Introduction
What is Data Science?
- Ability to Access
- Communicate
- Process
- Extract and load
- Visualization
- Analysis
- Make predictions Below are the major integrated components included in Data Science.

Tools used for Data Science
The below mentioned are some of the major tools that we use for Data Science.
- R
- SQL
- Python
- Hadoop
- SAS
- Java
- Hive
- Matlab
- Pig
- C++
- Ruby
- SPSS
- Perl
- Tableau
- Excel
- NoSQL
- AWS
- C
- HBase
- Bash
- Spark
- ElasticSearch
- PHP
- Scala
- Shark
- awk
- Cascading
- Cassandra
- Clojure
- Fortran
- JavaScript
- JMP
- Mahout
- objective-C
- QlickView
- Redis
- Redshift
- Sed
Trend of Data Science tools:
The Team Data Science Process (TDSP) provides a recommended life cycle that you can use to structure the development of your data science projects.
This process has five major stages.

Data Science process life cycle
- Business Understanding
- Data Acquisition and Understanding
- Modeling
- Deployment
- Customer Acceptance

Machine Learning
Predictive Analytics
Data Science in Visual Studio

- Data Storage and processing
- Data Science and Analytical applications
Data storage and processing
In this workload, we have advanced tools for big data storage and advanced analytics
- SQL
- Hadoop
- Spark
- Machine Learning with Azure
Data Science and analytical applications
- Python Tools
- R Tools
- F#
Python Tools and R Tools
These are the top two ranked Data Science tools in the industry and Visual Studio has a rich suite for these tools.
Job Trends for R Tool and Python
In this article, we got the basic idea of Data Science, the lifecycle of Data Science, the different tools used, the current trend of the tools and jobs, and the tools available in Visual Studio.


Anu VPosted Jan 1, 2017, 11:22 PM
Nice article.. Thanks for sharing..
Pankaj Kumar ChoudharyPosted Dec 6, 2016, 1:40 AM
Basic and enough information as a beginner point of view....