What data scientists basically do is that they collect and organize data to help guide organizations to make better decisions and come up with good strategies. In this fast paced, ever evolving and rapidly changing world of IT. Data scientists or even people in general are constantly looking for ways to boost productivity and enhance efficiency in their work. Here are four habits that are employed by data scientists for boosting productivity.

Wikipedia defines data science as ‘Data science is an interdisciplinary academic field that uses statistics, scientific computing, scientific methods, processes, algorithms and systems to extract or extrapolate knowledge and insights from potentially noisy, structured, or unstructured data‘. 

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Automate repetitive tasks

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Although the work of a data scientist seems very fun and creative but there are very boring aspects of it as well. They often have to deal with repetitive tasks which include data cleaning, preprocessing, and model evaluation. To save time, enhance productivity and have an overall better output in their work, Data scientists will often use tools like airflow or luigi to automate these tasks. This allows them to save time on boring tasks that have no major contribution to their work and focus more on the creative aspects of their work.

Make use of libraries

Libraries provide pre-built functions and algorithms for common data science tasks, allowing data scientists to focus on problem-solving rather than reinventing the wheel. Examples of Libraries include:-

  1. Pandas:  is a Python library for data analysis. Started by Wes McKinney in 2008 out of a need for a powerful and flexible quantitative analysis tool.
  2. Numpy: NumPy is a Python library used for working with arrays. It also has functions for working in domain of linear algebra, fourier transform, and matrices. NumPy was created in 2005 by Travis Oliphant.
  3. Scikit-learn: Scikit-learn is a machine learning library for Python. It features several regression, classification and clustering algorithms including SVMs, gradient boosting, k-means, random forests and DBSCAN. It is designed to work with Python Numpy and SciPy.

Data scientists use communication tools

Projects in data science usually require team efforts and collaboration with people of other backgrounds. These include domain experts, engineers, and designers. In such circumstances, having the right tools for seamless communication with your partners in work can be key for enhancing productivity and saving time. One such tool is slack.

Slack: Slack is a messaging app for business that connects people to the information they need. By bringing people together to work as one unified team, Slack transforms the way organizations communicate.

Continuous learning

We live in an ever evolving world of technology right now and so the field of data science is also subject to change over periods of time just like any other developing field in tech. In such times, It is necessary for data scientists to stay up to date with the latest algorithms, emerging techniques etc. They can do this by continuously learning and updating their skills through online courses, workshops, conferences, and reading research papers. Staying up to date with the world ensures that they get the most out of their work, boost productivity and don’t fall behind in the rat race.

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