- 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
Creating better scene with agile and data science
61 Lectures 1 hours
Agile methodology helps organizations to adapt change, compete in the market and build high quality products. It is observed that organizations mature with agile methodology, with increasing change in requirements from clients. Compiling and synchronizing data with agile teams of organization is significant in rolling up data across as per the required portfolio.
Build a better plan
The standardized agile performance solely depends on the plan. The ordered data-schema empowers productivity, quality and responsiveness of the organization’s progress. The level of data consistency is maintained with historical and real time scenarios.
Consider the following diagram to understand the data science experiment cycle −
Data science involves the analysis of requirements followed by the creation of algorithms based on the same. Once the algorithms are designed along with the environmental setup, a user can create experiments and collect data for better analysis.
This ideology computes the last sprint of agile, which is called “actions”.
Actions involves all the mandatory tasks for the last sprint or level of agile methodology. The track of data science phases (with respect to life cycle) can be maintained with story cards as action items.
Predictive Analysis and Big data
The future of planning completely lies in the customization of data reports with the data collected from analysis. It will also include manipulation with big data analysis. With the help of big data, discrete pieces of information can be analyzed, effectively with slicing and dicing the metrics of the organization. Analysis is always considered as a better solution.