Awesome Data Engineering

Get Started. It's Free
or sign up with your email address
Awesome Data Engineering by Mind Map: Awesome Data Engineering

1. Julia

2. Logit

3. ML libs

3.1. High-level

3.1.1. Scikit-learn

3.1.2. Keras

3.1.3. Tensorforce

3.2. Low-level

3.2.1. Tensorflow

3.2.2. Theano

3.2.3. Caffe2

3.2.4. Torch

3.2.5. CNTK

4. Jupyter Notebook

4.1. Boosting / Ensembles

5. OS / Shell / Environment

5.1. Linux

5.2. Bash

6. Programming Languages

6.1. Python

6.2. R

6.3. Scala

7. Machine Learning Methods

7.1. Neural Networks

7.1.1. Convolutional

7.1.2. Recurrent, LSTM

7.2. Support Vector Machine (SVM)

7.3. Decision Trees

7.4. Reinforcement Learning

8. Machine Learning Areas

8.1. NLP

8.2. Picture

8.2.1. Style Transfer

8.2.2. Object Detection and Classification

8.2.2.1. Optical Character Recognition (OCR)

8.2.2.2. ImageNet / VGG16 / VGG19

8.3. Sound

8.3.1. Text-to-Speech

8.4. Speech recognition

9. Math

10. Druid

11. OLAP-specific

12. Working With Data

12.1. Mapreduce Systems

12.1.1. Hadoop

12.1.2. YT

12.2. RDBMS-like

12.2.1. Google BigQuery

12.2.2. Amazon Redshift

12.2.3. Yandex Clickhouse

12.2.4. CockroachDB

12.3. PostgreSQL-based

12.3.1. Greenplum

12.3.2. Citus

12.4. NoSQL

12.4.1. Elasticsearch

12.5. MongoDB

12.6. BI / Quering / Reports

12.6.1. Kibana

12.6.1.1. Tableau

12.6.2. Metabase

12.6.2.1. Superset

12.6.3. Plotly

12.6.4. Redash

12.7. ETL

12.7.1. Splunk

12.7.2. Talend

12.7.3. Singer.io

13. Theory

13.1. Stats

13.2. Algorithms

14. DevOps

14.1. Continious Integragion

14.1.1. Gitlab CI

14.1.2. Travis CI

14.1.3. Drone

14.1.4. Teamcity

14.1.5. Jenkins

14.1.6. Buildbot

14.2. Amazon Web Services

14.3. Google Cloud Platform

14.4. Docker

14.5. Kubernetes

15. Minimal "Must have" example (could vary)

15.1. Bash

15.2. Scikit-learn

15.2.1. Jupyter Notebook

15.3. Keras

15.4. Docker

15.5. AWS or GCP

15.6. Gitlab CI