We Help Find Value in Data’s Big Potential

Any organizations (both commercial and federal) see the potential in capitalizing on data to unlock operational efficiencies, to create new services and experiences, and to propel innovation. Unfortunately, too many business leaders invest in one-off technical solutions—with a big price tag and mixed results—instead of investing in a strategic data science capability.

A data science capability embeds and operationalizes data science across an enterprise such that it can deliver the next level of organizational performance and return on investment. A data science capability moves an organization beyond performing pockets of analytics to an enterprise approach that uses analytical insights as part of the normal course of business.

Building a data science capability in any organization isn’t easy—there’s a lot to learn, with roadblocks and pitfalls at every turn. But it can be done—and done right. Based on our pioneering work with our clients across verticals, and in building our own data science team, TechnoSphere can help you understand what’s needed, how to get started, and how to mature your data science capability.

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10 Data Science Capabilities Your Team Needs (and the Tools to Support Them)

Version control with Git and git-flow

Capability: Typically you will go through many iterations of your code and the work products your code produces. It quickly becomes impossible to track changes and reproduce earlier work without some code version control tool. This is only exacerbated when your team size is >1.

Wrangling and persisting data with PostgreSQL

Capability:  Even if your data is small enough to fit in memory, reproducing work will involve running all those scripts into memory before you can pick up where you left off. Other team members have to do the same. This is painful and inefficient. You therefore need to persist your work (raw data, intermediate datasets and work products).