SML Roadmap
Statistics & Machine Learning combine reasoning, modeling, and domain knowledge to extract reliable insight from data.
This roadmap provides the core sequence for data acquisition, preparation, statistical analysis, modeling, and responsible AI practice.
SML Roadmap Progress:
| # | SML | Topic | Description |
|---|---|---|---|
| 01 | Basic Concepts | Core terminology and data science foundations. | |
| 02 | Data Life Cycle | Flow from data creation to archival and reuse. | |
| 03 | Data Collection | Acquisition methods, quality, and source validation. | |
| 04 | Data Storage | Storage models, access patterns, and governance basics. | |
| 05 | Data Cleaning | Pre-processing, transformation, and quality fixes. | |
| 06 | Data Analysis | Descriptive and inferential analysis methods. | |
| 07 | Data Visualization | Charts, dashboards, and communication of findings. | |
| 08 | Machine Learning | Modeling fundamentals and supervised workflows. | |
| 09 | Deep Learning | Neural network basics and practical applications. | |
| 10 | Ethical Issues | Bias, privacy, governance, and responsible AI. |
External Resources
- Data Science Specialization by Coursera
- Introduction to Data Science by edX
- Intro to Machine Learning by Udacity
- Data Science by Khan Academy
- Understanding Data Science by DataCamp