This course focuses on the concepts and tools behind reporting modern data analyses in a reproducible manner. Reproducible research is the idea that data analyses, and more generally, scientific claims, are published with their data and software code so that others may verify the findings and build upon them. The need for reproducibility is increasing dramatically as data analyses become more complex, involving larger datasets and more sophisticated computations. Reproducibility allows for people to focus on the actual content of a data analysis, rather than on superficial details reported in a written summary. In addition, reproducibility makes an analysis more useful to others because the data and code that actually conducted the analysis are available. This course will focus on literate statistical analysis tools which allow one to publish data analyses in a single document that allows others to easily execute the same analysis to obtain the same results.

This is a self-paced lab that takes place in the Google Cloud console. This lab focuses on how to create new reporting tables using SQL JOINS and UNIONs.

By the end of this project, learners will know how to start out with GeoDa to use it for spatial analyses. This includes how to access and download the software, import multiple layers, and a basic overview of GeoDa. Spatial analysis, as a type of data analysis, has been getting increasingly important. The beginnings are often dated back to John Snow’s cholera outbreak maps from the mid-1800s. In 2003, Dr. Luc Anselin at the University of Chicago developed GeoDa, together with his team, to provide free software that digitizes old school pin maps. Today, it is used in various fields to plan cities and infrastructure, create crime maps, emergency management, and visualize finds at archaeological sites. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

In this project, you will learn to perform mediation analysis in RStudio. The project explains the theoretical concepts of mediation and illustrates the process with sample stress detection data. It covers the distinction between mediation and moderation process, explains the selection criteria for a suitable mediator. The project describes the mediation process with statistical models, diagnostic measures and conceptual diagram.

In this 1.5-hour long project-based course, you will learn how to create a Facebook Prophet Machine learning Model and use it to Forecast the Price of Bitcoin for the future 30 days. We will begin by importing all the necessary libraries including Facebook Prophet. Then we will import our dataset and analyze it. Then we will start creating visualizations in Plotly express in order to understand the historical performance of Bitcoin. We will then prepare our data for Facebook Prophet and create a Facebook Prophet Machine learning Model. We will then fit our prepared data to the Facebook Prophet Model and command it to make a Forecast for the future 30 days. We will then Visualize the Forecast using the Prophet’s internal visualization tools and then download the Forecast data. In the final section, we will go to Google Sheets and learn to extract Financial data of Bitcoin using Google Finance. We will then import the Forecast data into Google Sheets and compare it against the actual data and evaluate the performance of the Model. Please note that although this project deals with Bitcoin and teaches to make Price predictions, it is for educational purposes only and should not be taken for a piece of Financial advice since Cryptocurrencies like Bitcoin are extremely volatile and speculative. Basic knowledge of Python programming language is recommended but even those with no prior programming experience will be able to complete this project. You will need a Google account to complete this project. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

This is a self-paced lab that takes place in the Google Cloud console. In this hands-on lab you’ll learn how to use BigQuery to explore the cryptocurrency public datasets now available. This is a challange lab, and you are required to complete some simple SQL statements.

In this one hour long project-based course, you will tackle a real-world prediction problem using machine learning. The dataset we are going to use comes from the Australian government. They recorded daily weather observations from a number of Australian weather stations. We will use this data to train an artificial neural network to predict whether it will rain tomorrow. By the end of this project, you will have created a machine learning model using industry standard tools, including Python and sklearn. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

DataOps is defined by Gartner as "a collaborative data management practice focused on improving the communication, integration and automation of data flows between data managers and consumers across an organization. Much like DevOps, DataOps is not a rigid dogma, but a principles-based practice influencing how data can be provided and updated to meet the need of the organization’s data consumers.” The DataOps Methodology is designed to enable an organization to utilize a repeatable process to build and deploy analytics and data pipelines. By following data governance and model management practices they can deliver high-quality enterprise data to enable AI. Successful implementation of this methodology allows an organization to know, trust and use data to drive value. In the DataOps Methodology course you will learn about best practices for defining a repeatable and business-oriented framework to provide delivery of trusted data. This course is part of the Data Engineering Specialization which provides learners with the foundational skills required to be a Data Engineer.

Students are introduced to the field of financial accounting through defining the foundational activities, tools, and users of financial accounting. Students learn to use the accounting equation and are introduced to the four major financial statements. Additional topics include ethical considerations, recording business transactions, and the application of credit/debit rules.

This course will focus on theory and implementation of hypothesis testing, especially as it relates to applications in data science. Students will learn to use hypothesis tests to make informed decisions from data. Special attention will be given to the general logic of hypothesis testing, error and error rates, power, simulation, and the correct computation and interpretation of p-values. Attention will also be given to the misuse of testing concepts, especially p-values, and the ethical implications of such misuse. This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.