- Agile Data Science Tutorial
- Agile Data Science - Home
- Agile Data Science - Introduction
- Methodology Concepts
- Agile Data Science - Process
- Agile Tools & Installation
- Data Processing in Agile
- SQL versus NoSQL
- NoSQL & Dataflow programming
- Collecting & Displaying Records
- Data Visualization
- Data Enrichment
- Working with Reports
- Role of Predictions
- Extracting features with PySpark
- Building a Regression Model
- Deploying a predictive system
- Agile Data Science - SparkML
- Fixing Prediction Problem
- Improving Prediction Performance
- Creating better scene with agile & data science
- Implementation of Agile
- Agile Data Science Useful Resources
- Agile Data Science - Quick Guide
- Agile Data Science - Resources
- Agile Data Science - Discussion
- Selected Reading
- UPSC IAS Exams Notes
- Developer's Best Practices
- Questions and Answers
- Effective Resume Writing
- HR Interview Questions
- Computer Glossary
- Who is Who
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 aligned 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.