The Big Data and Data Mining Training Program provides participants with in-depth knowledge of big data analysis, data mining techniques, and applications. The training equips participants with the skills needed to understand, analyze data, and make strategic decisions in the business world. It offers the opportunity to leverage the power of data to develop more effective and efficient business strategies.
The training is for professional development purposes, and the certificate obtained does not replace the MYK authorization certificate in trainings where the MYK authorization certificate is mandatory.
- What is Big Data?
Definition of Big Data: The concept and importance of big data. Characteristics of Big Data (3V): Volume, Velocity, Variety, and their impact on the business world. Types of Big Data: Differences between structured, semi-structured, and unstructured data.
- Big Data Technologies and Tools
Hadoop: The core open-source framework used for big data processing. Spark: A popular tool that enhances the capabilities of Hadoop, offering fast data processing. NoSQL Databases: Databases used for flexible and rapid data storage (MongoDB, Cassandra, CouchDB, etc.). Data Warehousing and Data Lakes: The use of data warehouses and data lakes for data storage methods.
- What is Data Mining?
Definition of Data Mining: The purpose of data mining and how it is used in business. The Data Mining Process: Data collection, preprocessing, model creation, and result evaluation. Data Mining Techniques: Methods such as classification, clustering, regression analysis, association, and anomaly detection.
- Data Mining Algorithms and Methods
Classification Methods: Algorithms such as Decision Trees, Naive Bayes, K-Nearest Neighbors (KNN). Clustering Methods: K-means, hierarchical clustering, DBSCAN. Regression Techniques: Linear regression, logistic regression, support vector machines (SVM). Time Series Analysis: Analyzing time series data and making future predictions.
- Applications of Data Mining
Marketing and Customer Relationship Management (CRM): Customer segmentation, behavior analysis, and recommendation systems. Data Mining in Healthcare: Disease predictions, patient behavior analysis, and treatment recommendations. Financial Analysis and Risk Management: Fraud detection, credit scoring, and risk analysis. Retail and E-commerce: Sales forecasting, inventory management, and price optimization.
- Ethics in Big Data and Data Mining Applications
Data Security: Ensuring personal data security and privacy protection in big data analytics. Ethical Issues: Ethical dilemmas encountered when using data mining and artificial intelligence. Legal Regulations: The impact of GDPR and other data protection laws on big data projects.
- Data Visualization and Reporting
Data Visualization Techniques: Methods for visualizing data sets to improve understanding. BI Tools (Business Intelligence): Data reporting and analytical visualization with tools like Tableau, Power BI, QlikView. Reporting and Decision Making: Integrating insights from data analysis into reports and decision-making processes.
- Future Trends in Big Data and Data Mining
Machine Learning and Deep Learning: The growing role of AI in big data analytics. Real-Time Analysis of Data Streams: Analyzing real-time data streams and making instant decisions. IoT and Big Data: Data collection through the Internet of Things (IoT) and integration with big data.
- Big Data and Data Mining Project Management
Project Management Methods: Strategies for managing big data and data mining projects. Data Analysis and Business Intelligence Tools: Using analytical tools for project management and strategic decision support systems. Monitoring and Evaluating Data Projects: Performance indicators and project success metrics.
- Career Opportunities in Big Data and Data Mining
Data Scientist and Data Analyst: Career opportunities in this field and required skills. Data Engineering: Specialization in data engineering and big data infrastructures. Data Mining Consulting: Career paths for professionals providing data analysis services to companies.
The training is open to corporate collaborations, and individual applications are not accepted. The training content can be re-planned based on the corporate participant profile and your specific needs. Following mutual discussions, the scope and method of the training (In-person, Online) will be determined, and the related processes will be completed. If an agreement is reached, the suitable dates and times for your institution's participants and our instructors, as well as the location of the training, will be determined.