Fundamentals of Big Data Online Course


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Over the previous two decades, data generation has transformed beyond human comprehension in volume and speed. This is where the term Big Data Comes from. Big Data refers to data that has a broader diversity and is arriving in growing volumes and velocity. Data mining techniques can be applied to Big Data to extract useful information for incorporation into machine learning, predictive modeling, and other analytics uses. Businesses utilize Big Data to enhance their operations, deliver better customer service, develop individualized marketing campaigns, and carry out other tasks that can ultimately increase sales and profits.
TechClass Fundamentals of Big Data online course aims to give you a good knowledge of what exists in the Big Data area. This course will help you understand Big Data's characteristics, making it distinguishable from traditional data. It will get you familiar with fundamental concepts needed for loading and analyzing Big Data like distributed systems and Big Data technologies and tools like Hadoop, Spark, and Kafka. By the end of this course, you can state your Big Data-based attitude toward real-world problems using critical concepts and techniques.

Learning outcomes

  • Learn about the concept of Big Data and its benefits
  • Get familiar with the types of data involved in Big Data
  • Learn about V's of Big Data
  • Learn about databases and the ones that are suitable for Big Data
  • Learn about Data warehouses and Data lakes
  • Learn about how Big Data can be analyzed
  • Learn about distributed file systems and how they work
  • Learn about Big data technologies and tools such as Hadoop, and Spark, etc.
  • Learn about Hadoop architecture and modules
  • Learn about the differences between Hadoop and Spark

Table of contents

Chapter 1: Intro to Course

  • 1.1. Welcome!
  • 1.2. About TechClass Data Science Department
  • 1.3. Learning Outcomes
  • 1.4. Your Expectations, Goals, and Knowledge
  • 1.5. Copyright Notice
  • 1.6. Abbreviations

Chapter 2: Introduction

  • 2.1. What is Data?
  • 2.2. What is Big Data?
  • 2.3. The Benefits of Big Data
  • 2.4. History of Big Data
  • 2.5. Real-world Examples of Big Data
  • 2.6. Big Data Challenges
  • 2.7. Quiz

Chapter 3: Data Generation and Storage

  • 3.1. Where does the Data Come From?
  • 3.2. Types of Data
  • 3.3. Types of Databases
  • 3.4. Database for Big Data
  • 3.5. Data Warehouse and Data Lake
  • 3.6. Quiz

Chapter 4: Big Data Characteristics

  • 4.1. Introduction
  • 4.2. The Volume of Big Data
  • 4.3. The Variety of Big Data
  • 4.4. The Velocity of Big Data
  • 4.5. The Veracity of Big Data
  • 4.6. The Value of Big Data
  • 4.7. The Validity of Big Data
  • 4.8. Quiz

Chapter 5: Big Data Analytics

  • 5.1. Building a Big Data Strategy
  • 5.2. Data Analytics Lifecycle
  • 5.3. Phase 1: Discovery
  • 5.4. Phase 2: Data Preparation
  • 5.5. Phase 3: Model Planning
  • 5.6. Phase 4: Model Building
  • 5.7. Phase 5: Communicate Results
  • 5.8. Phase 6: Operationalize
  • 5.9. Quiz

Chapter 6: Distributed Systems

  • 6.1. Data Storage
  • 6.2. Distributed System and File System
  • 6.3. Pros and Cons of Distributed Systems
  • 6.4. Criteria of a Big Data Tool
  • 6.5. Data Processing
  • 6.6. OLTP and OLAP
  • 6.7. ETL and ELT
  • 6.8. Traditional DFS
  • 6.9. CAP Theorem
  • 6.10. Quiz

Chapter 7: Big Data Technologies and Tools

  • 7.1. Introduction
  • 7.2. Hadoop
  • 7.3. Hive
  • 7.4. Hudi
  • 7.5. Kafka
  • 7.6. Spark
  • 7.7. Flink
  • 7.8. Cloudera
  • 7.9. Cloud Computing: An Alternative Method
  • 7.10. Quiz

Chapter 8: More on Hadoop and Spark

  • 8.1. Hadoop Ecosystem
  • 8.2. Hadoop Distributed File System (HDFS)
  • 8.3. YARN: A Resource Manager for Hadoop
  • 8.4. Details of MapReduce
  • 8.5. Hadoop vs. Spark
  • 8.6. When to Use and When Not to Use Hadoop
  • 8.7. Quiz

Chapter 9: Final Tasks

  • 9.1. Self-Study Essay
  • 9.2. Final Project

Chapter 10: Finishing the Course

  • 10.1. What We Have Learned
  • 10.2. Where to Go Next?
  • 10.3. Your Opinion Matters
  • 10.4. Congrats! You did it!

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