How mragpavank/data-science-materials-useful-for-all-data-science-professionals works
This repository is a collection of educational materials for learning data science and related topics. The principal workflow is a learner choosing and studying a resource directly; the README describes educational use but does not document an application or an ordered curriculum. The graph groups representative materials by subject, not by a claimed sequence. The flat file tree and lack of source excerpts provide no evidence for further runtime relationships.
Main entry points: Python manual.pdf Python manual · STATISTICS for Data science.pdf Statistics for data science · SQL technical concepts.pdf SQL concepts · Introducion to Data analysis.pdf Data analysis introduction
Components 18
Programming & statistics
- Python manual
Python manual.pdf - Statistics for data science
STATISTICS for Data science.pdf - SQL concepts
SQL technical concepts.pdf
Data analysis
- Data analysis introduction
Introducion to Data analysis.pdf - Learning pandas
Learning pandas.pdf - Data cleaning
Data cleaning.pdf - Data visualization
Principle of data visualization.pdf - Analytics guide
Analytics guide.pdf
Machine learning & AI
- ML basics
ML Basics.pdf - ML pipelines
Machine learning Pipelines.pdf - Deep learning
Deep learning.pdf - NLP
NLP.pdf - MLOps
MLOps.pdf
Projects & careers
- Data science projects
Data Science Projectss-Ideas from Krish Naik.pdf - Data analyst roadmap
Data Analyst Roadmap.pdf - Data science interview questions
Data science Interview questions.pdf - Data science resources
README.md
Other components
- Learner
Connections 17
- Learner → Python manual: studies
- Learner → Statistics for data science: studies
- Learner → SQL concepts: studies
- Learner → Data analysis introduction: studies
- Learner → Learning pandas: studies
- Learner → Data cleaning: studies
- Learner → Data visualization: studies
- Learner → Analytics guide: studies
- Learner → ML basics: studies
- Learner → ML pipelines: studies
- Learner → Deep learning: studies
- Learner → NLP: studies
- Learner → MLOps: studies
- Learner → Data science projects: studies
- Learner → Data analyst roadmap: studies
- Learner → Data science interview questions: studies
- Learner → Data science resources: checks purpose