Unveiling the Inner Workings: A Glimpse into a Day in the Life of a Data Scientist


Unveiling the Inner Workings: A Glimpse into a Day in the Life of a Data Scientist

Unveiling the Inner Workings: A Glimpse into a Day in the Life of a Data Scientist

The invention of technology, such as tablets, smartphones, and laptops, is extensively utilized by individuals worldwide these days. As a result, the amount of data generated in today's tech-savvy world is unimaginably vast. Here are some noteworthy statistics worth considering:

  • In 2022, digitization will have impacted over 70% of the global GDP.
  • The number of emails sent in 2022 surpassed a staggering 333.2 billion.
  • By 2025, the total amount of data stored in the cloud worldwide will exceed 200 zettabytes.

Calculating the precise amount of data drawn upon on the internet poses a challenge due to the vast volume of available data. This data loses its significance if not used and analyzed effectively. Consequently, this very fact is fueling the demand for data science professionals, specifically data scientists.

Understanding the Work of a Data Scientist

"A day in the life of a data scientist is a constant pursuit of innovation, pushing the boundaries of what is possible with data."

Given the job title, a data scientist typically revolves around working with data. They spend a significant amount of time gathering and shaping data, but in various ways and for different purposes. Here are some data-related tasks that data scientists might tackle.

  • Exploring Data

    Data scientists typically spend around 50 percent of their day working with data. On some days, their focus revolves around the initial stages of data analysis, which involves exploring, preprocessing, and wrangling data. These tasks form the foundation for any subsequent data-driven insights.

    Knowing data inside out is essential for data scientists. They are the go-to persons for multiple cross-functional teams when it comes to data: be it during strategic planning sessions, client meetings, or day-to-day decision-making processes. Whenever a client engagement arises, the data scientists have to first ask themselves if it is a familiar or new business problem. For the former, they can simply leverage existing solutions and data. However, for new business problems, they have to recognize if the problem can be addressed using the existing data. If that is not possible, they have to further consider exploring new datasets. The importance of data cannot be denied as it is the basic, yet crucial element in determining if they can support a client's business problem or not.

  • Developing Models

    Data scientists maintain and revalidate existing models, ensuring that they are trained with recent data and reflect current trends. Occasionally, new solutions are brainstormed to address additional business needs. During the development of new models, standard processes such as feature extraction, dataset splitting into train/test sets, cross-validation, out-of-time validation, and selection of the best model based on relevant performance metrics are executed. For data summarization and ad hoc queries, data scientists primarily use HiveQL (HQL), which resembles SQL and facilitates data retrieval from Hadoop. When exploring new data sources, Python is exercised to test various third-party APIs. Python is also heavily employed for data preparation and model development.

  • Communicating with Non-data Experts

    Data scientists strive to grasp the problems at hand as meetings revolve around data. Despite perceiving it as insignificant, they play a crucial role in communicating with non-experts. Their ultimate goal is problem-solving, not just building models. Understanding the business need that drives their work remains vital as data scientists deal with numbers and data for a reason. They must grasp the department's perspective and strategic motivations, which are essential. Equally important is their ability to explain decision implications and help others comprehend them. Like any professional, data scientists spend time attending meetings and responding to emails. However, effective communication becomes even more critical for them. They must convey the science behind the data in layman's terms while also understanding problems from a non-expert's viewpoint, rather than solely from their perspective as data scientists.

  • Embracing continuous learning

    As data scientists, working with data and collaborating with others will be a significant part of their day. However, staying updated with the evolving field is equally crucial. New insights and problem-solving approaches emerge daily, shared by fellow data scientists. To keep up, they’ll read industry blogs, and newsletters, and engage in discussions. Attending conferences and networking online with peers is essential. By staying informed, these professionals avoid reinventing the wheel and embrace better problem-solving methods.

Challenges Faced by a Data Scientist

We are well aware that every career or job role comes with its own set of challenges, and data scientists are no exception. Unfortunately, many firms fail to optimize the performance of their data scientists by not providing them with the necessary resources to achieve positive results. However, this should not discourage individuals from pursuing a career in data science. They can pave their way to success with cutting-edge skills and the right mindset to tackle challenges. The following are some common challenges faced by data scientists with solutions to overcome them.

  • Diverse Data Formats

    Organizations face the challenge of managing diverse data formats from various applications and tools, leading to multiple data sources that data scientists must access for valuable insights. However, relying on these sources involves manual data entry and time-consuming searches, resulting in errors, duplications, and subpar decision-making.

    To overcome this challenge, organizations need a centralized platform that seamlessly integrates with multiple data sources. This platform provides instant access to information from these disparate origins, consolidating data for efficient real-time management. By exploiting this centralized platform, organizations can aggregate data effectively, saving time and effort for data scientists. This streamlined process enhances data utilization, ultimately improving productivity and decision-making capabilities.

  • Interpreting the problem

    To successfully analyze data and find effective solutions, data scientists require a deep understanding of the business problem at hand. Unfortunately, many data scientists neglect this important step and rush into analyzing datasets without properly defining the problem and its objectives.

    That's why it's absolutely vital for them to embrace a structured workflow before delving into any analysis. This workflow should involve close collaboration with the business stakeholders, working hand in hand with them to establish a shared understanding. By doing so, they can create checklists that clearly outline the necessary steps for improved comprehension and problem identification. By following this thoughtful approach, data scientists can ensure that their analyses are in perfect alignment with the specific needs and goals of the business. This not only enhances the accuracy and relevance of their findings but also maximizes the impact of their solutions on the overall business objectives.

  • Misunderstanding the role

    A data scientist, sometimes, is expected to be a handyman and is expected to do several tasks such as building models, cleaning data, retrieving data, and also conduct analysis. To facilitate the effective functioning of a data science team, it is essential to distribute tasks among team members according to their respective areas of expertise. These tasks may encompass activities like data visualization, data preparation, model building, and more. Prior to commencing work with an organization, it is crucial for data scientists to have a clear understanding of their roles and responsibilities. This clarity ensures that everyone is aware of their specific tasks and can contribute effectively toward the team's objectives. By establishing individual responsibilities, the team can operate seamlessly and maximize productivity within the realm of data science.

In Summation

Data scientists play a crucial role in exploring and analyzing data, developing models, communicating with non-data experts, and embracing continuous learning. By addressing the aforementioned challenges through centralized data platforms, structured workflows, and clear role definitions, data scientists can overcome obstacles and contribute effectively to their teams' objectives and exert themselves. With the right skills and mindset, data scientists can thrive in the data-driven world and make a significant impact on businesses and society as a whole.

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