Prediction of Employee Attrition with Oracle Analytics

From the Customer Segmentation tutorial, you already know that Oracle Analytics provides several Machine Learning (ML) algorithms to solve the Supervised Learning and Unsupervised Learning problems with low-code required.

Prediction of Employee Attrition is one of such Supervised Learning problems. It is a popular use case that most organizations need to resolve to minimize the attrition rate. You train a model with a labeled dataset, and Employee is labeled Yes or No for Attrition in the dataset.

CodingSight - Prediction of Employee Attrition with Oracle Analytics
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Query Google’s Financial Service Using Oracle Data Integrator

RESTful API is a popular protocol for exchanging data between different applications and systems especially in Web applications. Most programming languages support calling RESTful API(s) to query data quickly. Oracle Data Integrator supports RESTful services for reading and writing data.

In this tutorial, we are using Oracle Data Integrator to query data from Google Financial Service for stock prices of various companies, returning results to JSON files. Then we’ll import the JSON file into Autonomous Database. Let’s have a closer look at this process.

CodingSight - Oracle Data Integrator to query data from Google Financial Service for stock prices
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Oracle Live SQL: Pros and Cons, and What You Miss Out if You Don’t Try It

Oracle Live SQL is a new feature in Oracle Database 12c that allows the developers to easily create and run SQL scripts against Oracle databases. It is suitable for quick code demos, debugging, and troubleshooting. In general, it is an easier way for those who are teaching or demonstrating SQL, PL/SQL, or another function to use Oracle.

The good thing about Oracle Live SQL is that it works with any database on the platform (not just Oracle), so the choice is up to you.

CodingSight - Oracle Live SQL
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Query JSON Data with SQL/JSON Functions & Autonomous Database

In the previous article, we worked on the Twitter Activity dataset in JSON format with the SQL Developer Web tool in Autonomous Database. However, SQL Developer Web has its limitations. It only allows you to parse the first level of the JSON data. Thus, it won’t support parsing complicated JSON structures.

So, does it mean you can’t deal with the data you need properly? Fortunately, there is an alternative. You can use SQL/JSON functions json_value, json_query to build complicated query or use dot-notation to access your JSON data.

CodingSight - Query JSON Data with SQL/JSON Functions & Autonomous Database
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Customer Segmentation with Data Flow in Oracle Analytics

Customer Segmentation Analysis is a popular business issue, especially in Retail domain, where companies need to discover the data and group customers based on customer attributes, such demographics and customer transactions. The results of customer segmentation analysis let the retailers understand the customers’ behavior and create effective campaigns.

This tutorial will describe the usage of Data Flow in Oracle Analytics for resolving the Customer Segmentation problem (the data is hosted in Autonomous Data Warehouse).

CodingSight - Customer Segmentation with Data Flow in Oracle Analytics
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Import JSON Data with SQL Developer Web into Autonomous Database

Twitter Activity provides loads of information for analysis. You can learn your target audience better, determine the most popular content, check the right time when your visitors are more active, etc. By getting the Twitter Activity data and putting it into the advanced analytical systems, you can correlate that data with other essential information from different sources. The prospects are tremendous.

In this article, we’ll explore the case of importing the Twitter data with the help of SQL Developer Web into an Autonomous Database. The imported Twitter dataset is in JSON format. Thus, you’ll need to work on how to import JSON data into Autonomous Database.

CodingSight - Import JSON Data with SQL Developer Web & Autonomous Database
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Financial Dashboard With Oracle Visualization Desktop

Oracle Analytics Cloud (OAC) is a single cloud-based platform for data preparation, in-depth analytics, and comprehensive detailed reporting with customizable visualization in one solution. Oracle Business Intelligence Analytics helps to improve planning and make better-grounded decisions to achieve objectives faster and with less effort. Users can easily load the data from both internal and external sources into the system and report the results.

CodingSight - Financial Dashboard With Oracle Visualization Desktop
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Setting Up the Data Flow in Oracle Analytics

The traditional approach to the transformation process (Extract, Load, Transform) requires that technical users get involved directly. They execute connection to data sources, the transformation of data, and provide final datasets to business users. The process often requires much time whenever business users ask for more information or due to changes/updates in business logic. Then, technical users have to revert datasets and transformations.

CodingSight - Build Data Flow with Oracle Analytics
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Advanced Analytics with Oracle Data Analytics – Handling with Express Builder Functionality

This article completes the series of posts dedicated to Advanced Analytics with Oracle Data Analytics. Previously, we explored such metrics as Reference Line and Forecast Line in Part 1, and Outliers, Clusters, and Explain features in Part 2. The drag-and-drop methodology helps to build your informative and interactive visuals to view the data.

This part of the material will focus on the Express Builder functionality. This feature is especially helpful for advanced users who possess a deeper understanding of data science and statistics. It is necessary because users need more control over advanced functions and parameters. It also offers running Python scripts for data analysis.

CodingSight - Advanced Analytics with Oracle Analytics Part 3
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