The Big Data Analysis Training Program aims to equip participants with the necessary skills to understand, process, and analyze large datasets. The training teaches how to extract valuable insights from big data using data mining, machine learning, and analytical tools.
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.
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Big Data Fundamentals and Concepts
- What is Big Data?: Definition of big data, its features, and how it is used in business.
- Characteristics of Big Data: Variety, velocity, volume, veracity, and value (the 5Vs).
- Types of Big Data: Structured, semi-structured, and unstructured data types.
- Big Data Ecosystem: Big data technologies like Hadoop, Spark, NoSQL.
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Introduction to Big Data Analysis
- Data Collection and Storage: How data is collected and stored in big data platforms.
- Data Processing Methods: Differences between batch and stream processing.
- Data Preprocessing: Data cleaning, integration, and transformation.
- Data Formats and Storage Systems: Formats like Parquet, Avro, JSON, and storage systems like HDFS, Amazon S3.
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Data Mining and Analytical Methods for Big Data
- Data Mining Basics: Classification, clustering, association analysis, and regression analysis.
- Advanced Data Analysis Techniques: Time series analysis, statistical analysis, and machine learning algorithms.
- Machine Learning and AI: Supervised and unsupervised learning, decision trees, k-means, regression, classification, and supervised learning.
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Big Data Analysis Using Hadoop and Spark
- Hadoop Ecosystem: Hadoop HDFS, MapReduce, and YARN.
- Apache Spark: Structure of Spark, RDDs, DataFrames, Spark SQL, MLlib, and Spark Streaming.
- Differences Between Hadoop and Spark: Comparing the two technologies and when to use them.
- Application Development and Speeding Up Processing: Fast data processing with Spark and storing large datasets with Hadoop.
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NoSQL Databases and Big Data Analysis
- NoSQL-Based Databases: MongoDB, Cassandra, HBase, and CouchDB.
- Differences Between NoSQL and Traditional SQL: Key differences between relational databases and NoSQL databases.
- Data Management and Advanced NoSQL Usage: Managing big data with NoSQL databases.
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Data Visualization and Reporting
- Data Visualization for Big Data: Analyzing big data using visualization tools (Tableau, Power BI).
- Interpreting and Reporting Big Data: Reporting analyzed data with meaningful visuals.
- Interactive Reports: Creating interactive data visualizations and dynamic reporting.
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Big Data Security and Ethics
- Data Security: Security, encryption, and authorization of big data.
- Data Privacy and Ethics: Data confidentiality, anonymization, ethical data usage, and laws.
- Data Breach and Risk Management: Big data security threats and preventive measures.
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Big Data Applications and Real-World Scenarios
- Financial Sector Applications: Risk analysis, credit scoring, fraud detection.
- Big Data in Healthcare: Patient data analysis, data mining in healthcare services.
- E-Commerce and Marketing: Customer behavior analysis, personalized marketing.
- IoT and Big Data: Processing and analyzing data from IoT devices.
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Big Data and Cloud Technologies
- Cloud Computing and Big Data: Big data analysis using Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP).
- Data Storage and Computing: Storing and processing data in cloud environments.
- Cloud-Based Solutions for Big Data: Integrating cloud infrastructure with data processing, analysis, and reporting tools.
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Skills and Certifications Acquired in the Training
- Expertise in Big Data Analysis: Participants will gain the ability to analyze large datasets, apply data mining techniques, and report results.
- Machine Learning and Statistical Analysis: Participants will gain the ability to derive meaningful results from big data using machine learning algorithms.
- Expertise in Big Data Tools: Participants will gain in-depth knowledge and experience in big data technologies like Hadoop, Spark, and NoSQL databases.
- Certification: At the end of the training, participants will receive internationally recognized certificates in big data analysis.
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.