Data Science Training in Durban

This instructor-led, live training in Durban (online or onsite) is designed for beginner-level professionals who wish to grasp the concept of pre-trained models and learn how to apply them to solve real-world problems without having to build models from scratch.

By the end of this training, participants will be able to:

• Comprehend the concept and advantages of pre-trained models.
• Explore various pre-trained model architectures and their use cases.
• Fine-tune a pre-trained model for specific tasks.
• Implement pre-trained models in simple machine learning projects.

This instructor-led, live training in Durban (online or onsite) is aimed at intermediate-level data scientists and analysts who wish to use AWS Cloud9 for streamlined data science workflows.

By the end of this training, participants will be able to:

• Set up a data science environment in AWS Cloud9.
• Perform data analysis using Python, R, and Jupyter Notebook in Cloud9.
• Integrate AWS Cloud9 with AWS data services like S3, RDS, and Redshift.
• Utilize AWS Cloud9 for machine learning model development and deployment.
• Optimize cloud-based workflows for data analysis and processing.

This instructor-led live training in Durban (online or onsite) is aimed at intermediate-level participants who wish to automate and manage machine learning workflows, including model training, validation, and deployment using Apache Airflow.

By the end of this training, participants will be able to:

• Set up Apache Airflow for machine learning workflow orchestration.
• Automate data preprocessing, model training, and validation tasks.
• Integrate Airflow with machine learning frameworks and tools.
• Deploy machine learning models using automated pipelines.
• Monitor and optimize machine learning workflows in production.

This live, instructor-led training programme in Durban (delivered online or on-site) is designed to support learners in preparing for the Microsoft Certified: Data Scientist Associate certification exam (DP-100).

The curriculum covers the essentials of data science and machine learning, illustrating how these disciplines can be applied using Microsoft Azure Machine Learning. Learners will develop the ability to prepare data, build and train machine learning models, and deploy models to the cloud. The programme also encompasses strategies for evaluating model performance and executing feature engineering.

This instructor-led live training in Durban (online or on-site) is aimed at beginner-level data scientists and IT professionals who wish to learn the basics of data science using Google Colab.

By the end of this training, participants will be able to:

• Set up and navigate Google Colab.
• Write and execute basic Python code.
• Import and handle datasets.
• Create visualizations using Python libraries.

This hands-on programme cultivates the skills necessary to analyse customer and operational data, decipher underlying patterns, forecast specific outcomes and convert evidence into actionable business recommendations. It addresses the demand for robust analytical capabilities by establishing accessible foundations before advancing to guided predictive analytics and continuous improvement strategies.

Participants navigate a cohesive customer-service case study: defining the business problem, curating the data, examining behavioural and performance trends, constructing a basic forecast or predictive model, and presenting a well-justified improvement proposal. The focus lies on selecting suitable methodologies and articulating results in clear business terms.

This is an ideal introduction to data science for managers, giving you the chance to learn about this powerful business tool.

This instructor-led, live training in Durban (online or onsite) is aimed at intermediate-level data analysts, developers, or aspiring data scientists who wish to apply machine learning techniques in Python to extract insights, make predictions, and automate data-driven decisions.

By the end of this course, participants will be able to:

• Understand and differentiate key machine learning paradigms.
• Explore data preprocessing techniques and model evaluation metrics.
• Apply machine learning algorithms to solve real-world data problems.
• Use Python libraries and Jupyter notebooks for hands-on development.
• Build models for prediction, classification, recommendation, and clustering.