Data Science for Executives Training Course

This five-day introduction to Data Science and Artificial Intelligence (AI) is designed for government professionals. The course includes practical examples and exercises using Python, ensuring that participants gain hands-on experience in applying these technologies within public sector workflows.

This instructor-led, live training (available 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: - Set up Apache Airflow for government use in orchestrating machine learning workflows. - Automate data preprocessing, model training, and validation tasks. - Integrate Airflow with various machine learning frameworks and tools. - Deploy machine learning models using automated pipelines. - Monitor and optimize machine learning workflows in a production environment.

This instructor-led, live training in US (online or onsite) is aimed at data scientists who wish to utilize the Anaconda ecosystem to manage and deploy packages and data analysis workflows within a single platform for government. 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 for government use. - Manage packages, environments, and channels using Anaconda Navigator. - Utilize Conda, R, and Python packages for data science and machine learning tasks. - Explore practical use cases and techniques for managing multiple data environments in a public sector context.

This instructor-led, live training (conducted online or on-site) is designed for intermediate-level data scientists and analysts who wish to leverage AWS Cloud9 for streamlined data science workflows. By the end of this training, participants will be able to: - Configure a data science environment in AWS Cloud9. - Conduct data analysis using Python, R, and Jupyter Notebook within Cloud9. - Integrate AWS Cloud9 with AWS data services such as S3, RDS, and Redshift. - Utilize AWS Cloud9 for the development and deployment of machine learning models. - Optimize cloud-based workflows for efficient data analysis and processing, ensuring alignment with public sector standards and governance practices for government.

This instructor-led, live training (conducted either online or onsite) is designed for government data scientists and IT professionals at the beginner level who wish to gain foundational knowledge in 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 manage datasets. - Create visualizations using Python libraries for government applications.

Participants who complete this training will acquire a practical, real-world understanding of Data Science and its associated technologies, methodologies, and tools. The training includes opportunities for participants to apply their knowledge through hands-on exercises. Group interaction and instructor feedback are integral components of the class. The course begins with an introduction to fundamental concepts in Data Science and then delves into the tools and methodologies used in the field. **Audience** - Developers - Technical Analysts - IT Consultants **Format of the Course for Government** - Part lecture, part discussion, exercises, and extensive hands-on practice **Note** To request a customized training for government for this course, please contact us to arrange.

Big data refers to datasets that are too large and complex for conventional data processing applications to manage effectively. Challenges associated with big data for government include the capture, storage, analysis, search, sharing, transfer, visualization, querying, updating, and ensuring the privacy of information. These issues require robust solutions that align with public sector workflows, governance, and accountability standards.

This course is designed for Marketing Sales Professionals who are looking to delve deeper into the application of data science in their fields. The curriculum provides comprehensive coverage of various data science techniques used for upsales, cross-sales, market segmentation, branding, and customer lifetime value (CLV). **Differences Between Marketing and Sales** - How do sales and marketing differ? In simple terms, sales can be described as a process that focuses on individuals or small groups. Conversely, marketing targets larger audiences or the general public. Marketing encompasses research to identify customer needs, product development to meet those needs, and promotion through advertisements to create awareness among consumers. Essentially, marketing generates leads or prospects. Once the product is available in the market, it is the responsibility of sales personnel to persuade customers to make a purchase. While sales are geared towards achieving short-term goals such as converting leads into purchases and orders, marketing strategies are typically aimed at longer-term objectives. This course is tailored to enhance the skills of professionals in both areas, ensuring they are well-equipped to leverage data science for government and private sector applications.

This instructor-led, live training (online or onsite) is designed for professionals who wish to embark on a career in Data Science. By the end of this training, participants will be able to: - Install and configure Python and MySQL. - Understand what Data Science is and how it can add value to any organization, including those in the public sector. - Learn the fundamentals of coding in Python. - Master supervised and unsupervised Machine Learning techniques, and understand how to implement them and interpret the results. ### Format of the Course - Interactive lecture and discussion. - Extensive exercises and practice sessions. - Hands-on implementation in a live-lab environment. ### Course Customization Options - To request a customized training for government or other specific needs, please contact us to arrange.

This instructor-led, live training in US (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." The training guides participants through the creation of a sample data science project using the Jupyter ecosystem. By the end of this training, participants will be able to: - Install and configure Jupyter, including setting up and integrating 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. - Select from languages like Scala, Python, or R to write and execute code against big data systems such as Apache Spark, all through the Jupyter interface. This training is designed to enhance teamwork and efficiency in data science projects for government agencies and other public sector organizations.

This instructor-led, live training in [location] (online or onsite) is aimed at data scientists and developers who wish to advance their careers in Data Science using Kaggle. By the end of this training, participants will be able to: - Gain a comprehensive understanding of data science and machine learning. - Explore various techniques in data analytics. - Learn about Kaggle and its functionalities, specifically tailored for government applications.

The KNIME Analytics Platform is a premier open-source solution for data-driven innovation, enabling users to uncover the potential within their data, extract valuable insights, and forecast future trends. Featuring over 1,000 modules, hundreds of ready-to-run examples, an extensive array of integrated tools, and a broad selection of advanced algorithms, KNIME Analytics Platform is an indispensable resource for any data scientist or business analyst.

This training course for the KNIME Analytics Platform provides an excellent opportunity for beginners, advanced users, and KNIME experts to familiarize themselves with KNIME, enhance their usage proficiency, and create clear, comprehensive reports based on KNIME workflows.

This instructor-led, live training (online or onsite) is designed for data professionals who wish to leverage KNIME to address complex business challenges.

It is particularly suitable for individuals without a programming background who aim to use advanced tools to implement sophisticated analytics scenarios.

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

• Install and configure KNIME.
• Develop data science scenarios.
• Train, test, and validate models.
• Implement the entire value chain of 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 a customized training for government or to learn more about this program, please contact us to arrange.

In the first part of this training, we cover the fundamentals of MATLAB and its role as both a language and a platform for government. This discussion includes an introduction to MATLAB syntax, arrays and matrices, data visualization, script development, and object-oriented principles.

In the second part, we demonstrate how to use MATLAB for data mining, machine learning, and predictive analytics in a government context. To provide participants with a clear and practical perspective of MATLAB's approach and power, we draw comparisons between using MATLAB and other tools such as spreadsheets, C, C++, and Visual Basic.

In the third part of the training, participants learn how to streamline their work by automating data processing and report generation for government tasks.

Throughout the course, participants will apply the concepts learned through hands-on exercises in a lab environment. By the end of the training, participants will have a thorough understanding of MATLAB's capabilities and will be able to employ it for solving real-world data science problems as well as for streamlining their work through automation for government.

Assessments will be conducted throughout the course to gauge progress.

Format of the Course

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

Note

• Practice sessions will be based on pre-arranged sample data report templates. If you have specific requirements for government use cases, please contact us to arrange.

This instructor-led, live training in US (online or onsite) is aimed at 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 for government applications. - Differentiate between Modin, Dask, and Ray in terms of their suitability for various data processing tasks. - Perform Pandas operations more efficiently using Modin. - Implement the entire Pandas API and functions to enhance data analysis capabilities.

This instructor-led, live training in [location] (online or onsite) is aimed at 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. 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 for government applications. - Leverage GPUs to accelerate end-to-end data and analytics pipelines. - Implement GPU-accelerated data preparation and ETL processes using 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.