Using SAS Viya REST APIs with Python and R

SAS via Coursera

Go to Course: https://www.coursera.org/learn/sas-viya-rest-api-python-r

Introduction

### Course Review: Using SAS Viya REST APIs with Python and R In today's data-driven world, harnessing the power of analytics is essential for business success. If you're looking to enhance your data analysis skills or dive into machine learning with a robust analytics platform, the Coursera course "Using SAS Viya REST APIs with Python and R" is an excellent choice. Here’s a detailed overview of what you can expect from the course, as well as my recommendations. #### Overview SAS Viya is an in-memory distributed environment designed to process big data efficiently. This course provides a comprehensive introduction to utilizing SAS Viya APIs through popular programming languages—Python and R—from a Jupyter Notebook environment. It is tailored for data scientists, analysts, and anyone interested in maximizing their data analysis capabilities with cutting-edge tools. #### Syllabus Breakdown 1. **Course Overview** In the first module, you’ll meet the instructor and receive a thorough introduction to the course logistics. This sets a clear foundation of what to expect as you navigate through the modules. 2. **SAS® Viya® and Open Source Integration** This module introduces the analytical processing engine underpinning SAS Viya. You'll learn how to issue data processing commands seamlessly from both R and Python, broadening your analytical toolkit. 3. **Machine Learning** Here, you will explore the fundamentals of building predictive models using SAS Viya's powerful capabilities. This module covers the creation, optimization, and assessment of models utilizing R and Python. 4. **Text Analytics** Natural language processing takes center stage in this segment. You'll discover how to transform unstructured text into structured data that can be leveraged for predictive modeling, a crucial skill in today's data landscape. 5. **Deep Learning** This module dives into deep learning methodologies, including advanced neural network architectures. You’ll also learn how to model sequence data with recurrent neural networks—an essential skill for handling time series and textual data. 6. **Time Series** Time series forecasting is vital for many business scenarios. In this module, you’ll learn classical methods like exponential smoothing and ARIMAX, along with advanced modeling using recurrent neural networks. 7. **Image Classification** Discover the power of convolutional neural networks (CNNs) in classifying images. You'll come to understand how these models can be implemented using SAS Viya APIs for both R and Python. 8. **Factorization Machines** This module explores how factorization machines can be used to create recommendation engines. You’ll learn to build and train these models—a useful capability in the era of personalized user experiences. #### Recommendation This course is highly recommended for: - **Data Scientists and Analysts**: If you're already familiar with Python or R and wish to enhance your capabilities with an industry-leading analytics platform, this course will serve as an invaluable resource. - **Beginner and Intermediate Learners**: Even if you are somewhat new to machine learning or SAS, the structured curriculum and hands-on approach will help you build confidence and competence in data analysis. - **Professionals Seeking Career Advancement**: Understanding SAS Viya and its APIs will set you apart in the job market, particularly in roles related to data science, machine learning, and analytics. #### Conclusion "Using SAS Viya REST APIs with Python and R" on Coursera offers an expansive curriculum that equips you with necessary skills to conquer big data challenges. With expert instruction and practical application, you'll be prepared to utilize SAS Viya's powerful analytics capabilities effectively. Whether you are looking to focus on machine learning, text analytics, or deep learning, this course provides the essential foundation for your analytics journey. Investing time in this course could very well enhance your data science skills and open up new career opportunities. Don't miss out on the chance to leverage SAS Viya's potential through the friendly interfaces of Python and R!

Syllabus

Course Overview

In this module, you meet the instructor and learn about course logistics, such as how to access the software for this course.

SAS® Viya® and Open Source Integration

In this module you learn about the analytical processing engine behind SAS Viya, the Cloud Analytic Services server. You also learn how to submit data processing commands to SAS Viya from the open source languages R and Python.

Machine Learning

In this module you learn how to use R and Python to create, optimize, and assess SAS Viya predictive models. You also learn how to use R and Python to efficiently manage the creation and assessment of these models.

Text Analytics

In this module you learn how natural language processing is used to analyze collections of text documents. You also learn how to turn blocks of unstructured text into numeric inputs suitable for predictive modeling.

Deep Learning

In this module you learn how deep learning methods extend traditional neural network models with new options and architectures. You also learn how recurrent neural networks are used to model sequence data like time series and text strings, and how to create these models using R and Python APIs for SAS Viya.

Time Series

In this module you learn how to model time series using two popular methods, exponential smoothing and ARIMAX. You also learn how to use the R and Python APIs for SAS Viya to create forecasts using these classical methods and using recurrent neural networks for more complex problems.

Image Classification

In this module you learn how convolutional neural networks are used to classify images and how to use the R and Python APIs for SAS Viya to create convolutional neural networks.

Factorization Machines

In this module you learn how factorization machines are used to create recommendation engines and how to build factorization machine models in SAS Viya using the R and Python APIs.