Data Science languages & tools

Language & Tools

language	tools & libraries
Python	TensorFlow, PyTorch, Scikit-learn, Keras
R	ggplots, Keras, TensorFlow
SQL	MySQL, PostgreSQL, SQLite, MS SQL Server, MariaDB, ORACHLE
Java	Weka, Java-ML, Apache MLlib, Deeplearning4j
Scala	Apache Spark, VEGAS, Big DL
C++	TensorFlow, MongoDB, Caffe
JavaScript	TensowFlow.js
Julia	JuliaDB

Data Science Tools

Apache Hadoop
- Hadoop is an open-source framework that is used for distributed storage and processing of large datasets. It consists of two main components: Hadoop Distributed File System (HDFS) for storing data and MapReduce for processing data.
Apache Spark
- Spark (Big Data#Spark) is another open-source framework that is used for distributed processing of large datasets, aka. It provides a unified analytics engine for batch processing, real-time processing, machine learning, and graph processing.
SQL and NoSQL Databases
- SQL databases: such as PostgreSQL, MySQL, and Oracle, are used for structured data storage and querying
- NoSQL databases: such as MongoDB, Cassandra, and Redis, are used for unstructured and semi-structured data storage and querying.
ETL tools
- build data pipelines and automate data integration, cleansing, and transformation.
- Apache NiFi, Talend, Informatica
Data warehouse tools (Data Warehouse & Data Lake#What is Data Warehouse)
- store and manage large datasets for business intelligence and analytics
- Amazon Redshift (AWS Storage & Databases#Amazon Redshift), Google BigQuery, Snowflake
Data integration tools
- ingest and integrate data from various sources into data pipelines
- such as Apache Kafka, Apache Flume, and AWS Glue
Data visualization and reporting tools
- create interactive dashboards and reports to communicate insights and trends from data.
- Tableau, Power BI, and Looker
Cloud platforms
- provide infrastructure, tools, and services for deploying, managing, and scaling data engineering solutions in the cloud.
- AWS, Google Cloud, Microsoft Azure