Data Science

Overview

End-to-End Data Science Solution

We provide end-to-end Data Science solution from consulting to implementation, data-driven solutions and insights required for digital transformation journeys. With our in-depth experience in Analytics and Business transformation, we strive to achieve best in class results for each and every business objective.

Our offerings include consulting ranging from problem identification to actual data preparation and modelling, followed by real-time optimization and value creation for our clients. We develop and implement comprehensive data strategies starting from business use cases. We provide customized service to your company by leading projects, performing R&D, or supporting your data science and IT teams to deliver on project goals.

We bring in expertise such as building data pipelines from data source to modeling cluster to visualization, regardless of the source being your own data warehouses and even third parties. The result is a driven system for delivering actionable insights and strategic recommendations while automating tedious steps like data wrangling and model tuning.

The core of our approach is the ability to abstract models of real-life businesses and commercial problems that are intuitive to use, yet sufficiently realistic to support robust decision-making. These models are used to provide insights, and quantify the risks as well as benefits associated with solutions to complex business problems. Using a combination of the latest data science techniques and big data technology, we can provide insights into all aspects of business operations.

Approach

Our Approach towards Data Science

Data Preprocess

Data preprocessing is a data mining technique which is used to transform the raw data in a useful and efficient format. Step in which the data gets transformed, or Encoded, to bring it to such a state that now the machine can easily parse it.

Data Exploration

A preliminary exploration of the data to better understand its characteristics.

Descriptive Statistics

Statistic quantitatively describes or summarize features of a collection of information, while descriptive statistics is the process of using and analyzing those statistics.

  • Feature Identification
  • Univariate Analysis
  • Bi-variate Analysis
  • Outlier Treatment
  • Feature Transformation
  • Feature Creation

Data Visualization

Visualization is the first step to make sense of data. To transcript and present data and data correlations in a simple way, data analysts use a wide range of techniques like charts, diagrams, maps, etc. data visualization mainly techniques and their types are:

  • Chart – Line, Pie, Bar
  • Plots – Bubble, Scatter
  • Maps – Heat, Dot distribution
  • Diagrams and matrices – Tree, Matrix

Descriptive Statistics

Statistic quantitatively describes or summarize features of a collection of information, while descriptive statistics is the process of using and analyzing those statistics.

  • Feature Identification
  • Univariate Analysis
  • Bi-variate Analysis
  • Outlier Treatment
  • Feature Transformation
  • Feature Creation

Data Visualization

Visualization is the first step to make sense of data. To transcript and present data and data correlations in a simple way, data analysts use a wide range of techniques like charts, diagrams, maps, etc. data visualization mainly techniques and their types are:

  • Chart – Line, Pie, Bar
  • Plots – Bubble, Scatter
  • Maps – Heat, Dot distribution
  • Diagrams and matrices – Tree, Matrix

Execution

This step includes process from training model to evaluation.

Our Post

Recent Posts

Contact

Get in touch for Data Science Consultation