Data Science Pipeline

This repository provides a detailed checklist and tech stack for each phase of a typical data science pipeline, from planning to data acquisition, data preparation, exploratory data analysis, model building, model evaluation, model deployment, monitoring and maintenance, and reporting.

Table of Contents

1. Planning
2. Data Acquisition
3. Data Preparation
4. Exploratory Data Analysis
5. Model Building
6. Model Evaluation
7. Model Deployment
8. Monitoring and Maintenance
9. Reporting
10. Data Science Tech Stack

Planning

The planning phase sets the foundation for a successful data science project. It involves defining project goals, identifying stakeholders, assessing data availability and accessibility, resource allocation, creating a project timeline, considering data privacy and ethics, risk assessment, defining evaluation metrics, establishing communication and collaboration channels, and planning documentation and project review. Detailed Checklist

Data Acquisition

This phase involves gathering data from various sources, which may include SQL databases, NoSQL databases, APIs, web scraping, file formats, cloud storage, and data streaming. It is important to ensure that the data collected is representative, legally and ethically sourced, and as clean as possible from the start. Detailed Checklist

Data Preparation

The data preparation phase involves cleaning the data, transforming it into a suitable format for analysis, feature engineering, handling outliers, data partitioning, handling imbalanced data, feature scaling, and feature selection. Proper cleaning, transformation, and feature engineering can lead to more accurate and reliable models. Detailed Checklist

Exploratory Data Analysis

This phase involves understanding the data by summarizing its main characteristics, often through visual methods. This is a critical step before the modeling phase as it involves understanding the underlying structure of the data, the variables and their relationships, and identifying any biases, outliers, or anomalies in the data. Detailed Checklist

Model Building

This phase involves selecting the appropriate algorithm for your data and goal, configuring parameters, training the model, and then testing the model for preliminary results. Detailed Checklist

Model Evaluation

The model evaluation phase involves evaluating the model's performance using appropriate metrics and then tuning the model if necessary. It's important to choose the right metric based on the business problem and the type of model. Detailed Checklist

Model Deployment

In this phase, the model is deployed into a production or production-like environment for scoring or further analysis. Once deployed, the model's performance is monitored to ensure it is providing the results expected. Detailed Checklist

Monitoring and Maintenance

Once the model is deployed, it needs to be monitored and maintained to ensure it's still performing as expected. This could involve retraining the model, performance checks, data drift detection, model versioning, and setting up alerting systems. Detailed Checklist

Reporting

The reporting phase involves communicating the results of the model to stakeholders in a clear and concise manner. This may involve generating visualizations, summarizing model performance, and explaining model predictions. Detailed Checklist

Data Science Tech Stack

This section provides a detailed description of the tech stack used in each phase of the data science pipeline. It covers a range of tools from Python, Jupyter Notebooks, various Python libraries, Git/GitHub, Docker, SQL/NoSQL Databases, Cloud Platforms, and MLflow. Detailed Checklist