Welcome to the Preparing for the SAS Viya Programming Certification Exam course. This is the third and final course in the Coursera SAS Programmer specialization. You will apply what you have learned in the first two courses by writing code to execute in SAS Cloud Analytic Services and practicing for the SAS certification exams. This is an advanced course, intended for learners who have completed the first two courses in the Coursera SAS Programmer specialization: SAS Programming for Distributed Computing in SAS Viya and CASL Programming for Distributed Computing in SAS Viya. By the end of the course, you be prepared to take either of these SAS credential exams: - SAS® Viya® Programming Associate - SAS® Viya® Programming Specialist

In this course, you will: - Learn about GANs and their applications - Understand the intuition behind the fundamental components of GANs - Explore and implement multiple GAN architectures - Build conditional GANs capable of generating examples from determined categories The DeepLearning.AI Generative Adversarial Networks (GANs) Specialization provides an exciting introduction to image generation with GANs, charting a path from foundational concepts to advanced techniques through an easy-to-understand approach. It also covers social implications, including bias in ML and the ways to detect it, privacy preservation, and more. Build a comprehensive knowledge base and gain hands-on experience in GANs. Train your own model using PyTorch, use it to create images, and evaluate a variety of advanced GANs. This Specialization provides an accessible pathway for all levels of learners looking to break into the GANs space or apply GANs to their own projects, even without prior familiarity with advanced math and machine learning research.

In this two hour project-based course, you will implement Deep Convolutional Generative Adversarial Network using PyTorch to generate handwritten digits. You will create a generator that will learn to generate images that look real and a discriminator that will learn to tell real images apart from fakes. This hands-on-project will provide you the detail information on how to implement such network and train to generate handwritten digit images. In order to be successful in this project, you will need to have a theoretical understanding on convolutional neural network and optimization algorithm like Adam or gradient descent. This project will focus more on the practical aspect of DCGAN and less on theoretical aspect. 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 course you have the opportunity to use the skills you acquired in the two SAS programming courses to solve realistic problems. This course is also designed to give you a thorough review of SAS programming concepts so you are prepared to take the SAS Certified Specialist: Base Programming Using SAS 9.4 Exam.

In this course, we will explore fundamental issues of fairness and bias in machine learning. As predictive models begin making important decisions, from college admission to loan decisions, it becomes paramount to keep models from making unfair predictions. From human bias to dataset awareness, we will explore many aspects of building more ethical models.

In this project you will learn how to set up PyCaret for your natural language processing tasks, compare and create models effectively, visualize your models and corpus. All this with just a few lines of code.

This course is organized into two parts presenting the theoretical and practical foundations of geographic information systems (GIS). - Together theses courses constitute an introduction to GIS and require no prior knowledge. - By following this introduction to GIS you will quickly acquire the basic knowledge required to create spatial databases and produce high-quality maps and cartographic representations. - This is a practical course and is based on free, open-source software, including QGIS. If you study or work in the fields of land management or the analysis of geographically distributed objects such as land use planning, biology, public health, ecology, or energy, then this course is for you! In this first part of the course, we will focus on the digitization and the storage of geodata. In particular, you will learn: - To characterize spatial objects and/or phenomena (territory modeling) with respect to their position in space (through coordinate systems, projections, and spatial relationships) and according to their intrinsic nature (object/vector mode vs. Image/raster mode); - About the different means used to acquire spatial data; including direct measurement, georeferencing images, digitization, existing data source, etc.); - About the different ways in which geodata can be stored - notably, files and relational databases; - How to use data modeling tools to describe and create a spatial database; - To query and analyze data using SQL, a common data manipulation language. The second part of this course will focus on methods of spatial analysis and geodata representation. In this section, you will learn: - How to describe and quantify the spatial properties of discrete variables, for example through spatial autocorrelation; - To work with continuous variables. In particular, we will look at sampling strategies, how to construct contour lines and isovalue curves, and we will explore different interpolation methods; - To use digital elevation models and create their derivative products (i.e. slope, orientation); - How to evaluate the interaction between different types of geodata through overlay and interaction techniques; - How to create effective maps based around the rules of graphic semiology; - Finally, we will also explore other, increasingly common, forms of spatial representation such as interactive web-mapping and 3D representations. You can find an interactive forum for course participants on our Facebook page: https://www.facebook.com/moocsig

In this 1-hour long project-based course, you will learn exploratory data analysis techniques and create visual methods to analyze trends, patterns, and relationships in the data. By the end of this project, you will have applied EDA on a real-world dataset. This class is for learners who want to use Python for applying data visualization and data analysis, and for learners who are currently taking a basic machine learning course or have already finished a machine learning course and are searching for a practical data visualization and analysis project course. Also, this project provides learners with basic knowledge about exploratory analysis and improves their skills in creating maps which helps them in fulfilling their career goals by adding this project to their portfolios.

In this course you will experience the process of defining, creating, and managing relational database tables using the SQL language. Tables are used as the containers for the data in a database. As such, the structure, or makeup, of each table in a relational database is critical, since it must be designed and created specifically to meet the needs of the data it will contain. The table’s structure indicates which pieces of data are stored in a table, as well as the type and size of each piece of data. Throughout the course, you’ll be exposed to guidelines and rules that database designers use to make sure that the tables will keep the data as safe and accurate as possible. You’ll learn to use SQL code to incorporate the constraints that help the database management enforce those rules. As you work through and complete hands-on tasks, you’ll become familiar with SQLiteStudio, the database management system used in the course. Tables that are well-designed and created correctly improve data integrity--and make data retrieval easier! 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 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.