Engineering and Technology
Learn about modularity, documentation, and automated testing to help you solve data science problems more quickly and reliably.
Data scientists can greatly enhance their skills by incorporating concepts from the field of software engineering. This integration enables them to efficiently reuse their code and collaborate effectively with others. In this course, participants will gain comprehensive knowledge of crucial concepts such as modularity, documentation, and automated testing. By understanding and implementing these ideas, data scientists can expedite problem-solving in the realm of data science while ensuring future satisfaction. Moreover, participants will have the opportunity to apply their newly acquired software engineering expertise by developing a personalized Python package for text analytics.
by DataCamp
Learn about modularity, documentation, and automated testing to help you solve data science problems...
by DataCamp
This course is an introduction to version control with Git for data scientists.
by DataCamp
Learn the basics of spreadsheets by working with rows, columns, addresses, and ranges.
by DataCamp
This course covers in detail the tools available in R for parallel computing.
by DataCamp
Data visualization is one of the most desired skills for data analysts. This course allows you to pr...
by DataCamp
Learn how to translate your SAS knowledge into R and analyze data using this free and powerful softw...
by DataCamp
In this Introduction to DevOps, you’ll master the DevOps basics and learn the key concepts, tools, a...
by DataCamp
Bring your spreadsheets to life by mastering fundamental skills such as formulas, operations, and ce...
by DataCamp
In this course, you will learn to read CSV, XLS, and text files in R using tools like readxl and dat...
by DataCamp
Learn how to build interactive and insight-rich dashboards with Dash and Plotly.