Data-Driven Management Decision-Making

In today's world, data is being generated at an unprecedented pace, and it has become increasingly important for managers to understand the challenges and techniques associated with the acquisition, storage, management, analysis, and visualisation of data to make informed management decisions. Our Short Learning Programme in Data-Driven Management Decision-Making has been designed to equip managers with the necessary skills and knowledge to navigate this complex world of big data. This programme offers a practical and hands-on approach that enables managers to gain immediate value from the interactions.

The gap in the market for this programme lies in the fact that while many organisations have recognised the importance of data-driven decision-making, they often struggle to implement it effectively due to a lack of understanding of the necessary tools and techniques. This programme addresses this gap by providing a comprehensive overview of the techniques and tools required for effective data-driven decision-making. Participants will gain hands-on experience with popular big data analytics tools and techniques. By the end of the programme, participants will be able to apply their learning to real-world scenarios and implement data-driven decision-making in their respective organisations.

Purpose of the course

The Short Learning Programme in Data-Driven Management Decision-Making offered by the NWU Business School is designed to equip participants with the necessary skills and knowledge to navigate the complex world of big data. By completing this programme, participants will gain a competitive advantage in today’s data-driven business landscape.

The programme takes a practical and hands-on approach, enabling participants to gain immediate value from the interactions. Participants can apply their learning to real-world scenarios, including case studies and exercises using popular big data analytics tools and techniques.

Participants will gain a comprehensive understanding of the challenges and techniques associated with data-driven decision-making. This includes data acquisition and management, data analysis and visualisation, statistical techniques, machine learning and artificial intelligence, and big data analytics tools and techniques. By the end of the programme, participants will be able to make data-driven decisions that are informed by insights from data analysis.

The programme’s curriculum is designed to address the gap in the market for effective data-driven decision-making in South Africa. Experienced industry professionals and academics deliver the programme with extensive data-driven decision-making experience. This combination of academic and industry expertise provides a unique learning experience tailored to South African organisations’ needs.

Admission requirements

Applicants must have completed grade 12 or an equivalent qualification recognised by the South African Qualifications Authority (SAQA). Participants should have access to their own computers and a stable, reliable internet connection for the duration of the course.

Learning assumed to be in place

Participants are expected to have a basic understanding of management principles and business operations. The programme is designed to provide a comprehensive overview of the techniques and tools required for effective data-driven decision-making. Therefore, no prior knowledge of statistics or data analysis concepts is necessary.

Course outcomes and assessment criteria

Course outcomes and the associated assessment criteria

CEd offering OUTCOMES

ASSESSMENT CRITERIA

On completion of the CEd offering, the participant should be able to demonstrate:

The participant will reach the CEd offering outcomes if he/she is able to:

A comprehensive understanding of data-driven Decision-making:

Basic knowledge and informed understanding of the challenges and techniques associated with data-driven decision-making.

Practical experience with popular big data analytics tools and techniques:

Hands-on experience using popular big data analytics tools and techniques and will be able to apply these to real-world scenarios and make data-driven decisions based on insights from data analysis.

Application of statistical techniques and machine learning algorithms:

The ability to apply statistical techniques to make data-driven decisions.

Implementation of data-driven decision-making:

An ability to implement data-driven decision-making in their respective organisations; an ability to identify challenges in implementing data-driven decision-making and to apply best practices to overcome these challenges.

Define the key challenges and techniques associated with data-driven decision-making.
Analyse and evaluate the suitability of different data acquisition, storage, management, analysis, and visualisation techniques for different scenarios.
Design and implement data-driven decision-making processes that are informed by insights from data analysis.
Identify and select appropriate big data analytics tools and techniques for different data-driven decision-making scenarios.
Apply popular big data analytics tools and techniques to real-world data-driven decision-making scenarios.
Evaluate the effectiveness of big data analytics tools and techniques in generating insights for data-driven decision-making.
Apply statistical techniques such as descriptive and inferential statistics, regression analysis, and hypothesis testing to real-world data-driven decision-making scenarios.
Apply machine learning algorithms to solve problems in data analysis and decision-making.
Evaluate the effectiveness of statistical techniques and machine learning algorithms in generating insights for data-driven decision-making.
Identify and analyse challenges in implementing data-driven decision-making within different organisational contexts.
Develop and apply best practices for implementing data-driven decision-making in real-world scenarios.
Evaluate the effectiveness of data-driven decision-making processes in driving business success.