DATA SCIENCE LEADERSHIP TRAINING

Required qualifications: advanced Data Science skills and practical experience
Number of training days: 5 days

Training objective:
To learn strategic level, data-driven decision making using Data Science.

Description of the training:
The focus of the training is on learning about data-driven strategy making and decision making processes, and on mastering innovative and effective decision making based on big data. We will review data mining methods and opportunities for executive-level work, and then learn about data-driven strategies for business development. Finally, practical examples will be used to demonstrate different implementation methods to create effective processes.

Detailed topics:

Day 1: Data-driven strategy and decision making

  • Identifying business strategies and goals using a data-driven approach
  • Developing data collection strategies and managing data sources
  • Developing data-driven KPIs and indicators to measure performance
  • Evaluation and fine-tuning of data-driven strategiesDay 2: Big data and data mining at executive level
  • The strategic importance and benefits of managing big data
  • The importance of big data and the importance of big data
  • Overview of data mining tools and platforms for executive-level data work
  • Managing data mining projects and evaluating their resultsDay 3: Innovation and efficiency in Data Science leadership
  • Innovation strategies and processes in a data-driven environment
  • Data-driven product and service development strategies
  • Management-level data visualisation and reporting
  • Data-driven culture and change management in the organisationDay 4: Leadership Decision Making with Data Science
  • Data-driven decision-making methods and tools at the executive level
  • Data-driven risk management and security strategies
  • Management decision making and measuring its effectiveness
  • Managing and monitoring data-driven projects at the executive levelDay 5: Strategic Planning and Implementation in Data Science
  • Data-driven business planning processes and methods
  • Data-driven marketing strategies and campaign management
  • Management and management of data strategies and strategies for data management and data architecture design
  • Implementation strategies and processes for data-driven projects

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