Data science has become an integral part of the business strategy of most forward-thinking and successful organizations globally. As the data proliferation increases and data becomes the enabler of business success, the role of the data scientists rises to become the hottest job of the 21st century.

However, data science is an evolving field. Its scope within the enterprise is constantly changing. It is now being used to solve complex problems, to build models that can accurately identify high-value customers and avenues and strategies to retain them. Enterprises are using their power to create highly effective product recommendation engines, identify process gaps and areas of process improvements, etc.

As the scope of data science is changing, the toolkit that enables data scientists and organizations is evolving too. The open-source community has been very active in this space and has aided the democratization of data science. The open-source ecosystem offers a lot of scope for collaboration and contribution, and the fact that these tools are now reliable and no longer limiting provides immense value to enterprises.

However, with the plethora of data science tools increasing, how can you determine which one is suited for you to help you become the Sherlock Holmes of data and help make data science faster, deeper, and more effective?

Here’s a look at some key considerations.

Programming language

Assessing the dexterity of programming languages is one of the key parameters while assessing data science tools. Data science tools are of two kinds, one for those with programming language knowledge and one for business users. Python and R are two open-source programming languages that have been popular in the data science landscape and have been used for data collection, data exploration, data visualization, and data analysis. They boast of great packages and libraries and are well-suited to meet the data science needs of organizations of today.

Alternately there are data science tools that do not need any programming capabilities and thus enable greater democratization of data science. These tools are user-friendly and assist organizations in creating their army of citizen data scientists out of business users. This approach also helps organizations become truly data-driven.

Flexibility

Apart from doing statistical analysis, data science tools also have to give you the flexibility to perform things like regression, component analysis, clustering, machine learning, etc. and should offer one or more of these methods.

They should also provide the capability to create, test, and maintain Basic and Advanced Analysis and Models. For example, if you want to build a statistical model and want to uncover optimal parameter values and want to use likelihood functions and optimization techniques, then the tools should offer the flexibility to do so.

In-depth information

A core aspect and function of data science is to help build awareness in the face of uncertainty. This factor has to be a key consideration when selecting data science tools. While some tools might provide results but unless they provide insights into how and why those results are reached, it is of no use. This is because it impedes the capability of the data scientist to de-construct the methods and the model to gain a deeper understanding of the model and system itself.

Then, if the model makes an error, it becomes a confounding exercise to diagnose the problem. The capability to see inside nearly every statistical method and result and even black-box machine learning methods in a user-friendly manner can deliver immense value to data science efforts.

Open-source is good

An open-source tool kit is something to look for in the data science tool evaluation kit. Open-source has a robust feedback loop, a big community to support and continuous improvements that help fix mistakes and issues in a timely manner.

However, while evaluating, you must ensure that the tool is maintained by a reputable organization and that it has a strong and committed user base. It is also imperative to ensure that the tool has been running without any significant issues. While doing so, it is important to assess the feedback loop and proactive community support when it comes to mitigating toolkits. Many tools not only leverage the power of the open-source community but also have a dedicated team of experts to take care of any issues, challenges, and concerns.

Does it provide extensions?

Given the growing volumes of data and the speed at which data processing needs to happen to power data science, it makes sense to evaluate the kind of extensions that the tool offers. Big Data connectors, API kits for Social & Cloud Platforms, Sensor Gateways, Mobile Apps, etc. are some usual suspects.

The tool also should have the capability to connect to cloud-based services to manage the large volumes of data, solve the complexity of processing, and improve in-memory storage and security.

Assess the analytics angle

Analytics is a key component of data science. Thus, evaluating the kind of analytics the tool provides becomes an important parameter. Tools that provide a rich library of visualizations and powerful interactions and have the capability to integrate complex data sets across varied business and analytical areas is essential.

Other analytics capabilities, such as text analytics and predictive analytics, help in furthering data science capabilities and velocity. Looking for capabilities such as Linguistic, Statistical, NLP and Machine Learning techniques to Model & Structure textual data for analysis, visualization, and collaboration also make tool selection easier.

Along with all this, it also makes sense to assess the integration capabilities of the tools. Data science tools should give you the flexibility to integrate functionalities, import data, and export results in generally accepted formats, further data science efforts. For example, if you want to integrate a statistical software method into a particular language, you should be able to do that.

With the right data science tools in place, your data science team can capably balance time against the quality of results and ensure that insights and information are timely. After all, ‘time is money’.