WSQ Data science principles (SF) (Synchronous and Asynchronous E-Learning)
About This Course
The "Data Science Principles" module equips learners with essential knowledge and skills crucial for navigating the data-driven landscape. Covering a spectrum of topics, including Fundamentals of Data, Artificial Intelligence, Data Pre-processing, Introduction to Machine Learning, and Automated Machine Learning, this module lays a robust foundation for aspiring data scientists.
Through hands-on projects, learners will apply theoretical concepts to real-world scenarios. The module kicks off with the Implementation of data pre-processing and data cleaning on a dataset with low code solutions, providing practical insights into managing and refining raw data. Subsequently, participants leverage the pre-processed data asset to delve into the realm of Automated Machine Learning (AutoML) for a regression task. This hands-on experience reinforces the understanding of how automation can streamline complex machine-learning processes.
The culmination involves an exploration of various machine learning algorithms within the AutoML framework, followed by a comparative study of the models generated. This project not only sharpens technical proficiency but also cultivates the ability to assess and select optimal models for different scenarios. By the module's conclusion, learners will emerge with a comprehensive skill set encompassing data fundamentals, AI principles, pre-processing techniques, and practical experience in implementing automated machine learning solutions.
What You'll Learn
Duration of this course is 130.5
Instructional Units
1. Data Science and Storage Technologies
2. Artificial Intelligence Solutions
3. Data Preprocessing
4. Introduction to Machine Learning
5. Automated Machine Learning
Knowledge Outcomes:
Foundational Understanding of Data Science:
Participants will gain a solid understanding of data science fundamentals, including concepts related to data storage technologies, artificial intelligence solutions, and the principles of machine learning.
Proficiency in Data Preprocessing Techniques:
Learners will acquire knowledge of data preprocessing techniques essential for cleaning and refining raw data, including handling missing values, feature engineering, and utilizing automated solutions for data featurization.
Insight into Automated Machine Learning (AutoML):
Students will develop an understanding of Automated Machine Learning (AutoML) concepts and methodologies, including the process of automating model selection, hyperparameter tuning, and model evaluation for regression tasks.
Skills Outcomes:
Data Management and Storage Skills:
Participants will develop practical skills in managing and storing datasets using storage accounts and datastores, ensuring data accessibility and integrity throughout the project lifecycle.
Data Preprocessing Proficiency:
Learners will gain hands-on experience in cleaning and preprocessing datasets using Auto featurization techniques, enhancing their ability to prepare data for machine learning tasks effectively.
Automated Machine Learning Implementation:
Students will acquire skills in setting up project environments, provisioning Azure resources, and implementing Automated ML experiments for predictive tasks, enabling them to leverage automation for streamlined machine learning model development.
Entry Requirements
Academic Qualification: Minimum 1 credit in O Level or its equivalent
Experience: Minimum 1-year experience in any business process