The new standard in Data Science Services

Data science is an interdisciplinary domain involving statistical and computational methods for extracting insights from data. It includes a wide array of services such as data collection and preprocessing, data analysis and visualization, and the development of predictive models and machine learning algorithms.

Data science can be beneficial in the following ways: -Enhanced decision-making -Increased efficiency and productivity -Process optimization -Customer insights -Risk management

Turing offers comprehensive and end-to-end data science services including data collection and pre-processing, data pipeline, analysis & visualization, machine learning, predictive analytics, and data governance.

We, at Turing, ensure the security and confidentiality of your data through the implementation of robust data safety, governance, and compliance protocols. The steps we follow to ensure complete security and confidentiality of your data include data encryption, access control, data governance framework, regulatory compliance, training & awareness, security audits, and service level agreements (SLAs).

You should choose Turing for your data science project because of our extensive experience in the industry. Our data scientists and experts come from the most successful Silicon Valley companies to have implemented data science projects at scale. Our team of experts is rigorously vetted in 100+ skills using advanced machine learning signals. We offer custom data science solutions while ensuring complete transparency, security, and compliance.

As a reliable data science consulting company, we follow a well-structured process to ensure we deliver high-quality outcomes that meet your expectations. We first identify the problem, then chart out the scope of the solution followed by comprehensive execution and delivery of the solution. Some prominent steps involved in our data science projects include:

1. Problem definition & scope
2. Data collection & pre-processing
3. Data analysis & modeling
4. Model evaluation & validation
5. Model deployment
6. Optimization & maintenance