Introduction to Data Science 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 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.

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.

The unprecedented growth in information and data in today’s world is driving rapid innovation and pushing the boundaries of what is possible. The role of Data Scientist is one of the most in-demand skills across industries.

We offer more than just theoretical learning; we provide practical, marketable skills that bridge the gap between academia and industry demands.

This 7-week curriculum can be tailored to meet specific requirements for government or other sectors. For further information, please contact us or visit the Govtra Institute website.

Audience:

This program is designed for post-graduate level individuals as well as those with the necessary prerequisite skills, which will be determined through an assessment and interview process.

Delivery:

The course delivery will combine Instructor-Led Classroom sessions and Instructor-Led Online sessions. Typically, the first week will be classroom-led, weeks 2 to 6 will be virtual classroom sessions, and the final week will return to a classroom-led format.

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 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.

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 designed for intermediate-level data analysts, developers, and aspiring data scientists who seek to apply machine learning techniques using Python to extract insights, make predictions, and automate data-driven decisions for government. 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 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.

Python is a programming language that has gained significant popularity in the financial sector. Adopted by major investment banks and hedge funds, it is used to develop a wide array of financial applications, from core trading systems to risk management platforms.

In this instructor-led, live training, participants will learn how to use Python to develop practical applications for solving a variety of finance-related challenges.

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

• Understand the fundamentals of the Python programming language
• Download, install, and maintain the best development tools for creating financial applications in Python
• Select and utilize the most appropriate Python packages and programming techniques to organize, visualize, and analyze financial data from various sources (CSV, Excel, databases, web, etc.)
• Build applications that address issues 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 extensive hands-on practice

Note

• This training is designed to provide solutions for some of the primary challenges faced by finance professionals. If you have a specific topic, tool, or technique that you would like to cover or expand upon, please contact us to arrange.

Govtra offers this course for government personnel and organizations looking to enhance their financial technology capabilities and align with public sector workflows, governance, and accountability standards.

The training course is designed to assist participants in preparing for web application development using Python programming, along with data analytics. This skill set enhances data visualization capabilities, which are invaluable tools for top management in the decision-making process for government and other public sector organizations.

This instructor-led, live training in [location] (online or onsite) is designed for government data analysts and web developers who wish to develop associative models using Qlik Sense. By the end of this training, participants will be able to: - Apply Qlik Sense in data science for government. - Navigate and utilize the Qlik Sense interface effectively. - Build a data-literate workforce with AI interaction. - Create a data-driven enterprise with Qlik Sense.

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.