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Introduction to Data Mining and Analytics

• Understand the key concepts of data mining, including data exploration and analysis.

• Learn about the types of data (structured, unstructured) and the tools for data mining.

• Understand the life cycle of data mining projects: from data collection to deployment.

• Study the ethical considerations in data mining and data privacy.

Data Preprocessing and Preparation

• Learn techniques for cleaning, transforming, and normalizing data.

• Address missing data, outliers, and data inconsistencies.

• Implement feature engineering methods to improve model performance.

• Apply data reduction techniques, including dimensionality reduction and aggregation.

Statistical Methods and Machine Learning Algorithms

• Gain proficiency in statistical methods such as regression analysis and hypothesis testing.

• Understand supervised learning techniques: decision trees, regression models, and neural networks.

• Learn unsupervised learning methods: clustering (e.

, K-means) and association rules.

• Master classification techniques, including support vector machines (SVM) and random forests.

Data Mining Techniques and Applications

• Apply classification, regression, clustering, and association rule mining to real-world datasets.

• Learn how to build and evaluate predictive models for business applications.

• Implement data mining techniques for fraud detection, customer segmentation, and market basket analysis.

• Analyze time-series data and trends for forecasting and decision-making.

Big Data Analytics

• Understand the challenges and opportunities of working with big data.

• Learn tools for processing and analyzing large-scale datasets (e.

, Hadoop, Spark).

• Implement distributed data mining techniques for scalability.

• Explore cloud-based analytics platforms and their applications in big data.

Data Visualization and Interpretation

• Learn the importance of visualizing data and analysis results for decision-makers.

• Master tools like Tableau, Power BI, and Python libraries (Matplotlib, Seaborn) for visualization.

• Develop interactive dashboards to present key findings and actionable insights.

• Interpret and communicate data analysis results effectively to non-technical stakeholders.

Certificate

Upon full attendance of this training course, participants will receive an accredited certificate issued by Al Manhal Institute, which is licensed by the Abu Dhabi Center for Technical and Vocational Education and Training. This certificate is regionally recognized, providing holders with a competitive advantage both within their organizations and when seeking new career opportunities.

Introduction to Data Mining and Analytics

• Understand the key concepts of data mining, including data exploration and analysis.

• Learn about the types of data (structured, unstructured) and the tools for data mining.

• Understand the life cycle of data mining projects: from data collection to deployment.

• Study the ethical considerations in data mining and data privacy.

Statistical Methods and Machine Learning Algorithms

• Gain proficiency in statistical methods such as regression analysis and hypothesis testing.

• Understand supervised learning techniques: decision trees, regression models, and neural networks.

• Learn unsupervised learning methods: clustering (e.

, K-means) and association rules.

• Master classification techniques, including support vector machines (SVM) and random forests.

Data Mining Techniques and Applications

• Apply classification, regression, clustering, and association rule mining to real-world datasets.

• Learn how to build and evaluate predictive models for business applications.

• Implement data mining techniques for fraud detection, customer segmentation, and market basket analysis.

• Analyze time-series data and trends for forecasting and decision-making.

Big Data Analytics

• Understand the challenges and opportunities of working with big data.

• Learn tools for processing and analyzing large-scale datasets (e.

, Hadoop, Spark).

• Implement distributed data mining techniques for scalability.

• Explore cloud-based analytics platforms and their applications in big data.

Data Visualization and Interpretation

• Learn the importance of visualizing data and analysis results for decision-makers.

• Master tools like Tableau, Power BI, and Python libraries (Matplotlib, Seaborn) for visualization.

• Develop interactive dashboards to present key findings and actionable insights.

• Interpret and communicate data analysis results effectively to non-technical stakeholders.

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Schedule

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Data Mining & Analytics

Goals

Contents

Introduction to Data Mining and Analytics

Data Preprocessing and Preparation

Statistical Methods and Machine Learning Algorithms

Data Mining Techniques and Applications

Big Data Analytics

Data Visualization and Interpretation

Certificate

Certificate

Extra Options

Introduction to Data Mining and Analytics

Data Preprocessing and Preparation

Statistical Methods and Machine Learning Algorithms

Data Mining Techniques and Applications

Big Data Analytics

Data Visualization and Interpretation