Data Science for Executives Training Course

Explores practical methodologies for Data Science and AI utilizing Python, empowering professionals with the capabilities to analyse data, construct machine learning models, and implement AI-powered solutions within business environments; Addresses CRISP-DM workflows, statistical analysis, supervised and unsupervised learning, deep learning via Tensorflow, natural language processing, big data processing with Spark, and data-driven storytelling; Perfect for novices pursuing a Python data science certification and comprehensive analytics training to enhance career prospects.

This instructor-led live training in South Africa (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 instructor-led, live training in South Africa (online or on-site) is designed for data scientists who wish to use the Anaconda ecosystem to capture, manage, and deploy packages and data analysis workflows on a single platform.

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

• Install and configure Anaconda components and libraries.
• Understand the core concepts, features, and benefits of Anaconda.
• Manage packages, environments, and channels using Anaconda Navigator.
• Use Conda, R, and Python packages for data science and machine learning.
• Get to know some practical use cases and techniques for managing multiple data environments.

This instructor-led, live training in South Africa (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 South Africa (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 course is designed for marketing and sales professionals looking to deepen their understanding of how data science can be applied within these fields. It offers comprehensive coverage of various data science techniques applicable to upselling, cross-selling, market segmentation, branding, and Customer Lifetime Value (CLV).

Distinguishing Between Marketing and Sales - What sets sales apart from marketing?

In simple terms, sales is a process that focuses on individuals or small groups, whereas marketing targets a broader audience or the general public. Marketing involves identifying customer needs through research, developing innovative products, and promoting them via advertising to build consumer awareness. Essentially, marketing generates leads or prospects. Once a product is available in the market, it becomes the salesperson's responsibility to persuade customers to make a purchase. While marketing aims for long-term goals, sales focuses on shorter-term objectives by converting those leads into actual orders and revenue.

This instructor-led, live training in South Africa (online or onsite) introduces the concept of collaborative development in data science and demonstrates how to use Jupyter to track and participate as a team in the "life cycle of a computational idea". It guides participants through the creation of a sample data science project built on the Jupyter ecosystem.

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

• Install and configure Jupyter, including the creation and integration of a team repository on Git.
• Utilise Jupyter features such as extensions, interactive widgets, multiuser mode and more to facilitate project collaboration.
• Create, share and organise Jupyter Notebooks with team members.
• Choose from Scala, Python, or R to write and execute code against big data systems such as Apache Spark, all via the Jupyter interface.

This instructor-led live training in South Africa (online or onsite) is tailored for data scientists and developers who wish to learn and build their careers in Data Science using Kaggle.

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

• Learn about data science and machine learning.
• Explore data analytics.
• Learn about Kaggle and how it works.

KNIME Analytics Platform stands as a premier open-source solution for data-driven innovation, empowering you to uncover the latent potential within your data, extract fresh insights, or forecast future trends. Featuring over 1000 modules, hundreds of ready-to-run examples, a comprehensive suite of integrated tools, and the broadest selection of advanced algorithms, KNIME Analytics Platform serves as the ultimate toolbox for any data scientist or business analyst.

This course on KNIME Analytics Platform offers an ideal entry point for beginners, advanced users, and KNIME experts alike, introducing them to the platform, teaching them how to utilise it more effectively, and guiding them in creating clear, comprehensive reports based on KNIME workflows.

This instructor-led live training (available online or onsite) is designed for data professionals seeking to leverage KNIME to address complex business challenges.

The course targets individuals who may not have programming experience but wish to utilise cutting-edge tools to implement analytics scenarios.

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

• Install and configure KNIME.
• Build Data Science scenarios
• Train, test, and validate models
• Implement the end-to-end value chain of data science models

Format of the Course

• Interactive lecture and discussion.
• Ample exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request customized training for this course or to learn more about this programme, please contact us to arrange.

This instructor-led, live training in South Africa (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.

This instructor-led, live training in South Africa (online or onsite) is designed to equip participants with the skills needed to prepare for the Microsoft Certified: Data Scientist Associate certification exam (DP-100).

The programme provides a comprehensive overview of data science and machine learning fundamentals, demonstrating how these concepts can be applied using Microsoft Azure Machine Learning. Participants will gain practical experience in preparing data, constructing and training machine learning models, and deploying these models to the cloud. Additionally, the course addresses techniques for evaluating model performance and executing feature engineering.

This instructor-led, live training in South Africa (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.

Python has become increasingly popular within the financial sector, adopted by major investment banks and hedge funds to create a diverse array of financial applications, from core trading systems to risk management platforms.

In this instructor-led live training, participants will discover how to leverage Python to develop practical solutions for specific finance-related challenges.

Upon completion of this course, participants will be able to:

• Grasp the fundamentals of Python programming.
• Download, install, and manage the optimal development tools for building financial applications in Python.
• Select and employ the most appropriate Python packages and programming techniques to organise, visualise, and analyse financial data from various sources (such as CSV, Excel, databases, and web services).
• Develop applications that address issues related to asset allocation, risk analysis, investment performance, and more.
• Troubleshoot, integrate, deploy, and optimise a Python application.

Audience

• Developers
• Analysts
• Quants

Format of the course

• A blend of lectures, discussions, exercises, and extensive hands-on practice.

Note

• This training aims to provide solutions for some of the key problems faced by finance professionals. However, if you have a particular topic, tool, or technique that you wish to include or explore further, please contact us to arrange.

This instructor-led, live training in South Africa (online or onsite) is designed for data scientists and developers who wish to use RAPIDS to build GPU-accelerated data pipelines, workflows, and visualizations, applying machine learning algorithms such as XGBoost and cuML.

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

• Set up the necessary development environment to build data models with NVIDIA RAPIDS.
• Understand the features, components, and advantages of RAPIDS.
• Leverage GPUs to accelerate end-to-end data and analytics pipelines.
• Implement GPU-accelerated data preparation and ETL with cuDF and Apache Arrow.
• Learn how to perform machine learning tasks with XGBoost and cuML algorithms.
• Build data visualizations and execute graph analysis with cuXfilter and cuGraph.