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Exam Notices- Chapter 1
- Lesson 1: Big Data Characteristics, Typesand Classifications
- Lesson 2: Scalability, Massively Parallel Processing (MPP), and Distributed Computing Systems
- Lesson 3: Design Layers in Data Processing Architecture
- Lesson 4: Data Sources, Data Quality, Preprocessing and Data Store Export to Cloud Services Architecture
- Lesson 5: Data storage and analysis, Comparison between traditional systems, and Approaches for Big Data storage and analytics
- Chapter 2
- Lesson 1: Hadoop
- Lesson 2: Hadoop Distributed File System (HDFS)
- Lesson 3: MapReduce Framework and Programming Model
- Lesson 4: Hadoop Yarn
- Lesson 5: HBase
- Chapter 3
- Lesson 1: NoSQL Data
- Lesson 2: Key Value Store
- Lesson 3: Document Store
- Lesson 4: Column family, RC, ORC, Parquet and Tabular Data Stores
- Lesson 5: Object Data Store
- Lesson 6: NoSQL For Big Data
- Lesson 7: Shared Nothing Architecture and Distribution Models
- Lesson 8: MongoDB
- Lesson 9: Cassandra Databases
- Chapter 4
- Lesson 1: Select Key Terms
- Lesson 2: Introduction to Big Data Architecture Design Layers
- Lesson 3: MapReduce Programming Tasks Execution
- Lesson 4: MapReduce Detailed Functions (Grouping, Shuffling, partitioning, Sorting, Combining, and Reducing )
- Lesson 5: Hive Characteristics and Functions
- Lesson 6: Hive Query Language (HiveQL)
- Lesson 7: PIG characteristics and Functions
- Lesson 8: PIG Programming
- Chapter 5
- Lesson 1: Key Terms Spark Programming, Data and Tabular-Data Processing
- Lesson 2: Apache Spark Main Components, Features, and Architecture Layers
- Lesson 3 (a): Data Analytics using Apache Spark Components Spark SQL and DataFrames
- Lesson 3 (b): Data Analytics using Apache Spark Components Spark SQL for Querying the data objects
- Lesson 4: Spark DataFrame and RDDs
- Lesson 5: Python and its Libraries with Spark for Data Analysis
- Lesson 6: Machine Learning with Apache Spark MLib
- Lesson 7 (a): Applications and Big Data analytics using Spark
- Lesson 7 (b): Extract, Transform and Load Process
- Lesson 8: Applications and Big Data analytics using Spark
- Chapter 6
- Lesson 1: Definitions and Meanings of AI, ML and DL
- Lesson 2: Classes of variables, and estimating the relationships
- Lesson 3: Sample, Population, Variance, and Probabilistic Distribution
- Lesson 4: Kernel Functions, Moments, Welch Test and ANOVA
- Lesson 5: Correlation
- Lesson 6: Regression, Multiple Regression Objective-function And Prediction
- Lesson 7: K-NN Regression Analysis
- Lesson 8: Finding Similar Items, Collaborative Filtering, and Distance Measures for Similarity Analysis
- Lesson 9: Frequent Itemsets and Association Rule Mining
- Lesson 10: Clustering
- Lesson 11: Clustering Algorithms
- Lesson 12: Classification, Supervised Learning, and KNN and SGD Classifiers
- Lesson 13: Decision Tree
- Lesson 14: Naive Bayes and Support Vector Machines (SVMs) Classifiers
- Lesson 15: Random Forest and AdaBoost Classifiers
- Lesson 16: Recommenders
- Lesson 17: Mahout
- Chapter 7
- Lesson 1: Data Stream Concepts and Models
- Lesson 2: Data Stream Management System (DSMS)
- Lesson 3: Data Stream Architecture and Processing Languages
- Lesson 4: Stream Computing Methods
- Lesson 5: Stream Frequent Itemsets and Association Rule Mining
- Lesson 6: Real-time Analytics
- Lesson 7: Apache Spark Streaming
- Chapter 8
- Lesson 1: Graph Characteristics and Properties
- Lesson 2: Graph Models
- Lesson 3: Graph Databases Resource Description Framework (RDF) and SparQL
- Lesson 4: NEO4J Native Graph Databases
- Lesson 5: Graph Network Organization- Applications In Analytics
- Lesson 6: Probabilistic Graphical Network Organizations Bayesian and Markov Networks
- Lesson 7: Graph Analytics
- Lesson 8: Apache Spark GraphX
- Chapter 9
- Lesson 1: Text Mining Basics
- Lesson 2: Text Mining Process Phases
- Lesson 3: Text Classification Using KNN and Naive Bayes Classifier Supervised Machine Learning
- Lesson 4: Text Classification Using Support Vector Machines Supervised Machine Learning Method
- Lesson 5: Web, Web-Contents, Web-Structure and Web-Usages Mining
- Lesson 6: Web Content Mining
- Lesson 7: Web Usage Mining
- Lesson 8: PageRank Analysis of a Web Structure
- Lesson 9: Hub, Authorities and Communities in Web Graph
- Lesson 10: Social Network Graph, Centralities, PageRank and KNN Analysis
- Lesson 11: Clustering and SimRank in Social Network Graph
- Lesson 12:Counting Triangles and Communities In Social Network Graph
