Tank & Tank Farms Design, Installation, Operation, Maintenance & Troubleshooting
25/10/2024Data Science and Machine Learning Foundations
25/10/2024Data Science Foundations for IT Leaders
£4,000.00
Overview:
This program introduces IT leaders and professionals to the foundational concepts and practical applications of data science. It focuses on equipping participants with both technical skills and strategic insights for managing and implementing data science initiatives within an organization. Covering essential data science techniques, data governance, cloud integration, and advanced analytics, this course prepares participants to leverage data-driven insights in decision-making, improve business outcomes, and align data projects with organizational goals. Hands-on activities and real-world case studies ensure practical skill development.
Program Objectives:
By the end of this program, participants will be able to:
- Understand core principles, methodologies, and tools in data science, including Python, SQL, and cloud-based platforms.
- Conduct data cleaning, exploratory data analysis (EDA), and visualization to uncover trends and inform decisions.
- Develop and implement predictive models using machine learning algorithms suited to business applications.
- Build and manage data pipelines on cloud platforms for scalable data processing.
- Apply data governance frameworks to ensure data privacy, security, and compliance with regulatory standards.
- Communicate data insights effectively to both technical and non-technical stakeholders.
- Align data science projects with organizational strategies, ensuring data-driven solutions support business goals.
Target Audience:
- IT Leaders and Managers overseeing data science and analytics initiatives
- Aspiring Data Scientists, Data Analysts, and Data Engineers
- Business Analysts seeking to enhance data-driven decision-making
- Technical Managers interested in applying data science for operational improvements
Program Outline:
Day 1: Data Science Fundamentals and Data Preparation
- Introduction to Data Science and Its Role in IT Management.
- Data Science Lifecycle – Phases from Collection to Analysis and Deployment.
- Data Preparation – Data Cleaning, Handling Missing Values, Data Transformation, and Preprocessing.
- Key Tools and Languages for Data Science – Overview of Python, SQL, and Jupyter Notebooks.
- Hands-On Activity: Data Cleaning and Preparation Using Python (Pandas).
- Reflection & Review: Group Discussion on Data Quality’s Impact on Business Insights.
Day 2: Data Analysis, Visualization, and Feature Engineering
- Exploratory Data Analysis (EDA) – Techniques to Identify Patterns, Trends, and Anomalies.
- Data Visualization Tools and Techniques – Matplotlib, Seaborn, and Tableau for Business Reporting.
- Feature Engineering – Creating and Selecting Relevant Features to Enhance Model Performance.
- Hands-On Exercise: Building Interactive Dashboards and Visualizations for Data Communication.
- Reflection & Review: Discussing Visualization’s Role in Stakeholder Decision-Making.
Day 3: Statistical Analysis and Predictive Modeling
- Statistical Fundamentals – Probability, Hypothesis Testing, and Statistical Inference.
- Predictive Modeling Techniques – Regression Analysis for Forecasting and Decision Support.
- Classification Techniques – Logistic Regression, Decision Trees, and Applications in Business.
- Advanced Feature Engineering – Techniques for Feature Transformation and Selection.
- Hands-On Activity: Applying Statistical Models and Machine Learning Techniques to Real-World Scenarios.
- Reflection & Review: Discussion on How Predictive Modeling Can Address Business Challenges.
Day 4: Machine Learning, Big Data, and Cloud Integration
- Introduction to Machine Learning – Key Algorithms and Business Use Cases.
- Model Evaluation Techniques – Cross-Validation, Confusion Matrix, Precision, and Recall.
- Big Data Tools – Overview of Hadoop, Spark, and Their Applications in Large-Scale Data Processing.
- Cloud Platforms for Data Science – AWS, Google Cloud, and Azure for Scalable Analytics and Storage.
- Hands-On Practice: Building and Evaluating Machine Learning Models on Cloud-Based Platforms.
- Reflection & Review: Examining the Benefits of Cloud Integration and Big Data Processing for IT Operations.
Day 5: Data Governance, Ethics, and Capstone Project
- Data Governance Principles – Ensuring Data Privacy, Security, and Compliance.
- Ethics in Data Science – Addressing Bias, Privacy, and Ethical Data Use.
- Communicating Insights and Storytelling with Data – Effective Presentation Techniques for Stakeholders.
- Strategic Alignment – Integrating Data Science Projects with Business Objectives.
- Capstone Project: Designing an End-to-End Data Science Strategy, Including Data Preparation, Modeling, and a Cloud-Enabled Data Pipeline.
- Reflection & Review: Project Presentations, Peer Feedback, and Discussion on Future Industry Trends.