Learn How a Day Looks Like in The Life of a Data Scientist

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Learn How a Day Looks Like in The Life of a Data Scientist

The career of a data scientist is highly rewarding and the most popular choice among aspiring tech enthusiasts. But do you know what a day in the life of a data scientist looks like?

The popularity of data science as a career is increasingly rapidly. Not just popularity, the demand of skilled data science professionals is also on the rise, as more and more organizations are looking to integrate data science technology into their business operations to make informed decisions. As per a study by the US Bureau of Labor Statistics, the number of data science jobs is going to reach around 11.5 million by 2026. That’s huge opportunities coming in the years ahead.

But what does the life of a data scientist look like? What do they do in their daily chores? Let’s have a sneak peek into the day of their lives.

Overview of the Role of Data Scientist

Data scientists are an integral part of any organization’s data science ecosystem. They are the main characters who lead and execute data science plans and projects. As the name suggests, students and data science enthusiasts believe that data scientists must be surrounded by data and work with data all the time in their job. However, it is not 100% correct.

Data scientists are multidisciplinary experts who have knowledge of mathematics and statistics, computer science and programming languages, and domain expertise. They are the masterminds behind the extraction of meaningful and actionable insights out of raw data and help decision-makers or stakeholders with data-driven decision-making.

For their data science skills and knowledge, they are paid handsome salaries across all industries around the globe. In the US the annual average salary of data scientists is $122635 (Indeed).

What does a data scientist do?

When you step into the shoes of a data scientist, here are some of the daily tasks you will be involved in:

  1. Data collection and preparation

You will be responsible for collecting data from various sources and cleaning it for analysis. This involves correcting repeated and missing values, merging datasets, and deleting incorrect values.

  • Data analysis and modelling

They also have to perform data analysis and data modeling using statistical methods, machine learning algorithms, etc. to find trends and patterns.

  • Data visualization

They use a variety of data science tools like Matplotlib and Tableau for data visualization and transform complex insights into easy-to-understand visuals for even non-technical stakeholders.

  • Building predictive models

They also design, build, and monitor efficient predictive models for various operations as required that can forecast future outcomes based on historical data. They are also behind the famous recommendation systems that suggest shows to watch, products to buy, and content to consume.

  • Problem solving

They use their data science knowledge and domain expertise to address various types of real-world business problems and opportunities.

They are the bridge between raw data and valuable insights. They help organizations make data-driven decisions that can improve efficiency, reduce costs, and drive innovation.

How to become a Data Scientist?

So, now that you understand the jobs performed by data scientists in their organizations, you must be eager to know how to get into a data science career.

Here is a simple roadmap to becoming a successful data scientist:

  1. Get a necessary educational qualification

To make a career in data science bachelor’s degree in IT, computer science, mathematics, and statistics is essential. Having a master’s degree can be an added advantage.

  • Gain essential data science skills

Data science is a broad field and you need to master the necessary data science skills, right from data collection, to data visualization. Master various programming languages and data science tools.

  • Learn machine learning concepts

Machine learning is an important part of data science. Learn various machine learning concepts such as regression, classification, clustering, etc., and get familiar with libraries like Scikit-learn, TensorFlow, Keras, and others.

  • Gain practical hands-on experience

Once you have mastered the theoretical knowledge, focus on gaining practical experience on real-world data science projects. This will add value to your profile and build a strong data science portfolio.

  • Validate your skill with data science certification

Earning a data science certification from a top institute such as USDSI® can be a great move in your data science career. Such certifications validate your skills and expertise and demonstrate your commitment to lifelong learning.

Conclusion

Data science is a very interesting field and the main characters of the execution of data science projects are data scientists. A day in the life of a data scientist is full of challenges, excitement, and is always new and interesting. If you are stepping into the career of a data scientist then be prepared for a roller coaster ride throughout your career journey with huge growth opportunities. Remember, the field of data science is highly dynamic and you need to upgrade yourself regularly with the latest data science skills and knowledge to stay relevant in the industry. So, register in top data science certification programs and start your data science career now.