- Implementation of Agile
- Creating better scene with agile & data science
- Improving Prediction Performance
- Fixing Prediction Problem
- Agile Data Science - SparkML
- Deploying a predictive system
- Building a Regression Model
- Extracting features with PySpark
- Role of Predictions
- Working with Reports
- Data Enrichment
- Data Visualization
- Collecting & Displaying Records
- NoSQL & Dataflow programming
- SQL versus NoSQL
- Data Processing in Agile
- Agile Tools & Installation
- Agile Data Science - Process
- Methodology Concepts
- Agile Data Science - Introduction
- Agile Data Science - Home
Agile Data Science Useful Resources
Selected Reading
- Who is Who
- Computer Glossary
- HR Interview Questions
- Effective Resume Writing
- Questions and Answers
- UPSC IAS Exams Notes
Agile Data Science Tutorial
Agile is a software development methodology that helps in building software through incremental sessions using short iterations of 1 to 4 weeks so that the development is apgned with the changing business needs. Agile Data science comprises of a combination of agile methodology and data science. In this tutorial, we have used appropriate examples to help you understand agile development and data science in a general and quick way.
Audience
This tutorial has been prepared for developers and project managers to help them understand the basics of agile principles and its implementation. After completing this tutorial, you will find yourself at a moderate level of expertise, from where you can advance further with implementation of data science and agile methodology.
Prerequisites
It is important to have basic knowledge of data science modules and software development concepts such as software requirements, coding along with testing.
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