Data Science Foundations for IT Leaders
25/10/2024Mastering Power BI for Data Analytics
25/10/2024Data Science and Machine Learning Foundations
£4,000.00
Overview:
This training program provides participants with a comprehensive foundation in data science and machine learning, focusing on practical applications through hands-on exercises, real-world case studies, and end-to-end mini-projects. Covering essential topics in data processing, machine learning, model deployment, and data visualization, the program equips participants with the skills to handle data science workflows from data acquisition to deployment. Participants will also learn to communicate insights effectively and address ethical considerations in data science.
Program Objectives:
By the end of this training course, participants will be able to:
- Grasp essential data science and machine learning concepts and workflows.
- Use popular tools, including Python, Jupyter, and libraries like Pandas, NumPy, and Scikit-learn, for data handling and analysis.
- Build, evaluate, and deploy machine learning models for real-world applications.
- Integrate cloud-based data storage and retrieval in data science workflows.
- Communicate data insights effectively through visualization and storytelling.
- Understand and apply ethical considerations, addressing privacy, bias, and responsible data practices.
Target Audience:
- Data Analysts and Aspiring Data Scientists
- Business Analysts and IT Professionals transitioning into data science
- Data Engineers and Software Developers interested in expanding into data science roles
- Professionals in roles involving data-driven decision-making
Program Outline:
Day 1: Introduction to Data Science, Data Handling, and Cloud Integration
- Introduction to Data Science – Key Concepts, Industry Impact, and Role Definitions.
- The Data Science Process – From Data Acquisition to Deployment.
- Introduction to Cloud-Based Data Storage – Working with AWS S3, Google Cloud Storage, or similar platforms.
- Tools of the Trade – Python, Jupyter, and Foundational Libraries (Pandas, NumPy).
- Hands-On Activity: Setting Up Cloud Data Storage, Retrieving Data with Python, and Initial Data Cleaning.
- Reflection & Review: Group Discussion on Data Management Best Practices.
Day 2: Exploratory Data Analysis (EDA) and Data Visualization
- Data Types and Structures – Categorical, Numerical, and Time-Series Data.
- Exploratory Data Analysis (EDA) – Identifying Trends, Outliers, and Patterns.
- Data Visualization Principles and Tools – Matplotlib, Seaborn, and Tableau for Business Insights.
- Hands-On Session: Creating Visualizations and Conducting EDA on a Real-World Dataset.
- Reflection & Review: Interactive Feedback on EDA and Visualization Exercises.
Day 3: Machine Learning Basics and Mini Project
- Introduction to Machine Learning – Supervised vs. Unsupervised Learning and Applications.
- Building Simple Models – Regression and Classification Techniques.
- Mini Project Introduction – Working Through a Machine Learning Workflow on a Dataset.
- Hands-On Session: Building, Training, and Evaluating Basic Models Using Scikit-Learn.
- Reflection & Review: Reviewing Key Takeaways from the Mini Project.
Day 4: Advanced Machine Learning and Model Deployment
- Advanced Machine Learning Algorithms – Decision Trees, Random Forests, and Support Vector Machines.
- Deep Learning Basics – Introduction to Neural Networks and Deep Learning Applications.
- Model Evaluation and Tuning – Cross-Validation, Confusion Matrix, Precision, and Recall.
- Model Deployment – Overview of Deployment Options with Docker, Flask, and Cloud Platforms.
- Hands-On Practice: Building and Deploying a Model Using Scikit-Learn and Flask or a Cloud Platform.
- Reflection & Review: Feedback on Deployment Strategies and Fine-Tuning Techniques.
Day 5: Communicating Insights, Ethics, and Future Trends in Data Science
- Storytelling with Data – Best Practices for Presenting Insights to Non-Technical Audiences.
- Building Dashboards – Using Tableau or Power BI for Dynamic Visualization.
- Ethics and Responsible Data Use – Addressing Bias, Privacy, and Ethical Data Science Practices.
- Handling Real-Time Data – Overview of Real-Time Data Tools such as Kafka and Spark Streaming.
- Career Paths and Future Trends – AI, Deep Learning, and Other Emerging Technologies in Data Science.
- Capstone Project: Designing a Data Science Strategy Aligned with Business Goals, Including a Cloud-Enabled Data Pipeline.
- Reflection & Review: Project Presentations, Peer Feedback, and Discussion on Future Career Paths.