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5 Data Science Project Problems & The Tool That Solved Them

The world is embracing data science and its potential to change everything. Data scientists are in heavy demand. With the tools and resources available today, it has never been easier to build models and excel in the field…at least on paper.

The fact is that data scientists continue to struggle and face frustrating project roadblocks, despite new and innovative technology. From data collection and exploration, to engineering, model training, and evaluation, the process is still complicated and inefficient.

This is where amazing data scientists, engineers, developers, and SMEs lose the opportunity to take their projects to the heights they should. Great tools exist, but the complex, disparate relationship between them means the process is cumbersome, requires a lot of additional learning, and becomes a nightmare to navigate with a team.

The platform gives users a working hub powered by all the best data science applications out there and makes learning and collaboration a seamless part of the process.

What exactly is wrong with the way things work now? Well, "data science is not magic."

Successful solutions need planning, focus, and strategic direction. Siloed applications that are limited due to integration challenges and overwhelming maintenance demands, missing collaboration features that undermine effective troubleshooting, and a lack of readily available support mean a team’s energy is not focused where it needs to be.

These challenges can be solved. Digital Hub™ grew from a team’s need to push the limits of current methods and make the exceptional specialized tools that exist work to the team’s advantage, not become a hindrance to project success.

Here is a breakdown of just five of the most common roadblocks:

Roadblock 1: It is cumbersome and time-consuming to set up the system configurations necessary for the success of a data science project. 

  • Technology should be an enabler in the field of data science. However, with the complexities in set up and configurations, it has become a significant barrier to entry in the field. Those looking to do so may not know where to start. They also may not have the requisite knowledge to set up and sustain their data science projects with the resources necessary. This can become an expensive endeavour. 

  • Open source tools work well for specific applications, but they are disparate. The result? Data flow through different applications and tools is sometimes unfeasible.

  • New tools and technologies for data science are invented and refined at a rapid pace. Selecting the correct tools for a particular project and integrating them with the infrastructure in-place can be overwhelming for the user. The complexity of integrating new tools and technologies is a barrier to staying up to date. The Solution: A system pre-configured to allow for seamless data flow. This unifies disparate applications into one holistic, streamlined interface, enabling data scientists to work on projects end-to-end without the hassle of downloading and uploading their data into different applications. Changes made to the dataset in one application will be registered in all other applications within a singular project space. The modular nature of Digital Hub™ allows for continuous upgrade and improvement and adding as many applications to the system as needed.

Roadblock 2: Data science technology infrastructure requirements, especially for big data, are highly advanced and complex. Documentation often doesn’t exist, and maintenance of these tools is difficult.

  • Data science processes can be computationally expensive. A limitation to the insights you can generate from data is how much of it you can easily analyze. This is difficult to do with traditional, monolithic architecture-based platforms and applications.

  • Documentation relevant to setting up big data pipelines is vastly dispersed and has a significant learning curve associated with it.

  • Setting up and maintaining big data processing for projects is difficult, and again, a significant learning curve exists.

  • Maintenance of a multi-server cluster to deploy applications onto is a significant task that not all users may be comfortable engaging in. This is a necessary task, though, for many computationally intensive data science processes. The Solution: Data scientists should focus on what they are passionate about: the data. Digital Hub™ is built to handle the rest. The system is built to handle big data and will automatically scale to meet the requirements of the project. Users can focus on delivering powerful insights from their data and will never have to worry about messy system configurations.

Roadblock 3: A place for the data science community to collaborate is currently not readily available.

  • Discourse related to data science techniques and approaches is difficult to find and the websites that enable it are dispersed across the internet.

  • It is difficult to troubleshoot and problem-solve cutting edge approaches.

  • There is no central area where individuals struggling on a data science project can ask pertinent questions to ease any concerns. The Solution: Digital Hub™ has a built-in forum that allows users to ask questions pertaining to their data science project. Learn about the latest techniques in data science through the News Feed and ask any and all implementation questions in the community. Rather than wasting time trying to find appropriate online resources, when a user is stuck on a problem, they can quickly dive into the communities and find the help they need.

Roadblock 4: Leading data science platforms provide expensive and restrictive solutions that can quickly become defunct.

  • Existing data science platforms are built to propagate a specific framework to solving data science problems. The diversity of data science problems can often preclude existing frameworks – out of the box thinking is often necessary. Rather than make the project fit with the existing technology, the reverse should be true. Technology should be malleable enough to support the needs of a particular project itself. The Solution: The technology landscape in the world changes at a moment’s notice. Users can find solace in knowing that Digital Hub™ evolves with it. The system, unlike other existing platforms, is highly modular and built to be future proof. Digital Hub™ incorporates new advancements in technology and unifies them into a holistic user interface. This ensures that users will always have access to the most advanced tools available on the market.

Roadblock 5: A significant barrier to entry into the field of data science is familiarity with approaches. 

  • Relevant, up-to-date information is difficult to source independently and can lead to significant time expenditure. Once this information is found, applying it, without prior experience, is also a significant challenge. The Solution: A data scientist's time should be spent on learning, researching, and innovating, as opposed to finding relevant information. Through a custom algorithm, Digital Hub™ sources articles relevant to user projects and makes them readily accessible.  This allows users to maximize time spent on delivering insights.

Current solutions aren’t cutting it, so we built a data science powerhouse.

The shift toward cloud-based data platforms to manage and mitigate data issues is gaining traction. Reduced funding within companies, a large influx of new data users, and an increased interest to transition to a digital space make this empowering hub the low-cost solution that can nourish a bourgeoning data science community.

Digital Hub™ is tailored towards data science teams – big or small. More so than ever, teams have been forced to shift to an online space and rethink their data strategy. Individuals are looking for areas where they can grow their skillset and survive in this new and changing world.

A data science platform should be a place where you can access the best tools for all things data science. 

That is exactly why we built Digital Hub™.

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