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Ramses Alexander Coraspe Valdez

1 May, 10 tweets, 3 min read

There are a couple of activities involved in the world of data engineering:

1. Describe data architecture
2. Understand data sources
3. Design your data model
4. Configure infrastructure
5. Run and monitor ETL processes
6. Business intelligence and analytics

Thread 👇👇

The project below will help you to get some hands-on experience with data engineering, you will see all the steps mentioned above:

Uber expenses tracking

Data Architecture

Data Sources

Data Model

Infrastructure

ETL Processes

Business intelligence and Data analytics

Github repository:
github.com/Wittline/Uber-…

Youtube Video:

Medium post:
coraspe-ramses.medium.com/uber-expenses-…

#dataengineering

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More from @RamsesCoraspe

Ramses Alexander Coraspe Valdez

@RamsesCoraspe

26 Aug

If you are strengthening your skills in data engineering and machine learning with python, here I recommend you some books with hands-on projects, It will help with the commom questions: why? and for what?
#Python #MachineLearning
Text Analytics with python (Dioanjan Sarkar)

Hands-On Machine Learning with Scikit-Learn, Keras & Tensorflow (Aurélien Géron)

Data Science in Production (Ben G. Weber)

Read 6 tweets

Ramses Alexander Coraspe Valdez

@RamsesCoraspe

24 Jun

Evolution of the word clouds from the titles of books purchased on Amazon USA (1995 - 2015)

#AWS #DataEngineering #Python #BigData #100DaysOfCode #awscloud

The goal of this repository is to offer and explain an AWS EMR template that you can use quickly if the need for your analysis involves working with millions of records, the template can be easily altered to support the size of your project:

Github: github.com/Wittline/pyspa…

EMR EC2 SPOT AND ON-DEMAND INSTANCES

Read 5 tweets

Ramses Alexander Coraspe Valdez

@RamsesCoraspe

10 May

If you are a data-driven company and you are still stucked with R, here are some points to keep in mind for the transition to Python

1. Be focused on outcomes
2. Forget language and focus on the ecosystem
3. Cross-language libraries
4. Real datasets
5. Work Locally

👇

1. Be focused on outcomes:
Instead of learning everything about python, be focus on build and train Machine learning models first: Linear Regression, Logistic Regression, KNN, SVM, NN

Check this out

Github: github.com/Wittline/Machi…

2. Forget learning the language and focus on the ecosystem: Go right to Numpy, Pandas and Scikit-learn

Medium: medium.com/personal-proje…

Read 8 tweets

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Ramses Alexander Coraspe Valdez

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