Introduction to Data Science Training Course

This is a 5-day introductory course covering Data Science and Artificial Intelligence (AI).

The course is delivered with examples and exercises using Python

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 serves as an ideal introduction to data science for managers, offering a valuable opportunity to master this powerful business tool.

Upon completing this training, participants will acquire a hands-on, real-world comprehension of Data Science, along with its associated technologies, methodologies, and tools.

Learners will have the chance to apply their new knowledge through practical exercises. The course emphasizes group interaction and valuable feedback from the instructor as key components of the learning experience.

The curriculum begins by introducing foundational concepts of Data Science before advancing to the specific tools and methodologies employed in the field.

Target Audience

• Developers
• Technical Analysts
• IT Consultants

Course Format

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

Note

• To arrange customized training for this course, please contact us directly.

Big data refers to datasets that are excessively large and complex, rendering traditional data processing software inadequate. The challenges associated with big data encompass data capture, storage, analysis, search, sharing, transfer, visualization, querying, updating, and information privacy.

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.

In the initial phase of this training, we explore the core fundamentals of MATLAB, highlighting its role as both a programming language and a comprehensive platform. This section introduces MATLAB syntax, arrays and matrices, data visualization techniques, script development, and object-oriented principles.

The second phase demonstrates the application of MATLAB in data mining, machine learning, and predictive analytics. To offer participants a clear and practical understanding of MATLAB's capabilities and advantages, we draw comparisons between utilizing MATLAB and other tools such as spreadsheets, C, C++, and Visual Basic.

In the final phase, participants learn how to optimize their workflow by automating data processing and report generation tasks.

Throughout the course, participants will apply the concepts learned through practical exercises in a lab environment. By the end of the training, participants will have a comprehensive understanding of MATLAB's capabilities and will be able to leverage it to solve real-world data science problems and streamline their work through automation.

Assessments will be conducted throughout the course to monitor progress.

Course Format

• The course comprises theoretical and practical exercises, including case discussions, sample code analysis, and hands-on implementation.

Note

• Practice sessions will utilize pre-arranged sample data report templates. If you have specific requirements, please contact us to make arrangements.

This instructor-led, live training in the UAE (online or onsite) is designed for data scientists and developers who wish to use Modin to build and implement parallel computations with Pandas for faster data analysis.

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

• Set up the necessary environment to start developing Pandas workflows at scale with Modin.
• Understand the features, architecture, and advantages of Modin.
• Know the differences between Modin, Dask, and Ray.
• Perform Pandas operations faster with Modin.
• Implement the entire Pandas API and functions.

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.