Exploratory Data Analysis (EDA) is an important step in the data science process that helps gain insights into the structure, distribution, and relationships within the data set. It is also crucial to identify any potential issues in the data and make informed decisions about how to pre-process and clean the data.

Participants will learn how to generate visualisations to help identify patterns and relationships within the data. They will be introduced to basic statistical concepts, such as measures of central tendency and dispersion, and how to make use of the data set for descriptive analytics.

This module will focus on helping participants extract insights from the data and link them together into a coherent story. This requires understanding the data, being able to identify trends and patterns, and being able to communicate these findings effectively

Feature engineering is the process of creating new features or modifying existing ones to capture important information in the data. This can help improve the performance of the model by providing it with relevant and informative input features. This may involve handling missing values, converting categorical variables into numerical ones, normalising or scaling the data, and so on.

The goal of feature engineering is to train participants to manipulate and prepare data, such that it maximises the performance of the machine learning algorithms and helps the model generalise well to new, unseen data.

Quality analytics can only be derived from quality data. Hence, it is vital for data professionals to appreciate what goes into a well-thought data engineering flow that converts raw data into analytics-ready data sets. This includes how data is first captured, ingested and stored, and how data can then be extracted and transformed for analytics.

Participants will be exposed to key concepts like ETL, data pipelines, DAGs (Directed Acyclic Graphs), and workflow orchestration, as well as best practices for handling structured data, data marts, and ensuring data quality.

This module will allow participants to better work with their data engineers on key data engineering processes that helps to facilitate their analytics needs. 

For professionals working with data, data is not just numbers – it’s the key to unlocking actionable insights, driving efficiency, and creating value. Employers rely on accurate, well-managed data to craft strategies, optimize operations, and deliver personalised experiences to customers. In the broader context of the Digital Economy, data powers emerging technologies like AI and machine learning, while in the Knowledge Economy, it fuels the exchange of ideas and expertise. Together, they highlight the pivotal role of data as both a strategic asset and a driver of economic growth. Data needs to be managed!

In this Data Management and Governance, participants will gain an appreciation for the critical role data plays as a valuable organisational asset. With increasing reliance on data-driven decision-making, effective data management and governance are essential for ensuring accuracy, security, and compliance. 

As data and machine learning continue to reshape industries, the need for robust AI governance has never been greater. This course is designed for professionals with knowledge in data and AI who want to deepen their understanding of the importance of AI governance. Participants will explore the ethical, operational, and regulatory challenges of implementing governance frameworks.

Learn how to identify risks, develop effective policies, and overcome obstacles in ensuring responsible AI use. Gain the knowledge to align AI practices with stakeholders while addressing the complexities of governance in a rapidly evolving landscape.

Businesses increasingly rely on data projects to drive growth, efficiency, and innovation. However, many fail due to poor planning, misaligned objectives, or unforeseen risks. For data professionals, mastering the end-to-end design and planning of data projects sets them apart as leaders in their field.

Moreover, designing and planning successful data projects requires more than technical expertise – it demands a holistic understanding of data, algorithms, business processes, and implementation strategies. This course empowers data professionals to structure their projects effectively, aligning them with business objectives while anticipating and addressing potential pitfalls.

Participants will explore best practices for project design, learn to identify and mitigate risks, and gain practical insights into integrating data-driven solutions within real-world business environments. By the end of the course, participants will be equipped to lead impactful and resilient data projects.