Data Mining and Warehousing
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Course Overview
The Data Mining and Warehousing program provides a comprehensive understanding of how organizations handle vast amounts of data, extract meaningful patterns, and utilize data warehouses for business intelligence. Participants will learn about data preprocessing, storage architectures, mining algorithms, and the latest advancements in big data analytics. This program combines theoretical concepts with hands-on practice to help professionals enhance their data management and analytical skills.
Program Syllabus
Module 1
About
The Data Mining and Warehousing program provides a comprehensive understanding of how organizations handle vast amounts of data, extract meaningful patterns, and utilize data warehouses for business intelligence. Participants will learn about data preprocessing, storage architectures, mining algorithms, and the latest advancements in big data analytics. This program combines theoretical concepts with hands-on practice to help professionals enhance their data management and analytical skills.
Module 2
Aim
To equip participants with the knowledge and skills needed to collect, store, manage, and analyze large-scale data using data mining and data warehousing techniques for informed decision-making.
Module 3
Lead Instructor
Dr. Sarah Chen
PhD in Computational Mechanics from MIT with 15+ years of experience in Industrial AI. Former Lead Data Scientist at Tesla and current advisor to Fortune 500 manufacturing firms.
Limited SeatsClosing Soon
Data Mining and Warehousing
Professional Certification Program
🎥
FormatLive + Recorded
📅
Duration8 Weeks
📜
CertificationVerified
Program Objectives
- To introduce participants to the fundamentals of data warehousing and data mining.
- To provide hands-on training in designing and managing data warehouses.
- To explore data mining techniques for extracting actionable insights.
- To discuss real-world applications and case studies in business intelligence.
- To address ethical, privacy, and security concerns in data management.
Module 4
Program Structure
Module 5
Module 1: Introduction to Data Mining
- What is Data Mining?
- Definition and goals of data mining.
- Key stages in data mining: Data cleaning, transformation, modeling, and evaluation.
- Types of Data Mining Tasks
- Classification, clustering, regression, association, and anomaly detection.
- Real-world applications of each task type.
- Data Mining Process
- Data collection, cleaning, and preparation.
- Choosing the right technique based on the problem at hand.
Module 6
Module 1: Introduction to Data Mining
- What is Data Mining?
- Definition and goals of data mining.
- Key stages in data mining: Data cleaning, transformation, modeling, and evaluation.
- Types of Data Mining Tasks
- Classification, clustering, regression, association, and anomaly detection.
- Real-world applications of each task type.
- Data Mining Process
- Data collection, cleaning, and preparation.
- Choosing the right technique based on the problem at hand.
Module 7
Module 2: Introduction to Data Warehousing
- What is Data Warehousing?
- Definition and key components: Data warehouse, ETL, OLAP, and Data Marts.
- Architecture of a data warehouse: Staging, warehouse, and data marts.
- Data Warehouse vs Operational Databases
- Differences in design, purpose, and usage.
- Why we need data warehouses in decision support systems.
- Business Intelligence and Data Warehousing
- Role of data warehousing in BI.
- How data warehousing enables analytics and reporting.
Module 8
Module 3: Data Mining vs Data Warehousing
- Integration of Data Mining and Data Warehousing
- How data mining and warehousing complement each other.
- Case studies showing integration for effective business intelligence.
- Challenges and solutions in data mining and warehousing integration.
Module 9
Assignments and Labs
- Assignment: Data collection and cleaning exercise.
- Lab: Data mining task using Python/R: Basic data analysis.
Module 10
Module 4: Data Mining Techniques
- Classification
- Supervised learning techniques: Decision trees, Naive Bayes, and k-NN.
- Evaluating classification models: Accuracy, precision, recall, F1-score.
- Clustering
- Unsupervised learning techniques: K-means, hierarchical clustering, and DBSCAN.
- Evaluating clustering models: Silhouette score, Davies-Bouldin index.
- Association Rule Mining
- Concepts: Frequent itemsets, association rules, support, confidence, lift.
- Algorithms: Apriori and FP-growth.
- Regression
- Linear regression, logistic regression, and other regression models.
- Evaluating regression models: Mean Squared Error (MSE), R-squared.
Module 11
Module 5: Advanced Data Warehousing Concepts
- Data Warehouse Design Principles
- Star and snowflake schemas.
- Fact and dimension tables: Designing the relational model.
- Normalization vs denormalization in a data warehouse.
- ETL Process
- Extract, Transform, and Load: Overview of the process.
- Best practices for ETL: Data cleansing, integration, and transformation.
- Tools and platforms for ETL: Talend, Apache Nifi, Microsoft SSIS.
- OLAP (Online Analytical Processing)
- OLAP cubes and multidimensional data models.
- Types of OLAP: MOLAP, ROLAP, and HOLAP.
- OLAP queries and performance optimization.
Module 12
Module 6: Data Warehousing in Cloud
- Cloud Data Warehousing
- Introduction to cloud-based data warehouses: Amazon Redshift, Google BigQuery, Snowflake.
- Benefits and challenges of cloud data warehousing.
- Real-time data warehousing in cloud environments.
Module 13
Assignments and Labs
- Assignment: Implement a classification model using a sample dataset.
- Lab: Data warehouse schema design and OLAP cube creation in a cloud-based environment (e.g., using Google BigQuery or Redshift).
Module 14
Module 7: Advanced Data Mining Techniques
- Text Mining
- Techniques for extracting useful information from text data.
- Natural Language Processing (NLP) techniques for text analysis.
- Sentiment analysis and topic modeling using machine learning.
- Big Data Mining
- Introduction to big data and Hadoop ecosystem.
- Using Apache Spark for data mining on large-scale datasets.
- NoSQL databases and their integration with big data mining.
- Real-Time Data Mining
- Techniques for streaming data: Apache Kafka, Spark Streaming.
- Real-time anomaly detection and predictive analytics.
Module 15
Module 8: Advanced Data Warehousing Techniques
- Real-Time Data Warehousing
- Concepts: Real-time ETL, change data capture (CDC), and data replication.
- Implementing real-time data pipelines with Kafka and Spark.
- Data Lake vs Data Warehouse
- Differences in architecture, use cases, and technologies.
- Integrating data lakes with data warehouses for enhanced analytics.
- Data Warehouse Automation
- Automating data integration and transformation processes.
- Tools for automating data warehouse deployment and scaling.
Module 16
Module 9: Case Studies and Industry Applications
- Industry Use Cases
- Data mining and warehousing in finance, healthcare, e-commerce, and telecommunications.
- Real-world examples of how data mining and warehousing drive business value.
- Future Trends in Data Mining and Warehousing
- AI in data mining and predictive analytics.
- Future of cloud data warehousing and AI-driven analytics.
Module 18
Program Outcomes
✔ Understanding the fundamentals of Data Mining & Warehousing
✔ Exposure to real-world applications in research and industry
✔ Practical knowledge of data processing and analytics tools
✔ Certificate of Completion
Module 20
Fee Structure
Fee: INR 2499 USD 55
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