Curriculum
- 3 Sections
- 11 Lessons
- 4 Hours
Expand all sectionsCollapse all sections
- Lesson 1: Data Science Fundamentals3
- 1.1Topic A: What Is Data Science? Data Science The Elements of Data Science Data Teams Required Skills and Knowledge Data Analytics Descriptive Analytics Types of Descriptive Analytics Diagnostic Analytics Types of Diagnostic Analytics Predictive Analytics Types of Predictive Analytics Prescriptive Analytics Types of Prescriptive Analytics Statistical Analysis Concepts Related Concepts and Technologies Pulling It All Together Discussing Data Science and Related Technologies Identifying Data Science Opportunities from a Dataset (Optional)
- 1.2Topic B: Types of Data Big Data Structured, Unstructured, and Semi-Structured Data Open and Proprietary Data Data Sources Data Repositories Discussing Types of Data Used in Data Science Projects
- 1.3Topic C: The Data Science Lifecycle Lifecycle Stages Problem Identification Data Collection Preprocessing Exploratory Data Analysis Modeling Model Deployment Communication of Results Discussing the Data Science Lifecycle
- Lesson 2: Functions of Data Science in Business4
- 2.1Topic A: Improve Customer Experience Customer Experience Personalized CX Sentiment Analysis Recommender Systems Self-Service Support Chatbots and Virtual Assistants Improving Customer Experience
- 2.2Topic B: Improve Marketing Efforts Audience Segmentation Targeted Advertising Campaign Optimization Marketing Measurement Analysis Improving Marketing Efforts
- 2.3Topic C: Optimize Organizational and Transactional Security Fraud Detection Minimization of Loan Defaults Reduction of Intellectual Property Theft Risk Identification and Mitigation Cybersecurity Considerations Optimizing Organizational and Transactional Security
- 2.4Topic D: Enhance Operational Practices System or Component Failure Sales Forecasting Dynamic Pricing Customer Churn Talent Acquisition Transportation and Logistics Enhancing Operational Practices
- Lesson 3: Implementing Business Requirements for Data Science4
- 3.1Topic A: Develop a Data-Centric Organization What Is a Data-Centric Organization? Challenges Associated with Building a Data-Centric Organization Organizational Preparation Team Preparation Discussing the Development of Data-Centric Organizations
- 3.2Topic B: Develop an Implementation Strategy Selection of Business Cases for Implementation Implementation Investments Data Collection Considerations Data Preparation Considerations Modeling and Deployment Considerations Results Communication Considerations Developing an Implementation Strategy
- 3.3Topic C: Identify Impact of Data Science on Business Effects on Overall Business Operations Effects on Business Processes and Practices Data Issues AI-Related Risks Discussing the Impact of Data Science on Business
- 3.4Topic D: Identify Governance Measures Ethical Considerations Legal and Regulatory Considerations, Frameworks, and Guidelines Discussing Governance Measures
Topic A: Improve Customer Experience Customer Experience Personalized CX Sentiment Analysis Recommender Systems Self-Service Support Chatbots and Virtual Assistants Improving Customer Experience
Prev
Topic C: Optimize Organizational and Transactional Security Fraud Detection Minimization of Loan Defaults Reduction of Intellectual Property Theft Risk Identification and Mitigation Cybersecurity Considerations Optimizing Organizational and Transactional Security
Next