By detecting and exploring missing values in datasets, you can enrich them through data imputation. In this way, you can run analyses with better information. This course teaches you how to treat variables and run the appropriate imputation method for your data set.
In data science, you will always encounter incomplete datasets you must deal with. This course teaches you how to address missing values and find relationships between them. Execute the best treatment to your data with missing values, eliminating or imputing them.
Your career in data science requires understanding the nature of data and its distribution and exploring it using statistical analysis or visualization tools. Through an EDA, you can propose the most appropriate model to address the questions of your projects.
Learn how to customize a data science workbench based on Python, Conda, and Jupyter Notebooks. With these tools, you will achieve harmony in your work environment and increase your productivity as a data scientist.
Learn about the different types of Jupyter notebook, the fundamental work tool for any data scientist.