Data Science
Learning outcomes: Analyze complex data using statistical and computational methods to extract insights Books and materials: Included Estimated duration: 16 Hours Pricing: USD 129.00
What You'll Learn
Module 1: Introduction to Data Science
This module develops core concepts and technical foundations for Data Science. Learners connect the topic to analyze complex data using statistical, computational methods, and extract insights in realistic data analysis & security situations. The main working habit in this module is that strong technical work starts with clear understanding before hands-on change.
3 lessons
Lesson 1: Understanding the Core Idea: Core Concepts and Technical Foundations
1h 5mImagine that you are a junior digital practitioner in a real system or guided lab where a careful change must be tested. Someone asks you to improve analyze complex data using statistical, but gives you no useful context. A weak response would repeat a definition. A professional response would first ask what result is needed, who is affected, and what evidence would show that the work is helping.
Lesson 2: Using the Idea in Practice: Core Concepts and Technical Foundations
1h 5mA concept only becomes useful when it survives contact with a real request. In this lesson, imagine that you are a junior digital practitioner in a real system or guided lab where a careful change must be tested. You have to improve analyze complex data using statistical with limited time, incomplete information, and a colleague waiting for a usable result.
Lesson 3: Scenario, Review, and Improvement: Core Concepts and Technical Foundations
1h 5mA useful skill is tested most clearly when the result is disappointing. In this lesson, imagine that you are a junior digital practitioner in a real system or guided lab where a careful change must be tested. The team has completed work on analyze complex data using statistical, but the result has not produced the intended result works and another person can understand what changed.
Module 2: Tools, Setup, and System Workflow: Analyze Complex Data Using Statistical
This module develops tools, setup, and system workflow for Data Science. Learners connect the topic to analyze complex data using statistical, computational methods, and extract insights in realistic data analysis & security situations. The main working habit in this module is that repeatable setup reduces avoidable technical mistakes.
3 lessons
Lesson 1: Understanding the Core Idea: Tools, Setup, and System Workflow
1h 5mImagine that you are a junior digital practitioner in a real system or guided lab where a careful change must be tested. Someone asks you to improve computational methods, but gives you no useful context. A weak response would repeat a definition. A professional response would first ask what result is needed, who is affected, and what evidence would show that the work is helping.
Lesson 2: Using the Idea in Practice: Tools, Setup, and System Workflow
1h 5mA concept only becomes useful when it survives contact with a real request. In this lesson, imagine that you are a junior digital practitioner in a real system or guided lab where a careful change must be tested. You have to improve computational methods with limited time, incomplete information, and a colleague waiting for a usable result.
Lesson 3: Scenario, Review, and Improvement: Tools, Setup, and System Workflow
1h 5mA useful skill is tested most clearly when the result is disappointing. In this lesson, imagine that you are a junior digital practitioner in a real system or guided lab where a careful change must be tested. The team has completed work on computational methods, but the result has not produced the intended result works and another person can understand what changed.
Module 3: Security, Quality, and Troubleshooting
This module develops security, quality, and troubleshooting for Data Science. Learners connect the topic to analyze complex data using statistical, computational methods, and extract insights in realistic data analysis & security situations. The main working habit in this module is that safe troubleshooting protects both the user and the system.
3 lessons
Lesson 1: Understanding the Core Idea: Security, Quality, and Troubleshooting
1h 5mImagine that you are a junior digital practitioner in a real system or guided lab where a careful change must be tested. Someone asks you to improve extract insights, but gives you no useful context. A weak response would repeat a definition. A professional response would first ask what result is needed, who is affected, and what evidence would show that the work is helping.
Lesson 2: Using the Idea in Practice: Security, Quality, and Troubleshooting
1h 5mA concept only becomes useful when it survives contact with a real request. In this lesson, imagine that you are a junior digital practitioner in a real system or guided lab where a careful change must be tested. You have to improve extract insights with limited time, incomplete information, and a colleague waiting for a usable result.
Lesson 3: Scenario, Review, and Improvement: Security, Quality, and Troubleshooting
1h 5mA useful skill is tested most clearly when the result is disappointing. In this lesson, imagine that you are a junior digital practitioner in a real system or guided lab where a careful change must be tested. The team has completed work on extract insights, but the result has not produced the intended result works and another person can understand what changed.
Module 4: Applied Practice, Testing, and Review
This module develops applied practice, testing, and review for Data Science. Learners connect the topic to analyze complex data using statistical, computational methods, and extract insights in realistic data analysis & security situations. The main working habit in this module is that technical confidence grows through hands-on repetition and clear notes.
3 lessons
Lesson 1: Understanding the Core Idea: Applied Practice, Testing, and Review
1h 5mImagine that you are a junior digital practitioner in a real system or guided lab where a careful change must be tested. Someone asks you to improve analyze complex data using statistical, but gives you no useful context. A weak response would repeat a definition. A professional response would first ask what result is needed, who is affected, and what evidence would show that the work is helping.
Lesson 2: Using the Idea in Practice: Applied Practice, Testing, and Review
1h 5mA concept only becomes useful when it survives contact with a real request. In this lesson, imagine that you are a junior digital practitioner in a real system or guided lab where a careful change must be tested. You have to improve analyze complex data using statistical with limited time, incomplete information, and a colleague waiting for a usable result.
Lesson 3: Scenario, Review, and Improvement: Applied Practice, Testing, and Review
1h 5mA useful skill is tested most clearly when the result is disappointing. In this lesson, imagine that you are a junior digital practitioner in a real system or guided lab where a careful change must be tested. The team has completed work on analyze complex data using statistical, but the result has not produced the intended result works and another person can understand what changed.
Enrollment
$129
Includes 12 lessons, learner enrollment, and access through the student account.
This course is already in the catalog, but new learner enrollment has not opened yet.