Data Architecture for Data Scientists

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Biju Krishnan

1:46:02

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1. Introduction

1. Million dollar slide.mp4

01:42

2. Batch and real time data processing - short explanation.html

2. Data Types

1. Structured data.mp4

05:02

2. Unstructured data.mp4

05:41

3. Semi-structured data.mp4

04:12

4. Short explanation of JSON and XML structures.html

5.1 Data Architecture - Module2 - Data Types.pdf

5. Semi-structured data in machine learning.mp4

02:40

6. Module review quiz - Data Types.html

7. Resources and Slides.html

3. Datawarehouse

1. Introduction to datawarehousing.mp4

02:53

2. Datawarehousing for data scientists.mp4

04:37

3.1 Data Architecture - Module3 - Datawarehouse.pdf

3. Cloud datawarehousing.mp4

05:18

4. Module review quiz - Datawarehousing.html

5. Resources and Slides.html

4. Data Lake

1. Introduction to a data lake.mp4

04:09

2. The technology used to build a data lake.mp4

04:10

3.1 Data Architecture - Module4 - Datalake.pdf

3. Cloud Storage terminology - Buckets and blobs.mp4

04:47

4. Module review quiz - Datalake.html

5. Resources and Slides.html

5. Data Lakehouse

1. Challenges with the data lake.mp4

04:31

2.1 Data Architecture - Module5 - Datalakehouse.pdf

2. Introduction to the data lakehouse.mp4

06:55

3. Module review quiz - Datalakehouse.html

4. Resources and Slides.html

6. Data Governance with the Data Mesh

1. Introduction to Data Mesh.mp4

03:57

2. Domain ownership and data as a product.mp4

05:26

3. Self service and federated governance.mp4

03:27

4.1 Data Architecture - Module6 - Data Governance with Data Mesh.pdf

4. Data Catalog.mp4

05:39

5. Module review quiz - Data Governance with the Data Mesh.html

6. Resources and Slides.html

7. Streaming data in Data Science

1. Introduction to streaming data.mp4

03:14

2. Kafka 101.mp4

02:43

3. Lambda architecture.mp4

02:29

4.1 Data Architecture - Module7 - Streaming data in DS.pdf

4. Kappa architecture and comparison.mp4

04:59

5. Word of caution and Resources.html

6. Module review quiz - Streaming data in Data Science.html

8. Data infrastructure for Machine Learning

1. Feature Store.mp4

06:01

2.1 Data Architecture - Module8 - Data Infra for ML.pdf

2. Vector Database.mp4

03:18

3. Module review quiz - Data Infrastructure for Machine Learning.html

9. Flowchart and Use case examples

1. Data Architecture decision making flowchart.mp4

05:08

2.1 Data Architecture - Module9 - Flowchart and use cases.pdf

2. Use case examples and applying the decision flow.mp4

03:04

Description

Learn about Datawarehousing, Data Lake, Data Lakehouse, Data Mesh, Kafka, Lambda and Kappa architecture and much more.

What You'll Learn?

Data Architecture in general, to be able to navigate your organizations data landscape
Develop understanding of topics like Data Lake, Datawarehousing and even Data Lakehouse to be able to communicate with data engineering teams
Understand the pricinciples of data governance topics like Data Mesh to better navigate the data governance paradigm
Get introduced to technologies related to machine learning specific data infrastructure like feature stores and vector databases

Who is this for?

Data Scientists who are transitioning from academia or business domains

Junior data scientists who would like to understand the topics surrounding data infrastructure

Citizen data scientists who wish to deploy machine learning models in production

Anyone who wishes to learn the basics of data architecture in a very short time

What You Need to Know?

Basic understanding of data science project workflow like model training and model deployment

Basic understanding of why data is needed for training and deploying models

Understanding of the difference between batch and real time use cases

More details

Description
Machine learning models are only as good as the data they are trained on, which is why understanding data architecture is critical for data scientists building machine learning models.
This course will teach you:
The fundamentals of data architecture
A refresher on data types, including structured, unstructured, and semi-structured data
The differences between data warehouses and data lakes
The concept of a data lakehouse
The idea of a data mesh for decentralized governance of data
The challenges of incorporating streaming data in data science
Some machine learning-specific data infrastructure, such as feature stores and vector databases
The course will help you:
Make informed decisions about the architecture of your data infrastructure to improve the accuracy and effectiveness of your models
Adopt modern technologies and practices to improve workflows
Develop a better understanding and empathy for data engineers
Improve your reputation as an all-around data scientist
Think of data architecture as the framework that supports the construction of a machine learning model. Just as a building needs a strong framework to support its structure, a machine learning model needs a solid data architecture to support its accuracy and effectiveness. Without a strong framework, the building is at risk of collapsing, and without a strong data architecture, machine learning models are at risk of producing inaccurate or biased results. By understanding the principles of data architecture, data scientists can ensure that their data infrastructure is robust, reliable, and capable of supporting the training and deployment of accurate and effective machine learning models.
By the end of this course, you'll have the knowledge to help guide your team and organization in creating the right data architecture for deploying data science use cases.
Who this course is for:
Data Scientists who are transitioning from academia or business domains
Junior data scientists who would like to understand the topics surrounding data infrastructure
Citizen data scientists who wish to deploy machine learning models in production
Anyone who wishes to learn the basics of data architecture in a very short time

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I have over 20 years of experience in helping enterprises manage data, and more than half of this in building scalable platforms for analytics and machine learning.Call it timing, luck or destiny, I was able to gain exposure to a variety of areas like Linux, Unix, Networking, Data Integration, Analytics, Big Data, and Machine Learning. My exposure to such varied set of technology areas, helps me craft solution architecture, that's not only easy to maintain but also economical.I'm originally from Mumbai, India, but got the opportunity to work in Malaysia, Singapore, Indonesia, Thailand, Sri Lanka, Finland, Sweden, Denmark, Norway, UK, and Germany, If we speak about my customers, they have been virtually in every country. I have gained immensely from this international exposure which not only reflects in my work but also in my personality.Currently I'm based in Munich, and when I'm not working, I enjoy hiking and snowboarding in the mountains nearby.If you like my courses, you are welcome to follow me on Linkedin where I regularly post

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