FULAFIA - STA212: BIG DATA ANALYTICS
Module 1: Introduction to Big Data
Definition and characteristics of big data (Volume, Velocity, Variety, Veracity)
Evolution of data analytics
The role of big data in various industries
Module 2: Big Data Architecture and Infrastructure
Overview of big data technologies
Cloud computing for big data
Scalability, reliability, and security in big data systems
Module 3: Data Collection and Management
Methods of data collection (sensors, social media, IoT)
Data quality, cleaning, and preprocessing
Data integration techniques
Module 4: Data Warehousing and Data Lakes
Comparison between data warehouses and data lakes
Implementation strategies and tools
Data governance and compliance
Module 5: NoSQL and Distributed Databases
Introduction to NoSQL databases (Document, Key-Value, Wide-Column, Graph)
Distributed database systems
CAP theorem and eventual consistency
Module 6: Data Processing with Hadoop and Spark
Hadoop ecosystem (HDFS, MapReduce, YARN)
Apache Spark fundamentals (RDDs, DataFrames, Spark SQL)
Performance considerations and optimizations
Module 7: Stream Processing and Real-Time Analytics
Concepts of stream processing
Tools like Apache Kafka, Flink, and Storm
Real-time data analytics use cases
Module 8: Predictive Modeling and Machine Learning
Basics of machine learning algorithms
Supervised vs. unsupervised learning
Model evaluation and selection
Module 9: Data Visualization and Dashboards
Techniques for effective data visualization
Tools: Tableau, Power BI, D3.js
Dashboard design principles
,Module 10: Statistics in Big Data
Descriptive statistics for large datasets
Inferential statistics and hypothesis testing
Predictive statistics with big data
Module 11: Specialized Analytics
Text Analytics and Natural Language Processing:
Text mining, sentiment analysis, topic modeling
Image and Video Analytics:
Techniques for processing visual data
Anomaly Detection and Fraud Detection:
Algorithms and methods for detecting anomalies
Module 12: Applications in Various Sectors
Customer Segmentation and Personalization
Supply Chain Optimization
Marketing Analytics and Customer Acquisition
Sales and Revenue Forecasting
Healthcare Analytics and Medical Informatics
Energy and Utilities Analytics
,Module 1: Introduction to Big Data
1. What is Big Data?
Definition:
Big Data refers to extremely large, complex datasets that cannot be effectively processed, stored,
or analyzed using traditional data management tools. It is characterized by the 5 Vs:
1. Volume:
o The sheer size of data, ranging from terabytes to petabytes and beyond.
o Example: Facebook processes 4 petabytes of data daily from user interactions.
o Traditional databases (e.g., MySQL) struggle with storage at this scale.
2. Velocity:
o The speed at which data is generated, collected, and processed.
o Example: IoT sensors in smart cities generate real-time traffic data every
millisecond.
o Requires tools like Apache Kafka for streaming data.
3. Variety:
o The diverse formats of data: structured, semi-structured, and unstructured.
o Examples:
▪ Structured: Excel sheets, SQL databases.
▪ Semi-structured: JSON, XML (e.g., social media APIs).
▪ Unstructured: Text, images, videos (e.g., YouTube uploads).
4. Veracity:
o The uncertainty, noise, or bias in data quality.
o Example: Social media data may contain spam or fake accounts.
o Requires preprocessing (cleaning, validation) before analysis.
5. Value:
o The insights and benefits derived from analyzing big data.
o Example: Retailers like Walmart use purchase data to optimize inventory and
reduce costs.
, Examples of Big Data:
• Social Media:
o Platforms like Twitter (500 million tweets/day) and Instagram (95 million
photos/day) generate vast unstructured data.
• IoT (Internet of Things):
o Smart devices (e.g., Fitbit wearables, Tesla cars) produce continuous sensor data.
• Healthcare:
o Electronic Health Records (EHRs), genomic sequencing (e.g., 200GB per human
genome).
• E-commerce:
o Amazon tracks user behavior (clicks, purchases) to personalize recommendations.
Historical Evolution:
• 1970s–2000s: Relational databases (SQL) dominated for structured data.
• Early 2000s: Rise of the internet and Web 2.0 led to data explosion (e.g., Google indexing
billions of web pages).
• 2004: Google published the MapReduce paper, inspiring Hadoop (2006) for distributed
processing.
• 2010s: Growth of mobile devices, IoT, and social media accelerated unstructured data.
• Today: Cloud computing (AWS, Azure) and AI/ML tools democratize big data processing.
2. Challenges in Big Data
1. Storage:
o Traditional databases cannot scale to petabytes.
o Solution: Distributed file systems like HDFS (Hadoop Distributed File System).
2. Processing:
o Sequential processing (e.g., single-server SQL) is too slow.
o Solution: Parallel frameworks like MapReduce and Apache Spark.
3. Scalability:
o Systems must scale horizontally (adding more machines) rather than vertically
(upgrading hardware).
Module 1: Introduction to Big Data
Definition and characteristics of big data (Volume, Velocity, Variety, Veracity)
Evolution of data analytics
The role of big data in various industries
Module 2: Big Data Architecture and Infrastructure
Overview of big data technologies
Cloud computing for big data
Scalability, reliability, and security in big data systems
Module 3: Data Collection and Management
Methods of data collection (sensors, social media, IoT)
Data quality, cleaning, and preprocessing
Data integration techniques
Module 4: Data Warehousing and Data Lakes
Comparison between data warehouses and data lakes
Implementation strategies and tools
Data governance and compliance
Module 5: NoSQL and Distributed Databases
Introduction to NoSQL databases (Document, Key-Value, Wide-Column, Graph)
Distributed database systems
CAP theorem and eventual consistency
Module 6: Data Processing with Hadoop and Spark
Hadoop ecosystem (HDFS, MapReduce, YARN)
Apache Spark fundamentals (RDDs, DataFrames, Spark SQL)
Performance considerations and optimizations
Module 7: Stream Processing and Real-Time Analytics
Concepts of stream processing
Tools like Apache Kafka, Flink, and Storm
Real-time data analytics use cases
Module 8: Predictive Modeling and Machine Learning
Basics of machine learning algorithms
Supervised vs. unsupervised learning
Model evaluation and selection
Module 9: Data Visualization and Dashboards
Techniques for effective data visualization
Tools: Tableau, Power BI, D3.js
Dashboard design principles
,Module 10: Statistics in Big Data
Descriptive statistics for large datasets
Inferential statistics and hypothesis testing
Predictive statistics with big data
Module 11: Specialized Analytics
Text Analytics and Natural Language Processing:
Text mining, sentiment analysis, topic modeling
Image and Video Analytics:
Techniques for processing visual data
Anomaly Detection and Fraud Detection:
Algorithms and methods for detecting anomalies
Module 12: Applications in Various Sectors
Customer Segmentation and Personalization
Supply Chain Optimization
Marketing Analytics and Customer Acquisition
Sales and Revenue Forecasting
Healthcare Analytics and Medical Informatics
Energy and Utilities Analytics
,Module 1: Introduction to Big Data
1. What is Big Data?
Definition:
Big Data refers to extremely large, complex datasets that cannot be effectively processed, stored,
or analyzed using traditional data management tools. It is characterized by the 5 Vs:
1. Volume:
o The sheer size of data, ranging from terabytes to petabytes and beyond.
o Example: Facebook processes 4 petabytes of data daily from user interactions.
o Traditional databases (e.g., MySQL) struggle with storage at this scale.
2. Velocity:
o The speed at which data is generated, collected, and processed.
o Example: IoT sensors in smart cities generate real-time traffic data every
millisecond.
o Requires tools like Apache Kafka for streaming data.
3. Variety:
o The diverse formats of data: structured, semi-structured, and unstructured.
o Examples:
▪ Structured: Excel sheets, SQL databases.
▪ Semi-structured: JSON, XML (e.g., social media APIs).
▪ Unstructured: Text, images, videos (e.g., YouTube uploads).
4. Veracity:
o The uncertainty, noise, or bias in data quality.
o Example: Social media data may contain spam or fake accounts.
o Requires preprocessing (cleaning, validation) before analysis.
5. Value:
o The insights and benefits derived from analyzing big data.
o Example: Retailers like Walmart use purchase data to optimize inventory and
reduce costs.
, Examples of Big Data:
• Social Media:
o Platforms like Twitter (500 million tweets/day) and Instagram (95 million
photos/day) generate vast unstructured data.
• IoT (Internet of Things):
o Smart devices (e.g., Fitbit wearables, Tesla cars) produce continuous sensor data.
• Healthcare:
o Electronic Health Records (EHRs), genomic sequencing (e.g., 200GB per human
genome).
• E-commerce:
o Amazon tracks user behavior (clicks, purchases) to personalize recommendations.
Historical Evolution:
• 1970s–2000s: Relational databases (SQL) dominated for structured data.
• Early 2000s: Rise of the internet and Web 2.0 led to data explosion (e.g., Google indexing
billions of web pages).
• 2004: Google published the MapReduce paper, inspiring Hadoop (2006) for distributed
processing.
• 2010s: Growth of mobile devices, IoT, and social media accelerated unstructured data.
• Today: Cloud computing (AWS, Azure) and AI/ML tools democratize big data processing.
2. Challenges in Big Data
1. Storage:
o Traditional databases cannot scale to petabytes.
o Solution: Distributed file systems like HDFS (Hadoop Distributed File System).
2. Processing:
o Sequential processing (e.g., single-server SQL) is too slow.
o Solution: Parallel frameworks like MapReduce and Apache Spark.
3. Scalability:
o Systems must scale horizontally (adding more machines) rather than vertically
(upgrading hardware).
