Data science tools Python

Data science tools Python: The data science is the insight of data called the process of collecting, analyzing the data. It is blended tools, algorithms and ML principles having a goal to discover patterns from raw data.

1. Programming Languages:-R, Python or Julia.
2. IDE:-Anaconda, Pycharm, Atom.
3. Database:-SQL for integrating data from different databases.
4. Big Data:-The spark is used for handling huge datasets.
5. Spreadsheet:-The spreadsheets like Excel, Google Sheets for some of the adhoc analysis.
6. Deployment:-Kubernetes and Docker for deployment.
7. Other Software’s:-People start consuming cloud services like Amazon Sage maker, Google ML Engine.etc.Airflow for scheduling.
8. Version Control:-use Github for version control.
9. Tools:-SAS, IBM Watson, Weka.
10. Visualization:-Some use tableau for better visualization.

Data Science Tools For Data Storage:-

Apache Hadoop:-

It is a free, open-source framework that will manage and store tons of data.
 It provides distributed computing of data sets over a cluster of 1000s of computers.
 It is high-level computations and data processing.
Features of Apache Hadoop are as follows:-

1. There are  large data on thousands of Hadoop clusters
2. It will use the Hadoop Distributed File System for data storage which distributes massive amounts of data across several nodes for distributed.
3.  Parallel computing.
4. Data Science Tools for Exploratory Data Analysis:-

1. Microsoft HD Insights:-

It is a cloud platform provided by Microsoft for data storage, processing, and analytics.
Features of Microsoft HD Insights are:-

1. It will provide support to integrate with Apache Hadoop and Spark for data processing.
2. The Windows Azure Blob is the storage system for Microsoft HD and manages the data across thousands of nodes.
3. It will provide Microsoft R Server that supports enterprise-scale R for performing statistical analysis and building Machine Learning models.

2. Informatica PowerCenter:-

The informatics has a product-focused on data integration and PowerCenter that stands for data integration capabilities.
Features of Informatica PowerCenter are:-

1. The data integration tool that is based on the ETL architecture.
2. It is used for extracting data from sources, transforming and processing it with business requirements.
3. It will provide support for processing, grid computing, adaptive load balancing, dynamic partitioning, and pushdown optimization.

3. RapidMiner:-

The RapidMiner is a popular tool for implementing Data Science.
Features of rapid miner are:-

1. It is a platform for data processing, building Machine Learning models and deployment.
2. Also provides support for integrating Hadoop framework with in-built RapidMiner Radoop.

Data Acquisition and Data Cleansing Tools:-

The data is collected from raw format into sensible and useful data for business users.
The organization is a challenge for data-driven companies that work on massive volumes of data.
The ETL tool will solve the issue of gathering and converting the data into an understandable format for further analysis.
ETL tools will start the process by extracting the data from underlying sources by a data model.

1. Talend:-

Talend is an open-source data integration tool also known to yield software solutions for data preparation, and application integration.
The Real-time statistics, easy scalability, efficient management, early cleansing, faster designing, better collaboration.
Features of this tool are:-

1. Development and deployment of the tasks.
2. The tool automates as well as maintains the task. 
3. Tools are open-source/free.
4. It has unified Platform.
5. Huge Community.

2. Go Spot Check:-

The application is powerful for the field which collects and shares data in real-time.
The tool will perform process as to create, gather, and analyze to achieve data analysis.
We analyze data in real-time access to utilize monitoring work progress and performance. 
Features:-

1. Customizable form builder.
2. Task distribution.
3. Configurable reporting.

3. IBM Datacamp:-

It can perform tasks with a high degree of automation, flexibility, and accuracy. 
Functionality of the Datacamp,

1. Acquisition of the documents. 
2. Processing of documents to pull out useful information.
3. Delivering content and data to back end systems.

4. Mozenda:-

The Mozenda is a cloud-based web-scraping platform and helps the companies collect.
The tool will have a point-to-click interface and user-friendly UI.
It is very easy to integrate and allows users to publish results in CSV, TSV, XML, or JSON format.
The tool will provide API access to fetch data and has inbuilt storage integrations like FTP, Amazon S3, Dropbox, and more.

5. Octoparse:-

Octoparse is client-side web scraping software for Windows.
A web scraping template is a simple powerful feature and the purpose is to input the target website/keywords in the parameters on the pre-formatted tasks.

6. OnBase:-

The OnBase is a tool developed by Hyland and called a single enterprise information platform that is designed to manage user’s content.
The tool will centralize a user’s business content in a secure location and then delivers to relevant information.
OnBase will allow the organization to become more agile, efficient, and capable, thereby increasing productivity, delivering excellent customer service, and reduce risk across their enterprise. 

Features of the tool:-

1. The Tool provides a single platform for building content-based applications while complementing other core business systems.
2. It reduces cost and development time by rapidly creating content-enabled solutions with low-code application development platform. 

D3.js:-
It is the java library used to make visualization on web browsers.
The tool is useful for data scientists working on IOT based devices.
Excel:-
The powerful analytical tool for data science and excel pack a punch.
NTLK:-
It has emerged as a field of data science and used for various languages like parsing, stremming.