Data Science
Computer ScienceChapter 1 — Introduction to Data Science
Lesson 1.4 : Data Science Lifecycle
Reading & Interactive Lesson
Data Science Lifecycle
Reading Material & Notes
Data Science Lifecycle
The Data Science Lifecycle describes the major steps involved in solving a data-driven problem.
Major Steps
- Problem Definition: Understand the business or technical problem.
- Data Collection: Collect relevant data from suitable sources.
- Data Cleaning: Handle missing, duplicate and incorrect data.
- Exploratory Data Analysis: Study patterns and relationships in the data.
- Feature Engineering: Create useful variables for analysis or modeling.
- Model Building: Apply suitable statistical or machine learning techniques.
- Evaluation: Measure the quality of the solution.
- Deployment: Make the solution available for practical use.
- Monitoring: Continuously check and improve the solution.
Simple Workflow
Problem
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Data Collection
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Data Cleaning
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Data Analysis
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Model Building
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Evaluation
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DeploymentCourse Curriculum
10 Chapters • 50 Lessons
Chapter 1 — Introduction to Data Science
5 Lessons
Chapter 2 — Data Science Lifecycle
5 Lessons
Chapter 3 — Python for Data Science
5 Lessons
Chapter 4 — NumPy and Pandas
5 Lessons
Chapter 5 — Data Cleaning and Preprocessing
5 Lessons
Chapter 6 — Exploratory Data Analysis
5 Lessons
Chapter 7 — Data Visualization
5 Lessons
Chapter 8 — Statistics for Data Science
5 Lessons
Chapter 9 — Introduction to Machine Learning
5 Lessons
Chapter 10 — Data Science Projects
5 Lessons