Clarusway

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

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

After students receive an introduction to Agile software development processes, instructors provide information about increating requests, the availability of cloud infrastructures, 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 roles is stressed.

After students receive an introduction to Agile software development processes, instructors provide information about increating requests, the availability of cloud infrastructures, 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 roles is stressed.

The importance of skills involved in cloud computing is explained in this course, which teaches students Jenkins, deploying infrastructure based on code, implementing configuration management using Ansible, and deploying microservices at scale. Additionally, students receive advice and guidance on their portfolios.

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 amounts of information and learning how to deliver measurable results to stakeholders. Upon conclusion, students will be able to store and organize data, decipher 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 amounts of information and learning how to deliver measurable results to stakeholders. Upon conclusion, students will be able to store and organize data, decipher useful information, and present professional solutions.

This course teaches data types, data structures, and the data lifecycle. Students are taught how to derive insightful knowledge from raw data. Topics in the course include basic statistics, Python libraries, Pandas, NumPy, SciPy, and SQL. Additionally, information about data visualization with Tableau and understanding Google Sheets is included.

This course teaches data types, data structures, and the data lifecycle. Students are taught how to derive insightful knowledge from raw data. Topics in the course include basic statistics, Python libraries, Pandas, NumPy, SciPy, and SQL. Additionally, information about data visualization with Tableau and understanding Google Sheets is included.

This course shows students how to use scientific methods, procedures, algorithms, and systems necessary for Data Scientists and Machine Learning Engineers. Instructors demonstrate how to build complex quantitative algorithms, synthesize large amounts of information, and gain technical communication skills for reporting results.