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
Understanding Business Problem
As with any project, we start by studying our client’s product needs or business challenges, documenting requirements and your vision of a solution to connect data and value.
Data Acquisition
Data acquisition is the processes for bringing data that has been created by a source outside the organization, into the organization, for production use. Prior to the Big Data revolution, companies were inward-looking in terms of data.
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 Cleaning
The data can have many irrelevant and missing parts. To handle this part, data cleaning is done. It involves handling missing data, noisy data etc.
Data Cleaning
The data can have many irrelevant and missing parts. To handle this part, data cleaning is done. It involves handling missing data, noisy data etc.
Data Transformation
This step is taken in order to transform the data in appropriate forms suitable for mining process. This involves following ways:
Data Transformation
This step is taken in order to transform the data in appropriate forms suitable for mining process. This involves following ways:
Data Reduction
Data mining is a technique that is used to handle huge amount of data. In order to get rid of this, we use data reduction technique.
Data Reduction
Data mining is a technique that is used to handle huge amount of data. In order to get rid of this, we use data reduction technique.
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.
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:
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.
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:
Execution
This step includes process from training model to evaluation.
Model Building
Sometimes the data or the business objectives lend themselves to a specific algorithm or model. Other times the best approach is not so clear-cut. As you explore the data, run as many algorithms as you can; compare their outputs. Base your choice of the final model on the overall results. Sometimes you’re better off running an ensemble of models simultaneously on the data and choosing a final model by comparing their outputs.
Model Building
Sometimes the data or the business objectives lend themselves to a specific algorithm or model. Other times the best approach is not so clear-cut. As you explore the data, run as many algorithms as you can; compare their outputs.
Base your choice of the final model on the overall results. Sometimes you’re better off running an ensemble of models simultaneously on the data and choosing a final model by comparing their outputs.
Evaluation
Model evaluation aims to estimate the generalization accuracy of a model on test data. Methods for evaluating a model's performance are divided into 2 categories: namely, train and Cross-validation. Both methods use a test set (i.e. data not seen by the model) to evaluate model performance.
Evaluation
Model evaluation aims to estimate the generalization accuracy of a model on test data. Methods for evaluating a model's performance are divided into 2 categories: namely, train and Cross-validation. Both methods use a test set (i.e. data not seen by the model) to evaluate model performance.
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