Data Science Services

Data Science Services

Datopic team of data scientist have expertise in the areas of Data Science, Big Data, Machine Learning, Natural Language Processing (NLP), Semantic technologies such as ontologies, Data mining, Automated information extraction, and Information retrieval. Our data science group is focused in helping you solve your real needs.

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Product Engineering Services

Product Engineering Services

Datopic product engineering services has been established to help engineer, enhance, and maintain products on behalf of associated organization. Our team of engineers are extensively trained on product engineering technique like Just-in-Time/Agile development process and are well versed with all aspects of Software Development Life cycle (SDLC) and Product Development Life cycle (PDLC).

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Our Data Analytic Life cycle

Data Discovery 01


To discover the primary business objectives, we set the stage for defining scope, team learns the business domain and business intelligence, assesses the resources available to support the project in terms of people, technology, time, and data, frames the business problem as an analytic challenge and formulates initial hypotheses (IHS) to test and learn.

Data Preparation 02

Data Preparation

At the data preparation stage, along with data availability a sandbox environment is set-up. Extract, load, and transform (ELT) or extract, transform and load (ETL) also known as ETLT processes are performed for data ingestion. Successful completion familiarizes with the data thoroughly and enable to take steps to condition the data.

Model Analysis and Planning 03

Model Analysis and Planning

Ensuring to select the right method to achieve its objective, the stage plays an important role. It includes descriptive analysis and visualization of dataset and ensure that the chosen analytical technique must enables to meet the business objectives set during hypothesis selection. Steps explores to apply cluster, classification, finding the relationships between variables and selects key variables forming most suitable models.

Model Building 04

Model Building

It is an implementation stage that includes development of datasets for testing, training and production. The Stage utilizes data transformations to better expose its model for applying algorithms. Adequate consideration and decision is made for robust environment for parallel processing and faster operations.

Evaluation 05


Evaluating the performance of the model is one of the core stage in data analytic process. It indicates how close the efforts meet business objectives. Various standard measures are performed on the model. Design a test harness to evaluate and select most effective and accurate algorithms.

Optimize Parameters 06

Optimize Parameters

Stage enable efficacy of the trained model by choosing appropriate parameters. The process includes model selection i.e. finding the optimal set of parameters to get the most out of well-performing algorithms.

Communicate Results 07

Communicate Results

Stage includes documenting key findings, explicitly list assumptions and limitations, share results about acceptance or rejection of defined hypothesis.

Lab to Production 08

Lab to Production

Share finalized reports, briefing and technical documents. Set-up and deploy code to production environment and integrate implementation with business intelligence processes. Advance techniques are utilized for ongoing monitoring of model performance and accuracy.

Case Study

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Blogs (Top 4)

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June 22, 2016

Next Generation Indian Railway E-Ticketing with BigData

With India’s massive on-line population growth, the e-ticketing systems supporting India Rail faced a major challenge with scale. At a recent forum in India, the team responsible for the applications explained how they overcame the scale and other issues with Pivotal GemFire, tripling application throughput. With a country-wide responsibility for making e-ticketing work for its citizens, India Rail relies on Pivotal GemFire to help them address the future as internet users increase, new applications connect, and functionality expands to support consumers

June 15, 2016

Public Datasets for Research and Development

Working with POC's for Big Data applications we require extensive support to download sample datasets. Playing around these datasets not only opens up new paradigms but also applying Machine Learning algorithms on top of these dataset is a real fun. Most of these datasets are free and provide near real time experience.

May 28, 2016

Top Big Data Papers

List of papers responsible to change BIG DATA trends.

May 22, 2016

Microsoft HDInsight Services – Available Versions and Compositions

With rapidly development of multiple available versions we sometime needs to know where we are coming from. The blog provides you the list of versions along with the offered compositions.

Whitepapers (Top 3)

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About Us

Datopic is client centric product engineering and software development company. As a team of technocrats, we offer engineering’s teams, consulting services and knowledge services across globe. Datopic engineering team comprises of decade years of experience and leverage our strong technology expertise and deep industry knowledge to develop customized solutions and services to fit specific needs. With vast hands-on with product engineering, online-offline application maintenance and domain specific software development Datopic team provides high quality, efficient and effective solutions. Our exceptional learnings from real time experience and knowledge from different domains enables organization to be prepare and adopt best-of different domains.

Datopic is an India based Data Analytic and Big Data organization. Our head office is in Delhi, India, development centers in Noida, India and direct assistance centers in London, U.K. and Boston US.

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About Us


Datopic is growing rapidly and looking for talented individuals.

If you have experience in Predictive Data Analytics and Data Science Areas, we may have a challenging position for you.

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