Herramientas para la ciencia de datos

In this module, you will learn the tasks a data scientist performs to build a model. You will learn Data Asset Management and Code Asset Management that support various data science categories. You will then learn the several types of open-source, commercial, and cloud-based tools used in data science.

For users who are just starting on their data science journey, the range of programming languages can be overwhelming. So, which language should you learn first? This module will bring awareness about the criteria that would determine which language you should learn. You will learn the benefits of Python, R, SQL, and other common languages such as Java, Scala, C++, JavaScript, and Julia. You will explore how you can use these languages in Data Science. You will also look at some sites to locate more information about the languages.

In this module, you will learn about the various libraries in data science. In addition, you will understand an API in relation to REST request and response. Further, in the module, you will explore open data sets on the Data Asset eXchange. Finally, you will learn how to use a machine learning model to solve a problem and navigate the Model Asset eXchange.

With the advancement of digital data, Jupyter Notebook allows a Data Scientist to record their data experiments and results that others can reuse. This module introduces the Jupyter Notebook and Jupyter Lab. You will learn how to work with different kernels in a Notebook session and about the basic Jupyter architecture. In addition, you will identify the tools in an Anaconda Jupyter environment. Finally, the module gives an overview of cloud based Jupyter environments and their data science features.