Designing and Implementing a Data Science Solution on Azure

Course Overview

The “Designing and Implementing a Data Science Solution on Azure” course is designed for individuals and professionals seeking to gain a comprehensive understanding of how to leverage Microsoft Azure for data science projects.

Throughout this course, participants will explore the complete data science lifecycle, from data collection and preparation to model development and deployment.
In addition to technical skills, this course emphasizes best practices in project design, data security, and ethical considerations relevant to data science.

Launch your career in Cloud by developing in-demand skills and become job-ready in 30 hours or less.

Highlights

Upgrade your career with top notch training

• Enhance Your Skills: Gain invaluable training that prepares you for success.
• Instructor-Led Training: Engage in interactive sessions that include hands-on exercises for practical experience.
• Flexible Online Format: Participate in the course from the comfort of your home or office.
• Accessible Learning Platform: Access course content on any device through our Learning Management System (LMS).
• Flexible Schedule: Enjoy a schedule that accommodates your personal and professional commitments.
• Job Assistance: Benefit from comprehensive support, including resume preparation and mock interviews to help you secure a position in the industry.

Outcomes

By the end of this course, participants will be equipped with:

• Essentials of Data Science: Gain foundational knowledge of data science concepts, including data collection, data analysis, machine learning, and data visualization.
• Proficiency in Azure Data Services: Explore Microsoft Azure’s various data services, including Azure Machine Learning, Azure Databricks, and Azure SQL Database, and understand how to leverage these tools for data science projects.
• Data Preparation and Cleaning: Develop skills in data preprocessing, including data cleaning, transformation, and normalization using Azure Data Factory and other Azure services, ensuring high-quality data input for models.
• Model Development and Training: Understand how to build, train, and evaluate machine learning models with Azure Machine Learning, applying different algorithms based on project requirements.
• Implementation of Machine Learning Solutions: Gain practical experience in implementing machine learning solutions, deploying models to production, and ensuring they can scale effectively on Azure.
• Data Visualization Techniques: Learn to create impactful data visualizations using tools such as Power BI and Matplotlib to communicate findings and insights effectively.
• Integrating AI Capabilities: Understand how to incorporate Azure Cognitive Services into data science solutions for enhanced functionalities like image recognition, natural language processing, and anomaly detection.
• Best Practices for Project Design: Explore best practices for designing data science solutions, including defining project scopes, understanding user requirements, and documenting processes to ensure alignment with business objectives.
• Understanding Security and Compliance: Gain insights into Azure’s security features, best practices for protecting data, and compliance requirements that organizations must adhere to when working with data.
• Career Preparedness in Data Science: Equip yourself with the skills and knowledge required to pursue entry-level roles in data science, data analysis, or machine learning engineering, enhancing your career opportunities in a rapidly growing field.

Key Learnings

• Gain insights into the core principles of data science, including data exploration, data cleaning, statistical analysis, and the role of machine learning.
• Learn how to navigate the Azure platform and utilize various Azure services, such as Azure Machine Learning, Azure Data Factory, and Azure Databricks, for data science applications.
• Master data preprocessing techniques including data collection, cleaning, transformation, and normalization, ensuring that high-quality data is used for model training.
• Understand how to build, train, evaluate, and deploy machine learning models using Azure Machine Learning, including supervised and unsupervised learning approaches.
• Learn to integrate Azure Cognitive Services (e.g., Text Analytics, Computer Vision) into data science solutions to add advanced capabilities such as natural language processing and image recognition.
• Develop skills to create meaningful data visualizations using tools like Power BI and Matplotlib, enabling effective communication of data insights to stakeholders.
• Explore best practices for designing data science projects, including defining project scopes, understanding stakeholder requirements, and ensuring alignment with business objectives.

Pre-requisites 

• Familiarity with Data Science Concepts: A basic understanding of data science principles, and programming using Python is recommended.
• Introduction to Microsoft Azure: While prior experience with Azure is not mandatory, having a foundational knowledge of Azure’s services will enhance the learning experience. Completion of a course like “Azure Fundamentals” may be helpful.

Job roles and career paths 

• This training will equip you for the following job roles and career paths:
• Data Scientist
• Machine Learning Engineer
• AI Solutions Architect
• Cloud Data Engineer
• Business Intelligence Analyst
• Data Engineer

Designing and Implementing a Data Science Solution on Azure

The demand for data science professionals is very high and is increasing steadily. Many companies across different industries are looking for experts who can analyze and interpret data to help them make better decisions. This is because data is crucial for understanding market trends, improving products, and driving business strategies.

Skills such as handling large datasets, applying statistical methods, and using machine learning to build predictive models are especially valuable. As businesses continue to rely on data to stay competitive and innovative, the need for skilled data scientists who can turn complex data into actionable insights remains strong and is expected to keep growing.