Big Data and Data Science Introduction

Big Data and Data Science

• Big Data refers to extremely large and diverse collections of structured, unstructured, and semi-structured data that grows exponentially over time.
• It is characterized by its volume, velocity, and variety, making it difficult for traditional data management systems to store, process, and analyze.
• Big Data is used in machine learning, predictive modeling, and other advanced analytics to solve business problems and make informed decisions.

Motivation and Applications

• Big Data has given organizations a new way to analyze and visualize their data effectively.
• Examples of its applications include:
  • Business: Customer feedback, trends, etc.
  • Health: Healthcare organizations use big data technology to capture patient information and get a complete view for insight into care coordination, health management, and outcome.

Benefits of Big Data Analytics

• Organizations can use big data analytics systems and software to make data-driven decisions that can improve business-related outcomes.
• Benefits include:
  • More effective marketing
  • New revenue opportunities
  • Customer personalization
  • Improved operational efficiency
• These benefits can provide competitive advantages over rivals.

Tools and Technologies

Apache Hadoop

• Apache Hadoop is an open-source, Java-based software platform that manages data processing and storage for big data applications.
• Key benefits of Hadoop include:
  • Scalability
  • Resilience
  • Flexibility
• The Hadoop Distributed File System (HDFS) provides reliability and resiliency by replicating nodes in a computing cluster.

Tableau

• Tableau is a powerful tool used for data analysis and visualization.
• Key features of Tableau include:
  • Creation of interactive visualizations without coding
  • Support for multiple data sources
  • Ability to connect to various data sources
  • Enable users to create reports by joining and blending different datasets

R Language

• R is a language and environment for statistical computing and graphics.
• R provides:
  • A wide variety of statistical and graphical techniques
  • Highly extensible capabilities
  • Easy production of well-designed publication-quality plots

Big Data and Cloud

• Cloud Computing providers often utilize a "software as a service" model to allow customers to easily process data.
• Big Data is often generated by large, network-based systems and can be in a standard or non-standard format.
• Cloud infrastructure allows for real-time processing of Big Data and enables Big Data analytics to occur in a fraction of the time it used to.

Job Roles and Skill Set

• Job roles in Big Data include:
  • Business Analyst
  • Data Analyst
  • Data Scientist
  • Data Engineer/Data Architect
  • Machine Learning Engineer
  • Big Data Engineer
• Required skills include:
  • Analytical skills
  • Data visualization skills
  • Problem-solving skills
  • SQL skills
  • Programming skills (Python, Java, R)