1. Workflow
   1. Understand the business problem at hand
   2. Gather the raw data
   3. Explore, transform, clean, and prepare the data
   4. Create and select models based on the data
   5. Train, test, tune and deploy the model
   6. Monitor the model’s performance
2. Blitzstein & Pfister’s workflow
   1. Ask an interesting question
   2. Get the data
   3. Explore the data
   4. Model the data
   5. Communicate and visualize the results
3. Aakash Tandel’s Workflow
   1. Objective
   2. Importing Data
   3. Data Exploration and Data Cleaning
   4. Modeling Data
      1. Baseline Modeling
      2. Secondary Modeling
   5. Communicating Results
4. Aakanksha Joshi’s Workflow
   1. Connect & access data
   2. Search and find relevant data
   3. Prepare for data analysis
   4. Build/train/deploy models
   5. Monitor/analyze/manage models
5. Philip Guo’s Workflow
   1. Preparation of the data, then alternating between
   2. Analysis
   3. Reflection to interpret the outputs, and finally
   4. Dissemination of results in the form of written reports and/or executable code
6. CRISP-DM
   1. CRoss-Industry Standard Process for Data Mining
   2. six iterative phases
      1. Business understanding
      2. data understanding
      3. data preparation
      4. modeling
      5. evaluation
      6. deployment
7. OSEMN
   1. Obtain
   2. Scrub
   3. Explore
   4. Model
   5. iNterpret
8. TDSP
   1. Team Data Science Process
      1. Business Understanding
      2. Data Acquisition and Understanding
      3. Modeling
      4. Deployment
      5. Customer Acceptance
9. Sources
   1. Data Science Workflow — Experiment Tracking
   2. What is a Data Science Workflow?
10. More my mindmaps
    1. Описываем задачу/гипотезу/метрику для ML проекта
    2. Строим Дашборд правильно
    3. О Сегментировании через 5W2H
    4. HADI. Let's Test Hypothesis (based on PDCA) (v2)
    5. My TOP 5 Python Libraries for Data Science
    6. Статистика: Задача- Метод. A/B Testing. Statistical tests (Ru, En)
    7. Some notes about decision trees
    8. ML-продукты. Разбираем основы
    9. Some notes about Exploratory Data Analysis (Python and Pandas)
    10. Some notes about A/B Testing (V 2.0)