This is a self-paced lab that takes place in the Google Cloud console. Cloud Dataprep by Trifacta is an intelligent data service for visually exploring, cleaning, and preparing structured and unstructured data for analysis. In this lab you will explore the Cloud Dataprep UI to build a data transformation pipeline.

This is a self-paced lab that takes place in the Google Cloud console. Learn the fundamentals of writing and executing queries to query soccer data stored in BigQuery tables. In this lab you will learn more fundamentals of sports data science by writing and executing queries to query data stored in BigQuery tables. The emphasis of the lab is to illustrate how the database works and answer some interesting questions related to the following topics in soccer.

In this 1 hour long project, you will build an attractive and eye-catching personal stock dashboard that is able to fetch the prices of stocks and cryptocurrencies in real-time. We will be using one of the best finance APIs to fetch real-time stock prices. We will begin by importing relevant financial data using the finance API. We will import two different data: one for stock summary and another for historical prices. We will then create our dashboard. In the final tasks, we will automate the dashboard so that you can get live prices and data for any of your favourite stocks or cryptocurrencies within a few seconds. By the end of this course, you will be confident in importing financial data using finance API, creating dashboards and live tracking of all of your favourite stocks in real-time using Power BI.

This course will teach you how to perform data analysis using MongoDB's powerful Aggregation Framework. You'll begin this course by building a foundation of essential aggregation knowledge. By understanding these features of the Aggregation Framework you will learn how to ask complex questions of your data. This will lay the groundwork for the remainder of the course where you'll dive deep and learn about schema design, relational data migrations, and machine learning with MongoDB. By the end of this course you'll understand how to best use MongoDB and its Aggregation Framework in your own data science workflow.

Successfully marketing brands today requires a well-balanced blend of art and science. This course introduces students to the science of web analytics while casting a keen eye toward the artful use of numbers found in the digital space. The goal is to provide the foundation needed to apply data analytics to real-world challenges marketers confront daily. Digital Analytics for Marketing Professionals: Marketing Analytics in Practice is the second in a two-part series of complementary courses and focuses on the skills and practical abilities analysts need to be successful in today's digital business world. You will be able to: - Identify the web analytic tool right for your specific needs - Understand valid and reliable ways to collect, analyze, and visualize data from the web - Utilize data in decision making for agencies, organizations, or clients This course is part of Gies College of Business’ suite of online programs, including the iMBA and iMSM. Learn more about admission into these programs and explore how your Coursera work can be leveraged if accepted into a degree program at https://degrees.giesbusiness.illinois.edu/idegrees/.

Object Localization is the task of locating an instance of a particular object category in an image, typically by specifying a tightly cropped bounding box centered on the instance. In this 2-hour project-based course, you will be able to understand the Object Localization Dataset and you will write a custom dataset class for Image-bounding box dataset. Additionally, you will apply augmentation for localization task to augment images as well as its effect on bounding box. For localization task augmentation you will use albumentation library. We will plot the (image-bounding box) pair. Thereafter, we will load a pretrained state of the art convolutional neural network using timm library.Moreover, we are going to create train function and evaluator function which will be helpful to write training loop. Lastly, you will use best trained model to find bounding box given any image.

In this 1-hour long project-based course, you will learn how to complete a training and test set using an R function, practice looking at data distribution using R and ggplot2, Apply a Random Forest model to the data using the FFTrees package in R, and examine the results using a Confusion Matrix. 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.

Welcome to this 1.5 hour long guided project on Data Visualization using Plotnine and ggplot. Plotnine library is a powerful python visualization library based on R's ggplot2 package and a great package to make professional plots. It has the grammar of graphics from ggplot and is used to add layers that control geometries, facets, themes and many constructs. In this project you will learn how to create beautiful visualizations using plotnine and gglot constructs. This guided project is for anyone who wants to learn data visualization or already in the data science field.

This introductory course is for SAS software users who perform statistical analyses using SAS/STAT software. The focus is on t tests, ANOVA, and linear regression, and includes a brief introduction to logistic regression.

This is a self-paced lab that takes place in the Google Cloud console. In this lab, you learn to use BigQuery to find data, query the data-to-insights public dataset, and write and execute queries.