Guide to learning the basics of data science
Published : February 12th, 2023
Updated : February 12th, 2023
1. Understand the basic concepts of statistics and mathematics
It's important to understand basic concepts such as probability, descriptive statistics, statistical inference, and mathematics such as linear algebra and optimizations.
For this, you can, for example, train at Khan Academy.
2. Apprendre à manipuler les données
3. Learn to use data visualization tools
Data visualizations are a crucial tool for understanding and interpreting data. It's important to know how to use tools such as Matplotlib and Seaborn to visualize data.
4. Understanding machine learning algorithms
It's important to understand the different machine learning algorithms such as linear regressions, decision trees, neural networks and clustering algorithms.
5. Understand model evaluation methods
It's important to understand model evaluation methods such as cross-validation, confusion matrix and performance measures such as AUC and F1 score.
6. Practice using data science projects
Practice is important to mastering data science skills. Participation in data science projects on online platforms such as Kaggle, Omdena, DrivenData, MachineHack or working on personal projects is recommended.
7. Stay up-to-date with the latest trends and developments in data science
Data science is a constantly evolving field. It's important to stay up to date with the latest trends by following data science blogs like KD Nuggets, attending conferences like NeurIPS, and reading professional literature like Paper With Code.
This is a starting point for learning the basics of data science. It is important to persevere with learning and never stop growing by following trends and practicing regularly. If you want quality data science courses, sign up here.
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