Data Science for Executives Training Course

This programme offers a hands-on exploration of Data Science and Artificial Intelligence through Python, empowering professionals with the capabilities to analyse data, develop machine learning models, and implement AI-driven solutions within commercial settings. The curriculum encompasses CRISP-DM methodologies, statistical analysis, supervised and unsupervised learning techniques, deep learning using TensorFlow, natural language processing, big data analytics via Spark, and data-driven storytelling. It is particularly suited for novices pursuing Python data science certification and practical analytics training to enhance career prospects.

This instructor-led live training in Botswana (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 training, facilitated by an instructor and available in Botswana (online or at your premises), is designed for data scientists who intend to leverage the Anaconda ecosystem to capture, manage, and deploy packages alongside data analysis workflows within a unified platform.

Upon completing this training, participants will be equipped to:

• Install and set up Anaconda components and libraries.
• Grasp the fundamental concepts, features, and advantages of Anaconda.
• Oversee packages, environments, and channels via Anaconda Navigator.
• Utilise Conda, R, and Python packages for data science and machine learning applications.
• Explore practical use cases and techniques for managing multiple data environments.

This instructor-led, live training in Botswana (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 Botswana (online or onsite) 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 specifically designed for marketing and sales professionals who wish to deepen their understanding of applying data science within their respective fields. It offers comprehensive coverage of various data science techniques applicable to upselling, cross-selling, market segmentation, branding, and Customer Lifetime Value (CLV).\n

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

Simply put, sales focuses on individuals or small groups, whereas marketing targets broader audiences or the general public. Marketing encompasses research (identifying customer needs), product development (creating innovative offerings), and promotion (using advertisements to build awareness). Essentially, marketing is about generating leads or prospects. Once a product reaches the market, the salesperson's role is to persuade customers to make a purchase. Sales aims to convert these leads into orders and purchases, typically with shorter-term goals, while marketing is oriented toward longer-term objectives.

This instructor-led, live training in Botswana (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.
• Use Jupyter features such as extensions, interactive widgets, multiuser mode and more to enable project collaboration.
• Create, share and organise Jupyter Notebooks with team members.
• Choose from Scala, Python, R, to write and execute code against big data systems such as Apache Spark, all through the Jupyter interface.

This instructor-led live training in Botswana (available online or onsite) is designed 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 driving data-led innovation. It empowers you to uncover hidden potential within your data, extract fresh insights, or forecast future trends. With over 1,000 modules, numerous ready-to-execute examples, a broad spectrum of integrated tools, and the most extensive selection of advanced algorithms available, KNIME Analytics Platform serves as the ideal toolkit for every data scientist and business analyst.

This course on KNIME Analytics Platform offers a valuable opportunity for beginners, advanced users, and KNIME experts alike to become familiar with the platform, enhance their proficiency, and learn how to produce clear and comprehensive reports using KNIME workflows.

This instructor-led live training, available online or onsite, is designed for data professionals aiming to leverage KNIME to address complex business requirements.

The programme targets individuals who may not have programming experience but wish to utilise state-of-the-art tools to implement analytical scenarios.

Upon completing this training, participants will be capable of:

• Installing and configuring KNIME.
• Developing Data Science scenarios.
• Training, testing, and validating models.
• Implementing the full value chain of data science models.

Format of the Course

• Interactive lectures and discussions.
• Extensive exercises and practice sessions.
• Practical implementation within a live lab environment.

Course Customization Options

• To request customised training for this course or to learn more about the programme, please contact us to make arrangements.

This instructor-led, live training in Botswana (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 Botswana (online or on-site) is aimed at beginner-level professionals who wish to understand the concept of pre-trained models and learn how to apply them to solve real-world problems without building models from scratch.

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

• Understand the concept and benefits 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 immensely popular within the financial sector. Adopted by major investment banks and hedge funds, it is used to develop a diverse array of financial applications, from core trading systems to risk management platforms.

In this instructor-led live training, participants will learn how to utilise Python to create practical applications that address specific financial challenges.

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

• Grasp the fundamentals of the Python programming language
• Download, install, and maintain the most suitable development tools for building financial applications in Python
• Select and apply appropriate Python packages and programming techniques to organise, visualise, and analyse financial data from various sources (CSV, Excel, databases, web, etc.)
• Build applications that solve problems 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 key problems faced by finance professionals. However, if there is a particular topic, tool, or technique you wish to add or elaborate on, please contact us to arrange.

This instructor-led, live training in Botswana (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, cuML, etc.

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.