The definitive intro to big data science

Course Curriculum

Chapter 1: Course Introduction

Lecture 1: Introduction

Lecture 2: Course setup

Lecture 3: Course goals

Chapter 2: Concepts and terminology

Lecture 1: Traditional data analytics as a process

Lecture 2: Definition and meaning of big data

Lecture 3: Definition and meaning of data science

Lecture 4: Interoperability between big data and data science

Lecture 5: Traditional databases and their limitations

Lecture 6: Example – Relational databases and SQL querying using MySQL

Lecture 7: Unstructured data

Lecture 8: Big data science as a process

Lecture 9: Recap – Concepts and terminology

Chapter 3: Big data tooling and technology – Introduction

Lecture 1: Tooling and technology as part of our process

Lecture 2: Scale-up vs. scale-out

Lecture 3: Big data terminology

Chapter 4: Big data tooling and technology- NoSQL

Lecture 1: NoSQL rationale

Lecture 2: NoSQL databases as a concept

Lecture 3: Key-value stores

Lecture 4: Column-oriented stores

Lecture 5: Document stores

Lecture 6: Graph stores

Lecture 7: Example – Document store – MongoDB

Lecture 8: Example – Graph store – Neo4J

Lecture 9: Recap – NoSQL

Chapter 5: Big data tooling and technology – Processing tools

Lecture 1: Introduction to processing tools

Lecture 2: Understanding MapReduce

Lecture 3: Example – MapReduce as a conceptual exercise

Lecture 4: MapReduce continued

Lecture 5: Beyond MapReduce – Streaming processing

Lecture 6: Example – Spark batch

Lecture 7: Example – Spark streaming

Lecture 8: Example – Storm

Lecture 9: Supportive operational tools – YARN, Zookeeper and Kafka

Lecture 10: Recap – Data processing

Chapter 6: Data science – Machine learning and AI – Supervised machine learning

Lecture 1: Introduction to machine learning & artificial intelligence

Lecture 2: Introduction to supervised machine learning

Lecture 3: Evaluation in supervised machine learning

Lecture 4: Algorithms – decisition trees

Lecture 5: Algorithms – naive bayes

Lecture 6: Algorithms – regression

Lecture 7: Algorithms – ensemble learners & random forests

Lecture 8: Recap – Supervised ML

Chapter 7: Data science – Machine learning and AI – Unupervised machine learning

Lecture 1: Introduction to unsupervised machine learning

Lecture 2: Algorithms – K-means

Lecture 3: Algorithms – DBSCAN

Lecture 4: Algorithms – Hierarchical clustering

Lecture 5: Recap – Unsupervised ML

Chapter 8: Data science – Machine learning and AI – Other principles & pre-/post-processing

Lecture 1: Other forms of machine learning

Lecture 2: Pre- and post-processing

Chapter 9: Data science – Machine learning and AI – Tools and technology

Lecture 1: Tools in machine learning

Lecture 2: A word on AI

Lecture 3: Example – Supervised machine learning using Spark

Lecture 4: Example – Using AI in Keras to predict crypto

Chapter 10: Visualization, cases & wrap-up

Lecture 1: Visualization in big data science

Lecture 2: Example – Case 1 – IoT

Lecture 3: Example – Case 2 – Data lake in a box

Lecture 4: Wrap-up