Data science is the field that use scientific methods, techniques, algorithms, and systems to extract knowledge and insights from noisy, structured, and unstructured data,and then implements that knowledge and actionable insights across a wide variety of application areas. For Starting your Machine Learning Journey, you need to be through with the basics.Here is where the art of analysing and understanding data steps in. Welcome to the world of Data Science
| S.No | Topics | Link |
|---|---|---|
| 1 | Choose a language Python(Preferred)/R | |
| 2 | Learn Libraries specific to Data Science | |
| 2.1 | Numpy | Docs |
| 2.2 | Pandas | Docs |
| 2.3 | Seaborn | Docs |
| 2.4 | Matplotlib | Docs |
| 3 | Analysing various Graphs | |
| 4 | Data Preprocessing | |
| 4.1 | Data Cleaning | |
| 4.1.1 | Missing Data | |
| 4.1.2 | Noisy Data | |
| 4.2 | Data Transformation | |
| 4.2.1 | Normalisation | |
| 4.2.2 | Attribution Selection | |
| 4.2.3 | Attribution Selection | |
| 4.2.4 | Concept Hierarchy Generation | |
| 4.3 | Data Reduction | |
| 4.3.1 | Data Cube Aggregation | |
| 4.3.2 | Attribute Subset Selection | |
| 4.3.3 | Numerosity Reduction | |
| 4.3.4 | Dimensionality Reduction |
Note:
- A lot of courses provide machine learning and data science together so it is bound to happen that a lot of topics might overlap.
- Perfection comes with practice , keep looking out for new notebooks and learn different methods of data pre-processing.
- Python for ML and Data Science Masterclass by Jose Portilla
- Kaggle course on Data Visualisation
- Kaggle course on Feature Engineering
- Kaggle course on Pandas
- Microsoft Repositroy - Data Science for Beginners
- Google Data Analytics Course
🎉 Wohoo, that's all for now, practice more and get better at it
With ❤️ by ISTE-VIT