Clarusway

Students learn how to use Agile software development processes and methodologies in this class. Instructors show how to manage requests from business units to expand live environments, cloud infrastructure, utilize data center automation, and the application of configuration tools. Students are encouraged to adopt a “DevOps mindset” to focus on collaboration between developers and other stakeholders.

Students learn how to use Agile software development processes and methodologies in this class. Instructors show how to manage requests from business units to expand live environments, cloud infrastructure, utilize data center automation, and the application of configuration tools. Students are encouraged to adopt a “DevOps mindset” to focus on collaboration between developers and other stakeholders.

After students receive an introduction to Agile software development processes, instructors provide information about creating requests, the use of cloud infrastructure, the use of data center automation, and a focus on test automation and continuous integration approaches. The importance of creating strong communication between development and operations (DevOps) professionals and other stakeholders is stressed.

After students receive an introduction to Agile software development processes, instructors provide information about creating requests, the use of cloud infrastructure, data center automation, and a focus on test automation and continuous integration approaches. The importance of creating strong communication between development and operations (DevOps) professionals and stakeholders is stressed.

Topics students study in this class include extracting knowledge and insights from different data types using scientific methods, processes, algorithms, and systems. Additional topics include building complex quantitative algorithms, synthesizing large volumes of information, and learning how to deliver measurable results to stakeholders. Upon conclusion, students will be able to store and organize data, extract useful information, and present professional solutions.

Topics students study in this class include extracting knowledge and insights from different data types using scientific methods, processes, algorithms, and systems. Additional topics include building complex quantitative algorithms, synthesizing large volumes of information, and learning how to deliver measurable results to stakeholders. Upon conclusion, students will be able to store and organize data, extract useful information, and present professional solutions.

This course teaches data types, data structures, and data lifecycle management. Students are taught how to derive insights from raw data. Topics in the course include basic statistics, Python libraries, Pandas, NumPy, SciPy, and SQL. Additionally, the course includes data visualization with Tableau and an introduction to Google Sheets.

This course teaches data types, data structures, and data lifecycle management. Students are taught how to derive insights from raw data. Topics in the course include basic statistics, Python libraries, Pandas, NumPy, SciPy, and SQL. Additionally, the course includes data visualization with Tableau and an introduction to Google Sheets.

This course shows students how to use scientific methods, procedures, algorithms, and systems that are necessary for data scientists and machine learning engineers. Instructors demonstrate how to build complex quantitative algorithms, synthesize large volumes of information, and develop technical communication skills to report results.

Students learn front end and back end essentials in order to become full stack developers through this course. IT fundamentals such as Scratch, Agile, Jira, and Linux are presented in the first section, as well as Git, Python, and SQL. In the second section, students learn about front end elements like HTML, CSS, Bootstrap 5, SASS, JavaScript, the DOM, APIs, and React.js. The final section shows students how to create functional back end applications using Django.