Data Science for Executives Training Course

Explore practical methodologies for Data Science and Artificial Intelligence (AI) using Python. This course equips professionals with the essential skills to analyze data, develop machine learning models, and implement AI-driven solutions within business environments. Topics covered include CRISP-DM workflows, statistical analysis, supervised and unsupervised learning, deep learning with TensorFlow, natural language processing (NLP), big data processing with Spark, and data-driven storytelling. It is ideal for beginners looking to obtain a Python data science certification and acquire career-ready analytics training.

This instructor-led, live training in Nigeria (online or onsite) is designed for 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:

• Configure Apache Airflow to orchestrate machine learning workflows.
• Automate tasks related to data preprocessing, model training, and validation.
• Integrate Airflow with various machine learning frameworks and tools.
• Deploy machine learning models through automated pipelines.
• Monitor and optimize machine learning workflows in a production environment.

This instructor-led live training in Nigeria (online or onsite) 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 Nigeria (online or onsite) is tailored for intermediate-level data scientists and analysts looking to use AWS Cloud9 for efficient data science workflows.

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

• Establish a data science environment in AWS Cloud9.
• Conduct data analysis using Python, R, and Jupyter Notebook within Cloud9.
• Connect AWS Cloud9 with AWS data services such as S3, RDS, and Redshift.
• Use AWS Cloud9 for developing and deploying machine learning models.
• Enhance cloud-based workflows for efficient data analysis and processing.

This instructor-led, live training in Nigeria (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 designed for Marketing and Sales professionals who wish to deepen their understanding of applying data science within these domains. The curriculum offers comprehensive coverage of various data science techniques applied to 'upselling', 'cross-selling', market segmentation, branding, and Customer Lifetime Value (CLV).

Understanding the Distinction Between Marketing and Sales - How do sales and marketing differ?

In simple terms, sales is a process focused on targeting individuals or small groups. Marketing, on the other hand, aims at reaching a broader audience or the general public. Marketing encompasses research (identifying customer needs), product development (creating innovative products), and promotion (using advertisements to build product awareness among consumers). Essentially, marketing is about generating leads or prospects. Once the product is launched, it becomes the salesperson's role to persuade customers to make a purchase. While sales focuses on converting leads into orders and purchases, marketing is oriented toward long-term goals, whereas sales is often concerned with shorter-term objectives.

This instructor-led, live training in Nigeria (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 based on the Jupyter ecosystem.

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

• Install and configure Jupyter, including creating and integrating a team repository on Git.
• Utilize Jupyter features such as extensions, interactive widgets, and multiuser mode 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 Nigeria (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:

• Gain foundational knowledge of data science and machine learning.
• Explore the field of data analytics.
• Understand the Kaggle platform and its operational mechanics.

KNIME Analytics Platform stands as a premier open-source solution for driving data-led innovation. It empowers users to uncover hidden potential within their data, extract fresh insights, or forecast future trends. Equipped with over 1000 modules, numerous ready-to-use examples, a broad suite of integrated tools, and the most extensive selection of advanced algorithms, KNIME Analytics Platform serves as the ideal toolbox for data scientists and business analysts alike.

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

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

The course is tailored for individuals who may not have programming knowledge but intend to utilise cutting-edge tools to implement analytics scenarios.

By the conclusion 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 of data science models.

Format of the Course

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

Course Customization Options

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

This instructor-led, live training in Nigeria (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 Nigeria (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.

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 extremely popular in the financial sector. It is widely adopted by major investment banks and hedge funds to build various financial applications, from core trading systems to risk management tools.

Through this instructor-led live training, participants will learn how to use Python to develop 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 manage the best development tools for creating financial applications in Python
• Choose and apply the most suitable Python packages and programming techniques to organize, visualize, and analyze 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 optimize a Python application

Audience

• Developers
• Analysts
• Quants

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

Note

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

This instructor-led live training in Nigeria (online or onsite) is designed for data scientists and developers who want to use RAPIDS to build GPU-accelerated data pipelines, workflows, and visualizations, 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.