Data Science for Executives Training Course

This course explores practical methodologies for Data Science and AI through Python, empowering professionals to analyze data, construct machine learning models, and implement AI-driven solutions within business environments. Topics include CRISP-DM workflows, statistical analysis, supervised and unsupervised learning, deep learning with TensorFlow, natural language processing, big data analytics using Spark, and data-driven storytelling. It is an ideal choice for beginners pursuing a Python data science certification and comprehensive career-focused analytics training.

This live, instructor-led training held in the UAE (online or onsite) is designed for intermediate-level participants who wish to automate and manage machine learning workflows, encompassing model training, validation, and deployment via 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 the UAE (online or onsite) is designed for data scientists aiming to leverage the Anaconda ecosystem to capture, manage, and deploy packages and data analysis workflows in a unified platform.

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

• Install and configure Anaconda components and libraries.
• Comprehend the core concepts, features, and advantages of Anaconda.
• Manage packages, environments, and channels using Anaconda Navigator.
• Utilize 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 the UAE (online or on-site) 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 the UAE (online or onsite) is targeted at beginner-level data scientists and IT professionals who intend 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 aiming to deepen their expertise in applying data science methodologies to these fields. It provides comprehensive coverage of various data science techniques utilized for upselling, cross-selling, market segmentation, branding, and Customer Lifetime Value (CLV).\n

The Distinction Between Marketing and Sales - What sets them apart?

In simple terms, sales targets individuals or small groups, whereas marketing addresses a broader audience or the general public. Marketing involves research to identify customer needs, product development for innovation, and promotion through advertising to build awareness. Essentially, marketing generates leads and prospects. Once a product is launched, the salesperson's role is to persuade these prospects to make a purchase. Thus, sales focuses on converting leads into orders with short-term goals, while marketing is oriented toward long-term strategy.

This instructor-led, live training in the UAE (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 the creation and integration of a team repository on Git.
• Leverage Jupyter features such as extensions, interactive widgets, multiuser mode, and more to facilitate project collaboration.
• Create, share, and organize Jupyter Notebooks with team members.
• Select from Scala, Python, or R to write and execute code against big data systems like Apache Spark, all within the Jupyter interface.

This instructor-led, live training in the UAE (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:

• Gain a solid understanding of data science and machine learning principles.
• Analyze data using advanced analytics techniques.
• Familiarize themselves with the Kaggle platform and its operational mechanisms.

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, and forecast future trends. Boasting over 1,000 modules, numerous ready-to-run examples, a comprehensive suite of integrated tools, and the broadest selection of advanced algorithms, KNIME Analytics Platform serves as the ideal toolkit for every data scientist and business analyst.

This course on KNIME Analytics Platform offers an excellent opportunity for beginners, advanced users, and KNIME experts to familiarize themselves with the platform, master its effective usage, and learn how to generate clear, comprehensive reports through 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 program targets participants who may not have programming experience 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

Format of the Course

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

Course Customization Options

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

This instructor-led, live training in the UAE (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 the UAE (online or onsite) is designed to help participants prepare for the Microsoft Certified: Data Scientist Associate certification exam (DP-100).

The course covers the basics of data science and machine learning, and how they can be implemented using Microsoft Azure Machine Learning. Participants will learn how to prepare data, build and train machine learning models, and deploy models to the cloud. The course also covers how to evaluate model performance and perform feature engineering.

This instructor-led, live training in the UAE (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 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 emerged as a dominant programming language within the financial sector. Widely adopted by leading investment banks and hedge funds, it is utilized to construct a diverse array of financial applications, from core trading platforms to sophisticated risk management systems.

During this instructor-led live training, participants will acquire the skills necessary to leverage Python for developing practical applications that address specific financial challenges.

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

• Grasp the fundamental concepts of the Python programming language
• Download, install, and maintain optimal development tools for building financial applications in Python
• Choose and employ the most appropriate Python packages and programming techniques to organize, visualize, and analyze financial data sourced from various channels (such as CSV, Excel, databases, and web interfaces)
• Develop applications that resolve issues related to asset allocation, risk analysis, investment performance, and other areas
• Troubleshoot, integrate, deploy, and optimize Python applications

Audience

• Developers
• Analysts
• Quantitative Analysts (Quants)

Format of the course

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

Note

• This training is designed to provide solutions for key challenges encountered by finance professionals. However, if there is a specific topic, tool, or technique you wish to include or explore in greater depth, please contact us to arrange for customization.

This instructor-led, live training in the UAE (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.

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

• Configure the required development environment to build data models using NVIDIA RAPIDS.
• Gain a comprehensive understanding of RAPIDS' features, components, and benefits.
• Utilize GPUs to speed up end-to-end data and analytics pipelines.
• Implement GPU-accelerated data preparation and ETL processes using cuDF and Apache Arrow.
• Master the execution of machine learning tasks using XGBoost and cuML algorithms.
• Create data visualizations and perform graph analysis with cuXfilter and cuGraph.