Tableau is widely recognized as one of the premier data visualization software programs. For many years access to the program was limited to those who purchased licenses. Recently, Tableau launched a public version that grants the ability to create amazing data visualizations for free. Account members can also share and join projects to collaborate on projects that can change the world. In this project, we will learn how to create an account, how to upload and work with diverse data sets, and how to analyze marketing spend within Tableau. Learning to use this in-demand tool has applications in Marketing, Finance, Operations, Sales, and many other business functions. 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 lab you will learn how to use Cloud Functions to do lightweight processing of Cloud Logging messages

In this 1-hour long project-based course, you will learn how to set up and run your Jupyter Notebook, load, preview and visualize data, then train, test and evaluate a machine learning model that predicts if a patient has breast cancer or not. 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.

Inventory analytics is the corner stone of supply chain analytics. A company in trade industries may have 30-50% of their assets tied up in inventory. An effective inventory management can improve revenue by increasing product variety and availability, and reduce cost and speed up cash cycle by reducing excessive inventory and waste. Through real-life examples (e.g., Amazon vs. Macy’s), you will learn hands-on tools and skills to discover and solve inventory problems by data analytics. Upon completion, you can answer the following questions: 1. For which industries is inventory important? 2. How may inventory drive a company’s financial performance? 3. How do I know that I have an inventory problem? 4. How to classify inventory and manage it accordingly? Note: This course is for beginners and thus focuses more on discovering inventory problems than solving them. It is my belief that discovering a problem and knowing which directions to go is at least equally important as solving the problem. We are planning to launch new courses or expand this course to cover more sophisticated inventory solutions in the future - thank you.

New Power BI users will begin the course by gaining a conceptual understanding of the Power BI desktop application and the Power BI service. Learners will explore the Power BI interface while learning how to manage pages and understand the basics of visualizations. Learners will engage in numerous hands-on experiences to discover how to import, connect, clean, transform, and model their own data in the Power BI desktop application. Learners will investigate reports, learn about workspaces, and practice viewing, creating, and publishing reports to the Power BI service. Finally, learners will become proficient in the creation and utilization dashboards. IMPORTANT NOTE: This course does not provide sample datasets for practice but requires learners to bring datasets. A video is included in Week 1 to show learners how to easily import Microsoft's sample datasets directly from their Power BI Service accounts.

This course focuses on data exploration, feature creation, and feature selection for time sequences. The topics discussed include binning, smoothing, transformations, and data set operations for time series, spectral analysis, singular spectrum analysis, distance measures, and motif analysis. In this course you learn to perform motif analysis and implement analyses in the spectral or frequency domain. You also discover how distance measures work, implement applications, explore signal components, and create time series features. This course is appropriate for analysts with a quantitative background as well as domain experts who would like to augment their time-series tool box. Before taking this course, you should be comfortable with basic statistical concepts. You can gain this experience by completing the Statistics with SAS course. Familiarity with matrices and principal component analysis are also helpful but not required.

By the end of this project, you will evaluate data types, apply naming conventions and integrate external resources in your SAS programs.

In this course you learn to develop and maintain a large-scale forecasting project using SAS Visual Forecasting tools. Emphasis is initially on selecting appropriate methods for data creation and variable transformations, model generation, and model selection. Then you learn how to improve overall baseline forecasting performance by modifying default processes in the system. This course is appropriate for analysts interested in augmenting their machine learning skills with analysis tools that are appropriate for assaying, modifying, modeling, forecasting, and managing data that consist of variables that are collected over time. The courses is primarily syntax based, so analysts taking this course need some familiarity with coding. Experience with an object-oriented language is helpful, as is familiarity with manipulating large tables.

Writing good code for data science is only part of the job. In order to maximizing the usefulness and reusability of data science software, code must be organized and distributed in a manner that adheres to community-based standards and provides a good user experience. This course covers the primary means by which R software is organized and distributed to others. We cover R package development, writing good documentation and vignettes, writing robust software, cross-platform development, continuous integration tools, and distributing packages via CRAN and GitHub. Learners will produce R packages that satisfy the criteria for submission to CRAN.

In this 2 hour 10 mins long project-based course, you will learn how to set up PyCaret Environment and become familiar with the variety of data preparing tasks done during setup, be able to create, see and compare performance of several models, learn how to tune your model without doing an exhaustive search, create impressive visuals of models, feature importance and much more. 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.