Data Science for Executives Training Course

This course provides a comprehensive exploration of practical Data Science and Artificial Intelligence techniques using Python. It empowers professionals with the essential skills to investigate data, construct machine learning models, and implement AI-driven solutions within business environments. The curriculum covers CRISP-DM methodologies, statistical analysis, supervised and unsupervised learning, deep learning with TensorFlow, natural language processing, big data processing with Spark, and data-driven storytelling. It is ideally suited for beginners pursuing a Python data science certification and those seeking career-ready analytics training.

This instructor-led, live training in Kenya (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 Kenya (online or onsite) is designed for data scientists who wish to utilize 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 Kenya (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 Kenya (available online or onsite) targets beginner-level data scientists and IT professionals who wish to learn the basics of data science using Google Colab.

By the conclusion 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 who wish to deepen their understanding of how to apply data science techniques within these fields. The curriculum provides a comprehensive exploration of various data science methods utilized for 'upselling,' 'cross-selling,' market segmentation, branding strategies, and Customer Lifetime Value (CLV) analysis.

Differentiating Marketing from Sales - What distinguishes sales from marketing?

Simply put, sales is a process that focuses on targeting individuals or small groups. Conversely, marketing aims at a broader audience or the general public. Marketing encompasses research (identifying customer needs), product development (creating innovative solutions), and promotion (via advertisements) to build brand awareness among consumers. Essentially, marketing is about generating leads or prospects. Once a product is launched, it becomes the salesperson's responsibility to persuade customers to make a purchase. Sales involves converting those leads into actual orders, whereas marketing is oriented toward long-term goals, while sales focuses on shorter-term objectives.

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

This instructor-led live training in Kenya (online or onsite) is aimed at 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.

The KNIME Analytics Platform stands out as a premier open-source solution for driving data-based innovation. It empowers users to uncover hidden potential within their data, extract fresh insights, or forecast future trends. Featuring over 1,000 modules, numerous ready-to-run examples, a wide array of integrated tools, and the most extensive selection of advanced algorithms, the KNIME Analytics Platform serves as the ideal toolkit for data scientists and business analysts.

This course on the KNIME Analytics Platform offers an excellent opportunity for beginners, advanced users, and KNIME experts to familiarize themselves with the platform, learn to utilize it more efficiently, and develop clear, comprehensive reports based on KNIME workflows.

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

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

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

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

Course Format

• Interactive lectures and discussions.
• Ample exercises and practice sessions.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• For those interested in requesting customized training for this course or seeking more information about the program, please get in touch with us to make arrangements.

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

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

• Comprehend the concept and advantages of pre-trained models.
• Examine various pre-trained model architectures and their use cases.
• Fine-tune a pre-trained model for specific tasks.
• Integrate pre-trained models into basic machine learning projects.

Python has become increasingly popular within the financial sector. It is widely adopted by major investment banks and hedge funds 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 leverage Python to create practical solutions for various financial challenges.

Upon completing this training, participants will be able to:

• Grasp the fundamental concepts of the Python programming language
• Download, install, and manage the most effective development tools for building financial applications in Python
• Choose and apply appropriate Python packages and programming techniques to organize, visualize, and analyze financial data from diverse sources such as CSV files, Excel spreadsheets, databases, and web interfaces
• Develop applications that address issues related to asset allocation, risk analysis, investment performance, and more
• Troubleshoot, integrate, deploy, and optimize Python applications

Audience

• Developers
• Analysts
• Quants

Format of the course

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

Note

• While this training focuses on providing solutions to key challenges faced by finance professionals, if you have specific topics, tools, or techniques you wish to explore in greater depth, please contact us to arrange customized content.

This instructor-led, live training in Kenya (online or onsite) targets data scientists and developers who want to use RAPIDS to create GPU-accelerated data pipelines, workflows, and visualizations, while applying machine learning algorithms like 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.