Analyze and Visualize your Data through best Data Science tools

By ridhigrg |Email | Jul 22, 2019 | 2181 Views

Today's businesses are undertaking a different kind of building and require a different set of tools.

As companies across the spectrum turn their attention to building a data analytics strategy to remain competitive in a digitally-focused global environment, they'll need an assortment of data science tools capable of slicing, dicing, and operationalizing enterprise data in myriad ways. 

But knowing which tools to use can be a real conundrum. A robust data science stack will meet the expectations of modern data analytics at minimum, and either supplement the existing expertise you have on staff, or stand-in for it entirely.

Fortunately, data-oriented solutions developers are consistently building new capabilities to keep up with the growing and evolving analytics demands. Often, it leads to successfully carving out niches in the market that make data analytics faster, deeper, and more user-friendly for enterprises of all kinds. This continuous vision and innovation in the data science discipline benefit a business world that is now almost solely dependent on data to drive everything from the smallest day-to-day improvements to the most provocative and forward-looking ideas.

Here's what you need to know about the best data science tools available to help you collect, store, analyze, and visualize your data, as well as incorporate the power and possibilities of machine learning.

Data collection tools
Collecting quality data that can be transformed into the rich analysis is the starting point for every data strategy. The right data collection tools can reduce errors and duplicates, ensure greater accuracy, and preserve the integrity of data coming from all sources.

The following data collection tools stand out:
GoSpotCheck
IBM Datacap
Mozenda
Octoparse
OnBase by Hyland

Data analysis tools
Finding meaning in and extracting value from your data is the core of all data analysis. Tools that enable you to easily understand and derive real meaning from your data help you make game-changing business decisions that impact revenue, competitiveness, innovation, the customer experience, and operational efficiency.

Check out these leading data analytics tools and platforms:
Alteryx
Domino Data Lab
KNIME Analytics Platform
RapidMiner

Data warehousing tools
Data warehouses function as repositories for data that's been combined and integrated from multiple, disparate sources and then standardized for ease of use. Data warehousing allows you to query clean and stable data on any subject relating to your business, in real-time or in batches. Solutions like Amazon Athena and S3, which run on the world's largest global cloud infrastructure, allow you to store and retrieve any amount of data from anywhere and run complex analytics without requiring separate analytics system.

Here are some of the top data warehousing tools today:
Amazon Redshift
Google BigQuery
Microsoft Azure
MySQL
Snowflake

Data visualization tools
Visual analytics tools identify patterns and trends in your data and help end-users understand and digest complex concepts. It's much easier to access, understand, and share visual representations of your data in the form of charts, graphs, and maps.

Consider any of these high-performing data visualization tools:
Google Fusion Tables
JReport by Jinfonet
Microsoft Power BI
Qlik
SAS

Machine learning tools
Parsing data, learning from it, and then making predictions about your business is the purpose and promise of machine learning. Today's machine learning tools use a variety of algorithms to represent, evaluate, and optimize your data to accurately interpret both generalizations and anomalies.

Here are some of the best machine learning tools available:
Anaconda
Databricks
DataRobot
Feature Labs
H20.ai

Enabling data analytics initiatives
The success of any modern data analytics strategy depends on full access to all data. A solution like Alooma - with a proprietary set of ETL (extract, transform, load) tools - simplifies and accelerates loading massive amounts of data from any data source and in any form into a cloud data warehouse or cloud-based storage like Amazon S3. And once the data is transferred to the DW, you can execute any machine learning models you've developed to deepen your knowledge of and engagement with your customers, or other important initiatives.

Source: HOB