For business inquiries : (+1) 438 601-1155
For special requests : (+1) 438 601-1155
For business inquiries : (+1) 438 601-1155
For special requests : (+1) 438 601-1155
This training equips participants with essential skills in handling large datasets using Apache Hadoop and Spark. It seamlessly transitions into machine learning, covering diverse techniques with TensorFlow and PyTorch. The program integrates real-world applications, ensuring participants gain practical insights into leveraging data analytics in today's dynamic landscape.
Module 1: Introduction to Big Data and Machine Learning
Understanding the Origins and Significance of Big Data
Types of Data: Structured, Semi-Structured, Unstructured
Data Quality and Cleaning Strategies
Differentiating BI, Big Data, and Data Science
Security, Ethical, and Legal Challenges in Big Data
Open Data and Its Objectives
Big Data Projects in Enterprises: Specifics and Strategic Importance
Module 2: Big Data Architecture and Infrastructure
Coexistence of RDBMS and NoSQL Solutions
Extract, Transform, Load (ETL) Tools
Data Quality Management
ETL Example with Big Data Dedicated ETL Tool
Master Data Management (MDM) Contribution
Storage Using Hadoop: HBase, HDFS
Alternative Big Data Solutions: Sybase IQ, SAP HANA, Vectorwise, HP Vertica
Module 3: Data Analysis and Visualization in Big Data
Statistical Analysis Definition
Querying with Hive
Data Analysis Tools: Pig, Mahout
Data Integration with Sqoop
Application Development in Big Data
MapReduce Philosophy and Apache Spark Contribution
Introduction to Machine Learning and Data Prediction
Module 4: Visualizing Data and Dataviz Techniques
Introduction to Data Visualization
Building Effective Visualizations
Choosing Appropriate Chart Types
Enhancing Visual Impact of Indicators
Creating Charts: Histograms, Bars, Rings, Treemaps, Curves
Utilizing Visualizations: Maps, Tables, Matrices
Module 5: Advanced Visualizations and Interactive Tools
Displaying Analyses with Geographic Data
Formatting Options
Filter Tools
Segments and Filter Panel
Synchronization Across Pages and Filter Scope
Creating Numeric and Chronological Filters
Key Performance Indicators (KPIs)
Data Storytelling Techniques
Module 6: Big Data and Cloud Relationship
Motivation for Public and Private Clouds
Storage Clouds and Their Role
Focusing on Business Issues with Managed Services
Module 7: Machine Learning Foundations and Applications
Basics of Artificial Intelligence, Data Science, and Machine Learning
Historical Context of Machine Learning
Application Fields and Terminology
Overview of Tools: Jupyter, scikit-learn, Pandas, BigML, Dataiku
Mathematical and Programming Concepts
Big Data and Machine Learning are interconnected fields. Big Data provides the vast amount of data needed for training machine learning models. Machine Learning, in turn, utilizes algorithms to analyze and derive insights from this massive dataset, uncovering patterns and making predictions.
