The definitive intro to big data science

Name: The definitive intro to big data science
Rating: 4.6 (49 reviews)

Learn the ins & outs of big data and data science in the most complete helicopter-view course that there is.

Created byErik Tromp

Last updated 7/2022

English

What you'll learn

No-nonsense approach to big data science that anyone can understand regardless of prerequisite knowledge
Get a broad overview of big data and data science
Evolve from a career in BI to a career in big data
Identify which topics you want to learn more about for your advancements
Be able to make well-motivated decisions in your day-to-day job around data

Course content

10 sections • 57 lectures • 7h 17m total length

Introduction2:05
In this lecture, I will introduce myself and this course. If you already know me or simply don't care - go ahead and skip this part! :)
Course setup3:24
We will look at how the course is set up and what you can expect.
Course goals2:50
A good exercise is to think upfront about what our goals are for this course. This will help us later on to reflect on whether those goals were met or not.

Introduction to processing tools9:13
Learn how big data processing turns storage into insight through distributed batch and real-time paradigms, MapReduce, and a shift from ETL to LTE with raw storage and query-time transforms.
Understanding MapReduce5:03
Example - MapReduce as a conceptual exercise4:19
MapReduce continued8:00
Beyond MapReduce - Streaming processing10:46
Compare batch mapreduce with streaming processing and uncover on-the-fly computations using tools like Spark Streaming, Storm, and Flink for real-time data.
Example - Spark batch14:46
Build and run a spark batch job by creating a spark session, reading JSON data, filtering and transforming a data frame, and writing output.
Example - Spark streaming11:38
Example - Storm5:52
Supportive operational tools - YARN, Zookeeper and Kafka8:46
Explore how supportive operational tools such as Zookeeper, YARN, and Kafka coordinate distributed servers, manage resources, and enable data movement across the Hadoop ecosystem and big data landscape.
Recap - Data processing3:56

Introduction to machine learning & artificial intelligence6:34
Introduction to supervised machine learning7:49
Evaluation in supervised machine learning10:04
Algorithms - decisition trees12:08
Explore decision trees, a family of intuitive supervised learning algorithms, using a weather toy example to show how feature splits yield a readable model while managing impurity and overfitting.
Algorithms - naive bayes11:01
Algorithms - regression8:49
Algorithms - ensemble learners & random forests6:27
Explore ensemble learners and random forests, which combine multiple simple models like decision trees and naive Bayes to improve accuracy, reduce overfitting, and scale out on big data.
Recap - Supervised ML2:44

Requirements

This course requires no prerequisite knowledge and will help both the novice as well as the expert
It does not matter whether you plan to practicion big data science as an engineer/scientist or whether you are C-level or manager and just want to be enabled to make proper decisions

Description

Are you interested into big data? Data science? Tired of finding only courses that describe one tool or programming language but fail to set a broad standard that sketches the bigger picture? Then this course is exactly what you've been looking for!

In this course we leave no stone unturned when it comes to big data science. Not only will we demystify big data in all of its aspects - NoSQL storage, batch processing using MapReduce, streaming tools like Spark - but we will also build a bridge to data science and its core principles such as supervised and unsupervised Machine Learning and Artificial Intelligence.

We provide a no-nonsense approach to introduce every aspect of data you will ever encounter in your career or organization and set a strong fundament to both marry the field of big data with data science AND continue in exactly the right direction for more in-depth learning on specific topics.

As the course's lecturer, Erik Tromp has been working in big data science for almost 15 years. He has published over 20 papers academically but is best-known for his pragmatic approach to data and applying it to real-life scenarios. Because of his broad understanding of big data, data science and data architecture, Erik has been successfully teaching these concepts commercially for over a decade and received honors for his courses.

For the first time ever, he has decided to make his award-winning material available to the masses digitally, providing an insanely good deal for anyone looking to learn something on data.

Who this course is for:

Best suitable for professionals of all layers of an organization that want to get a broad overview of big data science
If you are experience in big data AND data science or are looking for in-depth tutorials on specific tools, this course is NOT for you

The definitive intro to big data science

What you'll learn

Explore related topics

Course content

Course Introduction3 lectures • 8min

Concepts and terminology9 lectures • 1hr 19min

Big data tooling and technology - Introduction3 lectures • 10min

Big data tooling and technology- NoSQL9 lectures • 1hr 13min

Big data tooling and technology - Processing tools10 lectures • 1hr 22min

Data science - Machine learning and AI - Supervised machine learning8 lectures • 1hr 6min

Data science - Machine learning and AI - Unupervised machine learning5 lectures • 32min

Data science - Machine learning and AI - Other principles & pre-/post-processing2 lectures • 17min

Data science - Machine learning and AI - Tools and technology4 lectures • 32min

Visualization, cases & wrap-up4 lectures • 38min

Requirements

Description

Who this course is for: