analyst Nanodegree.
Machine Learning is a first-class ticket to the most exciting careers in data analysis today. As data sources proliferate along with the computing power to process them, going straight to the data is one of the most straightforward ways to quickly gain insights and make predictions. Machine learning brings together computer science and statistics to harness that predictive power. It’s a must-have skill for all aspiring data analysts and data scientists, or anyone else who wants to wrestle all that raw data into refined trends and predictions. This is a class that will teach you the end-to-end process of investigating data through a machine learning lens. It will teach you how to extract and identify useful features that best represent your data, a few of the most important machine learning algorithms, and how to evaluate the performance of your machine learning algorithms. This course is also a part of our Data Analyst Nanodegree.
Statistics is an important field of math that is used to analyze, interpret, and predict outcomes from data. Descriptive statistics will teach you the basic concepts used to describe data. This is a great beginner course for those interested in Data Science, Economics, Psychology, Machine Learning, Sports analytics and just about any other field.
This class presents the fundamental probability and statistical concepts used in elementary data analysis. It will be taught at an introductory level for students with junior or senior college-level mathematical training including a working knowledge of calculus. A small amount of linear algebra and programming are useful for the class, but not required.
Learn how to model social and economic networks and their impact on human behavior. How do networks form, why do they exhibit certain patterns, and how does their structure impact diffusion, learning, and other behaviors? We will bring together models and techniques from economics, sociology, math, physics, statistics and computer science to answer these questions.
The Coursera course, Data Analysis and Statistical Inference has been revised and is now offered as part of Coursera Specialization “Statistics with R”. This course introduces you to the discipline of statistics as a science of understanding and analyzing data. You will learn how to effectively make use of data in the face of uncertainty: how to collect data, how to analyze data, and how to use data to make inferences and conclusions about real world phenomena.
This course exposes students to the logic of statistical reasoning and its application in the quantitative social sciences. It is meant as a thorough but accessible introduction to the topics of descriptive statistics, probability theory, and statistical inference with hands-on exercises.
Learn about descriptive statistics, and how they are used and misused in the social and behavioral sciences. Learn how to critically evaluate the use of descriptive statistics in published research and how to generate descriptive statistics yourself, using freely available statistical software.
Learn about inferential statistics, and how they are used and misused in the social and behavioral sciences. Learn how to critically evaluate the use of inferential statistics in published research and how to generate these statistics yourself, using freely available statistical software.
We prepare high school teachers for teaching descriptive statistics. Teachers will learn basic principles for summarizing data in meaningful ways. Satellite videos will discuss pedagogy and teach statistical software via examples spanning pop culture, sports, health and other topics suitable for high school classrooms.