SAS Viya is an in-memory distributed environment used to analyze big data quickly and efficiently. In this course, you’ll learn how to use the SAS Viya APIs to take control of SAS Cloud Analytic Services from a Jupyter Notebook using R or Python. You’ll learn to upload data into the cloud, analyze data, and create predictive models with SAS Viya using familiar open source functionality via the SWAT package -- the SAS Scripting Wrapper for Analytics Transfer. You’ll learn how to create both machine learning and deep learning models to tackle a variety of data sets and complex problems. And once SAS Viya has done the heavy lifting, you’ll be able to download data to the client and use native open source syntax to compare results and create graphics.
Using SAS Viya REST APIs with Python and R
Dozenten: Jordan Bakerman
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Kompetenzen, die Sie erwerben
- Kategorie: Predictive Modeling
- Kategorie: SAS (Software)
- Kategorie: Deep Learning
- Kategorie: Data Processing
- Kategorie: Restful API
- Kategorie: R (Software)
- Kategorie: Advanced Analytics
- Kategorie: Data Analysis Software
- Kategorie: Jupyter
- Kategorie: Computer Vision
- Kategorie: Applied Machine Learning
- Kategorie: R Programming
- Kategorie: Big Data
- Kategorie: Predictive Analytics
- Kategorie: Time Series Analysis and Forecasting
- Kategorie: Natural Language Processing
- Kategorie: Image Analysis
- Kategorie: Artificial Neural Networks
- Kategorie: Statistical Programming
Wichtige Details
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23 Aufgaben
Erfahren Sie, wie Mitarbeiter führender Unternehmen gefragte Kompetenzen erwerben.
In diesem Kurs gibt es 8 Module
In this module, you meet the instructor and learn about course logistics, such as how to access the software for this course.
Das ist alles enthalten
1 Video4 Lektüren1 App-Element
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.
Das ist alles enthalten
10 Videos5 Aufgaben1 App-Element
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.
Das ist alles enthalten
15 Videos4 Aufgaben3 App-Elemente
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.
Das ist alles enthalten
9 Videos3 Aufgaben2 App-Elemente
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.
Das ist alles enthalten
13 Videos3 Aufgaben2 App-Elemente
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.
Das ist alles enthalten
11 Videos4 Aufgaben2 App-Elemente
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.
Das ist alles enthalten
7 Videos2 Aufgaben2 App-Elemente
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.
Das ist alles enthalten
4 Videos2 Aufgaben2 App-Elemente
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Status: Vorschau
Status: Kostenloser Testzeitraum
Status: Kostenloser Testzeitraum
Status: Kostenloser Testzeitraum
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Geprüft am 18. Okt. 2021
Grateful to the instructors! Thank you for enhancing my skills set.
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