Data Processing and Analysis
- Data integration and management: Combining data from different sources and formatting it in a way that can be used for analysis.
- Data cleansing and pre-processing: Identifying and correcting errors and inconsistencies in the data, handling missing values and formatting the data in a way that can be used by the machine learning algorithms.
- Advanced analytics: Applying advanced analytical techniques such as statistical modelling, machine learning, and predictive analytics to uncover insights from the data.
- Optimisation and decision making: Using the data and models to optimise business processes and make strategic decisions.
- Real-time data processing: Building and implementing systems to process data in real-time, enabling organisations to make timely decisions based on the most recent data.
- Predictive modelling: Building data models that can make predictions or decisions based on the data.
- Big Data: Working with large and complex data sets and utilising big data technologies such as Hadoop and Spark to process and analyse them.
- Data storytelling: Communicating the insights and findings from the data in a way that is engaging and easy to understand for different audiences.
- Cloud migration and Data Engineering: Assisting organisations in moving their data to cloud-based platforms, and building data pipelines and workflows.