Data Science

What is Data Science ?

What does Data Science mean?

Data science is an inter-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data and apply knowledge and actionable insights from data across a broad range of application domains.

Data science is the domain of study that deals with vast volumes of data using modern tools and techniques to find unseen patterns, derive meaningful information, and make business decisions. Data science uses complex machine learning algorithms to build predictive models. The data used for analysis can be from multiple sources and present in various formats.

Why Data Science?

Data science is a broad field of study pertaining to data systems and processes, aimed at maintaining data sets and deriving meaning out of them. Our main forte is Big Data analytics, Forecasting, predictive analytics, AI applications, security in IOT with AI.Here are the few highlights about the projects which we have taken care of under Data science.

Data science or data-driven science enables better decision making, predictive analysis, and pattern discovery. It lets you:

  • Find the leading cause of a problem by asking the right questions
  • Fraud analysis
  • Perform exploratory study on the data
  • Model the data using various algorithms
  • Communicate and visualize the results via graphs, dashboards, etc.

How Does Data Science Work?

Data science involves a plethora of disciplines and expertise areas to produce a holistic, thorough and refined look into raw data. Data scientists must be skilled in everything from data engineering, math, statistics, advanced computing and visualizations to be able to effectively sift through muddled masses of information and communicate only the most vital bits that will help drive innovation and efficiency.

Data scientists also rely heavily on artificial intelligence, especially its subfields of machine learning and deep learning, to create models and make predictions using algorithms and other techniques.

Data Science Uses
  • Healthcare
  • Self-Driving Cars
  • Logistics
  • Entertainment
  • Cybersecurity
  • Image Recognition
  • Weather Monitoring System
  • Business Decision making with Big Data Analytics
  • Recommendation Systems
  • AIOT based Women Security System

Prerequisites for Data Science
  • Machine Learning :
  • Machine learning is the backbone of data science. Data Scientists need to have a solid grasp on ML in addition to basic knowledge of statistics.

  • Modeling :
  • Mathematical models enable you to make quick calculations and predictions based on what you already know about the data. Modeling is also a part of ML and involves identifying which algorithm is the most suitable to solve a given problem and how to train these models.

  • Statistics:
  • Statistics are at the core of data science. A sturdy handle on statistics can help you extract more intelligence and obtain more meaningful results.

  • Programming:
  • Some level of programming is required to execute a successful data science project. The most common programming languages are Python, and R. Python is especially popular because it’s easy to learn, and it supports multiple libraries for data science and ML.

  • Databases:
  • A capable data scientist, you need to understand how databases work, how to manage them, and how to extract data from them.