Workflow in team of data scientists
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This document discusses the workflow in a team of data scientists. It outlines the typical data science process including business understanding, data understanding, data preparation, modeling, and evaluation. It also describes common machine learning techniques like supervised learning and discusses how different roles like data scientists, data engineers, and the business side collaborate at each stage of a data science project.
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About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
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Application
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Thank me later.
samsarthak31@gmail.com
Design and Analysis of Algorithms-DP,Backtracking,Graphs,B&B
Dynamic Programming
Backtracking
Techniques for Graphs
Branch and Bound
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Workflow in team of data scientists
- 1. Workflow in a team of data scientists
- 2. Understanding of data science process data models
- 3. Understanding of data science process data models
- 4. Types of ML
- 6. Data Science workflow by CRISP-DM
- 10. Data Preparation (50-70% of project time) ● ●
- 12. Modeling ● ● ●
- 13. Modeling ● sklearn.ensemble.GradientBoostingClassifier(loss=’deviance’, learning_rate=0.1, n_estimators=100, subsample=1.0, criterion=’friedman_mse’, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_depth=3, min_impurity_decrease=0.0, min_impurity_split=None, init=None, random_state=None, max_features=None, verbose=0, max_leaf_nodes=None, warm_start=False, presort=’auto’)
- 14. Modeling ●
- 15. Evaluation
- 16. De data about an application OneKarma Scoring service API trained model { model scorefetch data
- 18. Terms for each stage
- 19. Data scientists Data engineersData scientists Business side Data scientists Data engineers Data scientists Data scientists Data engineers Development team QA team Data scientists Business side
- 20. Thank you for your attention.