This is a self-paced lab that takes place in the Google Cloud console. In this lab you will explore existing datasets with Data Catalog and mine the table and column metadata for insights.

This course is the second part of a course dedicated to the theoretical and practical bases of Geographic Information Systems (GIS). It offers an introduction to GIS that does not require prior computer skills. It gives the opportunity to quickly acquire the basics that allow you to create spatial databases and produce geographic maps. This is a practical course that relies on the use of free Open Source software (QGIS, Geoda). In the first part of the course (Geographical Information Systems - Part 1), you explored the basics of land digitization and geodata storage. In particular, you learned how to: - Characterize spatial objects and phenomena (spatial modeling) from the point of view of their positioning in space (coordinate systems and projections, spatial relationships) and according to their intrinsic nature (object or vector mode vs. image or raster mode); - Use various data acquisition methods (direct measurement, georeferencing of images, digitization, existing data source, etc.); - Use various geodata storage methods (simple files and relational databases); - Use data modeling tools to describe and implement a database; - Create queries in a query language and data manipulation. The second part of the course deals with spatial analysis methods and georeferenced information representation techniques. In particular, you will learn how to: - Analyze the spatial properties of discrete variables, for example by quantifying spatial autocorrelation; - Work with continuous variables (sampling, interpolation and construction of isolines) - Use digital elevation models (DEMs) and their derivatives (slope, orientation, etc.); - Use geodata superposition techniques; - Produce cartographic documents according to the rules of the semiology of graphics; - Explore other forms of spatial representation (interactive cartography on the internet, 3D representations, and augmented reality). The page https://www.facebook.com/moocsig provides an interactive forum for participants in this course.

By the end of this project you will be able to use 15 practical date & time functions in Excel, and have a better understanding of their use. You’ll learn the basic use of the function, an example formula for the function, and some alternative ways to use the function by adding or combining to the formula. I will guide you step-by-step, explaining every part of the formula and how it achieves the desired calculation.

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!

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.

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.