Guide to learning the basics of data science

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.

This requires an understanding of data structures, data manipulation with tools such as Pandas and Numpy, data cleaning and preparation.

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.

It's important to understand the different machine learning algorithms such as linear regressions, decision trees, neural networks and clustering algorithms.

It's important to understand model evaluation methods such as cross-validation, confusion matrix and performance measures such as AUC and F1 score.

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.

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.