Data scientists often get asked questions related to causality: (1) did recent PR coverage drive sign-ups, (2) does customer support increase sales, or (3) did improving the recommendation model drive revenue? Supporting company stakeholders requires every data scientist to learn techniques that can answer questions like these, which are centered around issues of causality and are solved with causal inference. In this project, you will learn the high level theory and intuition behind the four main causal inference techniques of controlled regression, regression discontinuity, difference in difference, and instrumental variables as well as some techniques at the intersection of machine learning and causal inference that are useful in data science called double selection and causal forests. These will help you rigorously answer questions like those above and become a better data scientist!

In this 2 hours project you will learn how to build custom regional reports with Google Analytics. You will familiarize with Google Analytics and its usage, create a marketing custom regional dashboard with table and graph widgets, customize a standard geo report and scheduled the report you have designed to be sent monthly via email to a distributed regional marketing team.

This course aims to provide managers and developers of contact tracing programs guidance on the most important indicators of performance of a contact tracing program, and a tool that can be used to project the likely impact of improvements in specific indicators. Students who complete the course will be proficient in using the Contact Tracing Evaluation and Strategic Support Application (ConTESSA) to estimate the impact of their contact tracing program on transmission and strategizing about how to increase their program’s impact. A secondary audience for the course will be decision makers interested in knowing more about the characteristics of effective contact tracing programs, and strategies to improve. The course is designed for individuals who are already leading contact tracing programs who have significant experience with epidemiology and public health. We strongly recommend completing this course on a laptop or a desktop rather than a phone as you’ll need to complete worksheets and open the course and the application simultaneously.

In this course, you will build on the skills learned in Exploratory Data Analysis with MATLAB and Data Processing and Feature Engineering with MATLAB to increase your ability to harness the power of MATLAB to analyze data relevant to the work you do. These skills are valuable for those who have domain knowledge and some exposure to computational tools, but no programming background. To be successful in this course, you should have some background in basic statistics (histograms, averages, standard deviation, curve fitting, interpolation) and have completed courses 1 through 2 of this specialization. By the end of this course, you will use MATLAB to identify the best machine learning model for obtaining answers from your data. You will prepare your data, train a predictive model, evaluate and improve your model, and understand how to get the most out of your models.

This is a self-paced lab that takes place in the Google Cloud console. In this hands-on lab you will explore using Google Cloud Kubernetes Engine and Kubeflow TFJob to scale out TensorFlow distributed training.

This course is an introduction to 3D scientific data visualization, with an emphasis on science communication and cinematic design for appealing to broad audiences. You will develop visualization literacy, through being able to interpret/analyze (read) visualizations and create (write) your own visualizations. By the end of this course, you will: -Develop visualization literacy. -Learn the practicality of working with spatial data. -Understand what makes a scientific visualization meaningful. -Learn how to create educational visualizations that maintain scientific accuracy. -Understand what makes a scientific visualization cinematic. -Learn how to create visualizations that appeal to broad audiences. -Learn how to work with image-making software. (for those completing the Honors track)

This is a self-paced lab that takes place in the Google Cloud console. Use the Variant Transforms tool to transform and load VCF files from Cloud Storage into BigQuery.

This is a self-paced lab that takes place in the Google Cloud console. In this lab you'll learn how to assess an IT environment with StratoZone's scalable discovery.

This course introduces several tools for processing business data to obtain actionable insight. The most important tool is the mind of the data analyst. Accordingly, in this course, you will explore what it means to have an analytic mindset. You will also practice identifying business problems that can be answered using data analytics. You will then be introduced to various software platforms to extract, transform, and load (ETL) data into tools for conducting exploratory data analytics (EDA). Specifically, you will practice using PowerBI, Alteryx, and RStudio to conduct the ETL and EDA processes. The learning outcomes for this course include: 1. Development of an analytic mindset for approaching business problems. 2. The ability to appraise the value of datasets for addressing business problems using summary statistics and data visualizations. 3. The ability to competently operate business analytic software applications for exploratory data analysis.

Optimization is an important piece of an agile supply chain. In this course, we will explore the components of optimization and how to set up an optimization problem in Excel. We will also practice capacity and resource optimization and explore examples of both in the supply chain. Building off of our optimization practice, we will next learn how to use a Monte Carlo simulation to make the least risky decision in uncertain supply chain situations. Finally, we will combine our skills from this and the previous two courses to build a demand and inventory snapshot and optimize it, using a Monte Carlo simulation, to mitigate risks in the supply chain.