IT technical & project delivery

Data Science and Analytics

Here are some of the ways our clients are leveraging data science and analytics to improve and optimise their operations, drive business efficiencies, and mitigate risk:

Predictive Maintenance: Using data to predict equipment failures before they occur, thereby reducing downtime and maintenance costs.

Geospatial Analysis: Integrating geological and spatial data to optimise exploration and extraction processes. This includes GIS (Geographic Information Systems) skills and remote sensing techniques.

Machine Learning in Exploration: Utilising machine learning algorithms to analyse geological data and identify promising mining sites or trends.

Environmental Monitoring: Using data analytics to monitor and manage environmental impacts, ensuring compliance with regulations and minimising ecological footprints.

Supply Chain Analytics: Optimising supply chain management through data-driven insights, improving procurement efficiencies and reducing costs.

Safety Analytics: Applying data science to improve safety protocols and practices, including predictive analytics for identifying and mitigating potential hazards.

Big Data Handling: Managing large volumes of data generated from sensors, IoT devices, and operational databases, ensuring data quality and scalability.

Data Visualisation: Communicating insights effectively through visualisation tools and techniques, aiding decision-making across different levels of the organisation.

Regulatory Compliance and Risk Management: Using data analytics to navigate regulatory requirements, assess risks, and implement strategies to mitigate them.

Here are some of the key skillsets we help our clients to find:

Programming Languages:

• Python: Essential for data analysis, machine learning, and automation.
• R: Widely used for statistical analysis and data visualisation.
• SQL: Crucial for querying and managing databases.
• Java/Scala: Important for big data technologies and data engineering tasks.

Data Manipulation and Analysis:

• Pandas: Key Python library for data manipulation and analysis.
• NumPy: Fundamental for numerical computations in Python.
• Dplyr/Tidyverse (R): For data manipulation and visualisation in R.

Machine Learning and Deep Learning:

• Scikit-learn: Popular machine learning library in Python.
• TensorFlow/Keras/PyTorch: Leading frameworks for deep learning.
• XGBoost/LightGBM: For gradient boosting algorithms.

Data Visualisation:

• Matplotlib/Seaborn: Python libraries for creating static, animated, and interactive visualisations.
• ggplot2: R package for data visualisation.
• Tableau/Power BI: Tools for interactive data visualisation and business intelligence.

Big Data Technologies:

• Hadoop/Spark: Essential for processing and analysing large datasets.
• Kafka: Used for real-time data streaming.

Database Management:

• NoSQL Databases: MongoDB, Cassandra for handling unstructured data.
• Relational Databases: MySQL, PostgreSQL for structured data management.

Cloud Computing:

• AWS/GCP/Azure: Proficiency in cloud platforms for deploying data solutions.

GIS (Geographic Information Systems)

Some of our clients operate within the following industries where GIS is becoming increasingly crucial to their success:

Mining, Environmental and Natural Resources

• Applying GIS to manage and analyse natural resources.

Urban Planning and Development

• Using GIS for city planning, infrastructure development, and smart cities.

Transportation and Logistics

• Optimising routes, managing transportation networks, and logistics planning with GIS.

Public Health

• Analysing spatial patterns in health data to inform public health decisions.

GIS is a rapidly growing field, and several skills are in high demand. Here are some of the top GIS skills that employers are looking for:

Technical Skills

Proficiency with GIS Software

• Esri ArcGIS: The most widely used GIS software.
• QGIS: An open-source alternative to ArcGIS.

Database Management

• SQL: For querying and managing spatial databases.
• Geo databases: Knowledge of managing spatial data in formats like Esri Geo database.

Spatial Analysis

• Techniques for analysing spatial data to uncover patterns, relationships, and trends.

Programming and Scripting

• Python: Widely used for automation and customisation in GIS.
• R: For spatial data analysis and visualisation.
• JavaScript: For web mapping and development using libraries like Leaflet or APIs like Google Maps.

Remote Sensing

• Techniques for acquiring and analysing satellite and aerial imagery.

Data Visualisation

• Creating meaningful visual representations of spatial data using tools like Tableau or specialised GIS software.

Cartography

• Designing and creating maps that effectively communicate information.

Specialist Skills

Geospatial Data Science

• Integrating GIS with data science techniques for advanced analysis.

Web GIS Development

• Building and maintaining interactive web maps and GIS applications.

3D GIS

• Working with 3D spatial data and visualisations, often for urban planning and architecture.

Mobile GIS

• Using GIS on mobile devices for field data collection and real-time updates.

We will also support you in recruiting permanent and contract IT technical & project delivery staff. We currently support clients ranging from global technology firms through to consultancies and end users. We can help you build out your team of:

• Data Engineers / Data Scientists / Business Intelligence Developers / Data Analysts
• Security Architects / Security Engineers / Network Security Architects
• AI Engineers / ML Engineers / Robotics Engineers
• Software Developers / Software Engineers / Software Architects / UI & UX designers
• Product Managers / Product Developers / Scrum Masters / Agile Consultants
• Software Testers / Test Engineers / Test Analysts / Test Managers
• PMO / Program Managers / Project Managers / Implementation Managers / Business Analysts