What is Data Science and it’s tools

Data Science VS Machine Learning

Data Science can be called as a Data Blending Technique. It is multidisciplinary which blends data inference, understand complex behaviors, trends, develops algorithm & technology and analytically solves complex problems.

The future of Artificial Intelligence is Data Science.

The Core Data which is the raw information is stored in enterprise data warehouses. Data Science is the Process by which you can use to uncover the findings from data and using the stored Data for generating Business Value. It adds in advanced Capabilities while Building Data.

Data Science is:

  1. A science of getting into the Core of the Giant Data and Gaining Hidden Insights for Better Decision-Making Process.
  2. A science of analyzing the real-time data that will enhance the clarity and get you the right and Perfect solution to an enterprise.

“It is a capital mistake to theorize before one has data.
~ Sherlock Holmes”

What is a data scientist?

Data scientists are inquisitive thinkers and Big data wranglers. They’re partially mathematicians, computer scientists and even the bets trend-spotters.

Data Scientists are motivated to solve difficult Problems from the Structured Data or the Unstructured Data.  They Derive Complex Equations from the Data by making Observations and Coming up with Problem Solving Techniques. Strong knowledge of Python, SAS, R, Scala and must have their hands-on experience in SQL database coding

Data Science Course focuses on all the skills that one needs to Master to become a Data Scientist but Data Science Course or Data Science Tutorials alone is not enough to make you a Data Scientist. It is a completely new breed of analytical data experts which demands to master proper Programming skills, Business Experience and Concrete Focus in order to become a Data Scientist and nail into the Data Scientist Jobs.

Data Science VS Machine Learning

Data Science vs. machine learning

Data science is a Multidisciplinary Umbrella for multiple Parental disciplines. These Disciplines are Data Analytics, Data Mining, data engineering, Predictive Analysis and Machine learning. Machine learning is one of the Wings of data science. Machine learning uses Machine Learning Algorithms like regression and supervised clustering. However, the ‘data’ in data science may or may not evolve from a machine or a mechanical process. Data Science and Data Analytics and Data Science and Machine Learning are the trending paths for Processing the Data.

5 Top Data Science Tools

The Top Data Science tools are as follows:-


Rapid Miner Focuses on prediction modeling.

The Steps include:

  1. Data preparation
  2. Model building
  3. Data validation and
  4. Data deployment.

It lets you connect a large variety of algorithms that can be run without a single line of code.

It also allows the integration of custom R and Python scripts in the system.

DataRobot (DR)

DataRobot (DR) built by Kagglers including Jeremy Achin, Thoman DeGodoy and Owen Zhang. It is an Automated Machine Learning Platform. DataRobot helps in bringing the business knowledge and data. The rest is taken care by the cutting-edge automation.


BigML provides a good GUI which helps the users with:

  • Sources: You get the various sources of information
  • Datasets: With the sources, a Dataset can be created
  • Models: Predictive models can be Prepared
  • Predictions: Models help in giving predictions
  • Ensembles: Creating ensemble of various models
  • Evaluation: Validating the Models

BigML visualizes, does Problem-Solving, classifies, does regression, clustering and anomaly detection

Google Cloud AutoML

Cloud AutoML is Google’s Machine Learning suite. It lets the Users Build high-quality Models with Limited ML expertise.  Helps in training image Recognition Models. It comes with a drag-and-drop interface. It helps in to easily upload the images, train the model also deploying the Models directly on Google Cloud.

Cloud AutoML Vision is set up on Google’s transfer learning and neural architecture search technologies.


Paxata focuses on data cleaning and preparation. It gives a visual guidance which brings the Data Together and overcomes the technical barriers involved in handling data.

Paxata is an MS Excel-like application which finds and fixes the Missing Data. You can share and Reuse this Data. There is No Coding or Scripting required in Paxata.

What is the Data Science Process?

Step 1: Organizing the Data

  • Including the physical storage
  • Formatting the data
  • Integrating the best practices in data management

Step2: Packaging Data

  • Data prototypes are created
  • Data visualization
  • Statistical performance
  • Joining and manipulating the raw data and building new representations

Step 3: Delivering Data

Delivering the Data to the ones who require it the Most.

Data scientist jobs:

There are some prominent Data Scientist jobs that are present in the Market. These are:

  • Data Architect
  • Data Analyst
  • Data Analyst
  • Analytics Manager
  • Data Administrator
  • Data Engineer
  • Data Scientist
  • Business Analyst
  • Business Intelligence Manager

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