WSQ Data Science Essentials (SF) (Synchronous and Asynchronous E-Learning)
About This Course
The "Data Science Essentials" module equips learners with the essential knowledge and skills needed to excel in the realm of data science, specifically focusing on Azure Machine Learning. Through a series of Instructional Units (IU), participants will delve into foundational concepts and practical applications.
In the initial learning units, participants will receive a comprehensive introduction to data science and the utilization of Azure Machine Learning for model development. The module guides learners through the intricacies of working with data and compute resources, laying a solid foundation for effective dataset processing and management.
The module progresses to training models using scripts, ensuring learners acquire the scripting proficiency necessary for successful model development. Emphasis is placed on optimizing model training, honing the learners' ability to fine-tune machine learning algorithms for superior predictive analytics outcomes.
Furthermore, the module explores the critical aspects of managing and deploying models, emphasizing the seamless integration of data science solutions into real-world scenarios. Through hands-on projects utilizing Microsoft Azure Machine Learning, participants apply their knowledge to develop end-to-end data science projects, covering pre-processing, training, testing, optimization, and deployment.
By the conclusion of the module, learners will have gained a profound understanding of data science essentials and Azure Machine Learning, showcasing proficiency in handling diverse datasets, scripting for model training, and effectively deploying models. The hands-on projects will empower participants to translate theoretical knowledge into practical solutions, ensuring they are well-prepared for the dynamic field of data science. This module equips learners with versatile skills and a deep understanding of essential concepts, enabling them to contribute meaningfully to the evolving landscape of data science.
What You'll Learn
This course comprises of following Instructional Units
1. Introduction to Data Science and Azure Machine Learning
2. Work with Data and Compute
3. Train models with scripts.
4. Optimize model training.
5. Manage and deploy models.
Knowledge Outcomes
Identify data validation techniques and their applications in ensuring dataset quality and accuracy.
Apply preprocessing steps to transform raw data, enhancing its readiness for analysis and modeling.
Demonstrate data transfer methodologies to maintain data integrity and consistency across diverse sources.
Utilize machine learning algorithms to model and analyze data, extracting meaningful insights.
Implement Azure-based deployment for real-time predictions and analysis of data science models
Skills outcomes
Proficiently apply data validation techniques, ensuring data quality for accurate analysis.
Skillfully execute data preprocessing, optimizing data suitability for subsequent modeling steps.
Efficiently manage data transfer across sources, maintaining data integrity throughout the process.
Capably employ machine learning algorithms, deriving insights and making informed decisions from data.
Independently deploy Azure models, enabling real-time predictions and analysis for business applications.
Entry Requirements
Academic Qualification - Minimum one credit in O level
Knowledge & Skill requirement: Logical thinking skills