How ozx-68102/coursera-ibm-data-science-practise works
This repository is an archived collection of solutions and learning materials for six IBM Data Science Professional Certificate courses, rather than a single application. A learner works through notebooks and notes to practice Python, collect data, query databases, analyze and visualize datasets, and build and evaluate machine-learning models. The analysis course names a clear progression from importing and wrangling data through exploratory analysis, model development, and evaluation. Other course areas are represented as capabilities, not as runtime-connected services. The graph is based on the README, tree, and sampled dashboard source; unsampled notebooks may contain additional details.
Main entry points: Module 1 Data Import · Module 2 Data Wrangling · Module 3 Exploratory Analysis · Module 4 Model Development
Components 25
Python and Collection
- Python Fundamentals
I. Python for Data Science, AI & Development/Module 1 - Programming Fundamentals
I. Python for Data Science, AI & Development/Module 3 - Files and Data Libraries
I. Python for Data Science, AI & Development/Module 4 - APIs and Web Scraping
I. Python for Data Science, AI & Development/Module 5 - Stock Data Project
II. Python Project for Data Science
Databases and SQL
- SQL Foundations
III. Databases and SQL for Data Science/Module 1-3 - Database Analysis
III. Databases and SQL for Data Science/Module 4 - SQLite Querying
III. Databases and SQL for Data Science/Module 5
Data Analysis
- Data Import
IV. Data Analysis with Python/Module 1 - Data Wrangling
IV. Data Analysis with Python/Module 2 - Exploratory Analysis
IV. Data Analysis with Python/Module 3 - Model Development
IV. Data Analysis with Python/Module 4 - Model Evaluation
IV. Data Analysis with Python/Module 5 - Applied Analysis
IV. Data Analysis with Python/Module 6
Data Visualization
- Plotting Fundamentals
V. Data Visualization with Python/Module 1 - Charts and Maps
V. Data Visualization with Python/Module 2 - Advanced Visualizations
V. Data Visualization with Python/Module 3 - Interactive Dashboards
V. Data Visualization with Python/Module 4 - Visualization Projects
V. Data Visualization with Python/Module 5
Machine Learning
- Supervised Learning
VI. Machine Learning with Python/Module 2 - Classification and Trees
VI. Machine Learning with Python/Module 3 - Unsupervised Learning
VI. Machine Learning with Python/Module 4 - Model Assessment
VI. Machine Learning with Python/Module 5 - Applied Prediction
VI. Machine Learning with Python/Module 6
Other components
- Learner
Connections 24
- Learner → Python Fundamentals: studies
- Learner → Programming Fundamentals: practices
- Learner → Files and Data Libraries: practices
- Learner → APIs and Web Scraping: explores
- Learner → Stock Data Project: completes
- Learner → SQL Foundations: studies
- Learner → Database Analysis: practices
- Learner → SQLite Querying: practices
- Learner → Data Import: imports data
- Data Import → Data Wrangling: curriculum continues
- Data Wrangling → Exploratory Analysis: curriculum continues
- Exploratory Analysis → Model Development: curriculum continues
- Model Development → Model Evaluation: curriculum continues
- Learner → Applied Analysis: analyzes
- Learner → Plotting Fundamentals: studies
- Learner → Charts and Maps: practices
- Learner → Advanced Visualizations: practices
- Learner → Interactive Dashboards: builds
- Learner → Visualization Projects: completes
- Learner → Supervised Learning: studies
- Learner → Classification and Trees: practices
- Learner → Unsupervised Learning: studies
- Learner → Model Assessment: evaluates models
- Learner → Applied Prediction: builds predictions