August 1, 2022
Nowadays, there seems to be an endless barrage of distractions that make it hard for employees to stay focused. As a matter of fact, a Thrive Global survey asked professionals when they felt most unproductive. The study revealed that more than 75% are finding it so hard to concentrate that they end up feeling overwhelmed and less productive. Obviously, this poses a real issue not just for the aforementioned professionals but also for the broader industries that they impact. This is especially true for those in in-demand careers such as data scientists.
There are approximately 2.5 quintillion bytes of data produced every day, and data scientists must use a wide range of programming languages, big data analytics, and predictive models every day to assess data relative to their industry. Maryville University’s look at data scientists explains it all. Understandably, this can prove to be a highly demanding role that sees many data scientists overworked and close to burnout. If data scientists are unable to stay productive, this could negatively impact the many industries they service such as retail, cybersecurity, and healthcare.
That said, here are some of the most useful productivity tools in 2022 for data scientists to work to their full potential and preserve their own work-life balance.
Data dependency graph maker
Unfortunately, waiting around for other people’s output is part and parcel of data science. While there isn’t really anything that you can do to speed this up on your end, it is possible for data scientists to optimize this time with a data dependency graph.One popular type of data dependency graph is a PERT (program evaluation and review technique) diagram.
Designed to identify the longest path of dependencies, a PERT diagram also includes the time needed to complete each dependency so you can work around it. There are numerous PERT diagram makers available on the market, but Lucidchart is one of the most user-friendly ones. Created with numerous visual-heavy features, Lucidchart allows users to quickly create personalized PERT diagrams that also calculate timelines and outcomes. Although it’s not a free software, this diagramming and formatting tool does offer a free trial.
Simplifies issue identification, tracking, and organizational groundwork
Linear’s name really says it all in terms of how it serves data science. When you tackle complex projects with sets of tasks, sub-tasks , and more, it can be a real saving grace to have a tool that neatly puts things in a more linear fashion. This can make it easier to track existing problems and add new ones for monitoring.
Not only is it useful for productivity and avoiding miscommunication, but it also allows teams to have a reliable reference to map out their future plans. Any next steps and further applications can be accomplished with more confidence because Linear ensures an aligned space with structured insights.
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Easier package management and open-source programming languages
Any data scientist knows the value of simplified deployment for productivity and minimizing errors. Anaconda literally makes package distribution, monitoring, and deployment a more simple and accessible process. It has a pretty vast library and is built to simplify processes, which is apparent when configuring one’s tools and projects.
Because it is open source, data scientists and managers are not limited by the tools at their disposal. This makes improvements smoother and allows for a more personalized approach to common program languages like Python. Even without modification, Anaconda lets users easily make use of packages for data analysis, software tracking and installation, and machine learning.
Visualization and storytelling aid
Since the final output that data scientists create is utilized by a myriad of departments outside of IT, part of their task includes presenting their findings in formats that are much easier for others to digest. Consequently, Project Jupyter (or Jupyter, for short) is a very popular web-application tool that helps in numerous aspects of visualization and presentation.
This makes it a staple for many data scientists who want to be able to effectively tell a story with their output. Because Jupyter has online cloud environments, it also supports collaborations which can further enhance the creation of presentations. As an open-source IPython-based tool, Jupyter supports common languages like Julia, R, and Python and is absolutely free.
Scalable cloud computing, analysis, and data reporting
Google Cloud is primarily known for being a highly supported cloud computing suite, which in itself can be a real boon for data scientists that need a reliable and secure infrastructure that isn’t too complicated. Whether working solo on projects or collaborating, Google Cloud offers a lot.
The most relevant tools in its arsenal for data scientists are arguably BigQuery and Google Data Studio. Used together, these can streamline the whole process from start to finish.
BigQuery lets you compile and manage your big data, deploy services, and scale analysis without having to rely on a server. This means you use up less time to compute and even have a more cost-effective store point.
You can then convert all of this data, as needed, using the Data Studio. Aside from being free, its main advantage comes in the fact that it's very easy to share via various Google solutions and offers customizability that is quite comprehensive. With this method, final reports and dashboards aren't hard to decipher or even put together in the first place.
Better collaboration and delegation of tasks
The biggest plus point for Miro is how easy it is to create a manageable workflow using this tool. It is highly intuitive and easy to use and even has built-in integration with other popular tools like Google, Slack, Zoom, and Jira, among others. Because of Miro’s ingenious and intuitive setup, it is truly meant to make shared resources, collaborating, and delegating tasks a more worry-free endeavor.
Its sticky note and whiteboard interface make each project, designation, and data point easier to distinguish, so there's less of a chance for confusion or misunderstanding between collaborators. Further discussion can even be explored with ease because of its chats and video conferencing options. Not only does this help for task management but also makes problem-solving and data reporting much simpler, largely due to its visual elements and various collaboration-centric features.
Comprehensive link management tool
A big part of a data science professional’s daily tasks includes visiting countless URLs for research, collaboration, and communication. On top of the URLs that employees come across on their own, there are dozens more that they receive from colleagues and clients alike. Understandably, sieving through these URLs can be a huge waste of time and energy. Case in point, a knowledge worker spends an average of nine hours every week just looking for this information.
To effectively eliminate this chokepoint, data scientists can make use of OSlash as a productivity tool. An enterprise URL manager, OSlash can help professionals create shortcuts to replace URLs for improved navigation and data sharing. By using OSlash, data scientists can easily access critical information on time and create a single source of truth that other team members can also benefit from.
Granted, overcoming personal and professional distractions that impede productivity requires more than just digital tools. However, given how data scientists spend most of their working hours online anyway, having such tools can reduce hiccups and improve optimal performance.
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