Azure Data Engineering Mastery: Real-World Projects
Why take this course?
-
Project Title: Predictive Maintenance for Manufacturing Machines
Project Description: In this project, you will build a machine learning model to predict machine failures in real-time, thereby enabling predictive maintenance and reducing downtime. You'll start by cleaning and preprocessing the dataset of sensor data collected from manufacturing machines. Then, you'll apply feature engineering techniques to extract meaningful information that can help in predicting anomalies or potential failures. Finally, you'll build a machine learning model using Azure Machine Learning and deploy it as a web service for real-time predictions. The insights gained from this project could save companies millions of dollars by preventing unexpected machine breakdowns.
Key Features:
- Data preprocessing and feature engineering with PySpark
- Model training and evaluation using Azure Machine Learning
- Deployment of the model as a web service for real-time predictions
- Integration with IoT devices for monitoring machinery
Target Audience:
- Data Scientists looking to apply machine learning in an industrial context
- Engineers interested in predictive maintenance and IoT applications
- Data Engineers who want to deploy models into production environments
Prerequisites:
- Knowledge of Python programming
- Basic understanding of machine learning concepts
- Familiarity with Azure Machine Learning service
What You'll Gain:
- Experience in preprocessing and feature engineering for predictive analytics
- Skills in training, evaluating, and deploying ML models on the cloud using Azure
- Insights into IoT integration and real-time data processing
-
Project Title: Climate Change Impact Analysis
Project Description: Climate change is one of the most pressing issues of our time. In this project, you will analyze climate change data to identify trends, patterns, and potential impacts on various aspects of life on Earth. By using satellite imagery and climate datasets from sources like NASA and NOAA, you'll perform exploratory data analysis, statistical modeling, and geospatial analysis to understand the effects of climate change on global temperatures, sea levels, biodiversity, etc. You will then visualize your findings to communicate the impact of climate change effectively.
Key Features:
- Data ingestion from various sources into Azure Data Lake Storage
- Exploratory data analysis and statistical modeling with PySpark and Jupyter Notebooks
- Geospatial analysis using Azure Maps
- Visualization of insights with Azure Synapse Link for Azure Cosmos DB
Target Audience:
- Data Analysts interested in environmental science
- Environmental Scientists who want to leverage cloud technologies for data analysis
- Anyone passionate about sustainability and the impact of climate change
Prerequisites:
- Basic understanding of climate change concepts
- Familiarity with data analysis and statistical modeling
- Experience with Azure services is a plus but not mandatory
What You'll Gain:
- Proficiency in handling large-scale environmental datasets
- Skills in conducting geospatial and statistical analyses in the cloud
- Understanding of the impact of climate change on various ecosystems and human society
-
Project Title: Stack Overflow Dataset Analysis
Project Description: The annual Developer Survey by Stack Overflow is a rich source of data about the software development community. In this project, you will analyze this dataset to understand the demographics of developers, their educational backgrounds, preferred learning resources, and coding preferences. By processing and visualizing the data within Azure Databricks, you'll be able to provide actionable insights that can help businesses and educators better target the developer community with resources, tools, and opportunities.
Key Features:
- Dynamic data factory pipeline for copying zipped survey data to a storage account
- Data extraction, pre-processing, and analysis within Databricks
- Development of comprehensive insights into the developer community
Target Audience:
- Data Engineers looking to work with large datasets on the cloud
- Business Analysts interested in insights about software developers
- Educators and institutions who want to tailor their offerings based on real data
Prerequisites:
- Basic understanding of Python programming
- Familiarity with data engineering concepts What You'll Gain:
- Experience with large-scale datasets
-
Project Title: Real Estate Market Analysis Project Description: The real estate market is complex, with a wide variety of factors affecting property values and trends. In this project, you will analyze real estate data to predict future trends in the housing market. By leveraging Azure services such as Azure Data Lake and Azure Synapse Analytics, you'll clean and preprocess large datasets. Then, using machine learning models, you'll forecast property prices and other key metrics. Key Features:
- Large-scale data ingestion into Azure Data Lake Storage
- Preprocessing and exploration of real estate datasets with PySpark and Jupyter Notebooks
- Machine Learning model building and evaluation within Azure Machine Learning environment Target Audience:
- Real Estate Market Analysts and Experts
- Entrepreneurs looking to start-ups in the real estate market analysis sector
- Data Science Enthusiasts with an interest in the real estate industry Prerequisites:
- Basic knowledge of Python programming language
- Familiarity with machine learning concepts and Azure cloud services
-
Project Title: Enhancing Patient Engagement through AI Solutions Project Description: In this project, you will explore the use of artificial intelligence (AI) solutions to enhance patient engagement within a healthcare ecosystem. By leveraging advanced analytics and machine learning capabilities available on Microsoft Azure Cloud Platform, you'll analyze patient data to identify trends and patterns in patient engagement behaviors. With the insights gained from this analysis, AI-driven personalized patient engagement strategies can be developed and deployed to improve overall patient satisfaction and experience within healthcare facilities. Key Features:
- Large-scale healthcare datasets ingestion and processing
- Advanced analytics and machine learning model building and evaluation on Microsoft Azure Cloud Platform
- Development and deployment of AI-driven personalized patient engagement strategies within healthcare ecosystems Target Audience:
- Healthcare Industry Professionals and Experts
- Data Science Enthusiasts with an interest in the healthcare industry and patient engagement solutions Prerequisites:
- Basic knowledge of Python programming language
- Familiarity with advanced analytics, machine learning concepts, and Microsoft Azure Cloud Platform services
In each of these projects, you'll be working with real-world data and applying cloud computing technologies from Microsoft Azure to analyze and uncover actionable insights that can drive informed decision-making. The skills and knowledge gained from successfully completing these projects will equip you with the tools and expertise required in today's data-driven, cloud-centric digital world.
Course Gallery
Loading charts...