Data Science as a Career in 2023

The Data Science market is seeing an ongoing rise in demand for Data scientists, and the industry has developed its own niche of statistical rigor combined with an engineering discipline.

A data scientist should have a solid foundation in computer science, mathematics, and statistics, as well as industry-specific domain knowledge. In order to forecast future outcomes, a data scientist must be adept at evaluating vast amounts of structured and unstructured data. As they will need to explain their findings to other team members, data scientists are also expected to be effective communicators.

Analytics that are descriptive and predictive are only a small portion of a data scientist’s work. Some data scientists may work in the field of deep learning, running repetitive experiments to use special algorithms to address complicated data problems.

Data scientists make an average yearly salary of $124,991 (total pay), and there are approximately 105,980 data scientists in the US, according to the US Bureau of labor statistics.

Industry and the Job Market

Data science is a field of technology that is always developing and changing. We must examine the past and comprehend the factors that have molded the industry up to this point in order to predict how it will change in the future.

The concept of combining computer science and applied statistics gave rise to the field of data science. The incredible capacity of contemporary computing would be utilized in the resulting field of study. Scientists came to the realization that they could utilize data not just to collect data and address statistical issues but also to address issues in the actual world and generate accurate fact-based predictions.

The incorporation of ideas like machine learning, artificial intelligence, and the internet of things, along with the accessibility of Big Data, had a role in the emergence of data science. Later, Spark and Cassandra made their debuts after Hadoop successfully met the challenge. Data science began to spread to other industries, including medicine, engineering, and more, as a result of the influx of fresh information and businesses searching for innovative strategies to boost profits and make better judgments.

We can understandably wonder, “Where do we go from here?” given the extent to which data and data science currently power our world. There has never been a better time to join the data science revolution, which is still in its infancy. The field of data science is dynamic and ever-expanding, and it is becoming more and more important. This creates a huge demand for skilled workers.

Aspiring students now have a rare opportunity thanks to the high demand for data scientists and the shortage of qualified workers. And demand will continue to rise due to the increasing use of data science applications across a variety of businesses and organizations.

Plan Your Career Path and Specialization

The most straightforward way to become a data scientist is through formal education like taking a Data science specialized degree in a college or a BootCamp. Recently, through the availability of open-source resources and materials that are freely available, it is possible to enter this industry through self-preparation as well. 

Review Your Data Science Foundations

• Data Structures and Algorithms (Python, R)
• Basics of Data Mining and Visualization (Pandas, Numpy, Plotly, Matplotlib, etc)
• Data Analysis and Dashboarding (Tableau, Looker, Power BI, etc)
• Statistics for Practical Data Science
• Machine Learning, Deep Learning, and Natural Language Processing
• Basics of Data Engineering (SQL, ability to deploy data pipelines)

Develop Your Portfolio and Work on Projects to Acquire Experience

• College Projects or Bootcamps
• Kaggle, Hackathons
• Internships

Get Ready for the Data Science Interview and Ace It

A data scientist entering the field will need to be able to program in Python and execute intricate statistical analyses on huge datasets. Building smart data visualizations to communicate stories and running SQL queries and web scraping to explore and extract data from databases and websites are essential for increasing operational efficiency.

Once they enter the field, every data scientist will need to advance to operational analytics for product and business improvements, automating machine learning algorithms and creating methods for predictive modeling. It is now statistically necessary to run A/B tests and incrementality tests when testing products. Data scientists today have access to a wide range of applications, including customer segmentation research, user churn modeling, lifetime value analysis, inventory management, and optimization, and metric trend forecasts.

Strategize and Deploy

Nowadays, the industry follows a very conventional interviewing procedure. In the past, data science interviews varied greatly between companies. Some employers required you to code on a whiteboard, while others didn’t even ask you any questions about programming during the interview. To determine the skills required for a particular data science function, the majority of firms have developed an efficient and open interview procedure. During the interview, companies will also go over the position and duties, tool usage, and daily tasks.

The “sexiest job of the 21st century,” according to the Harvard Business Review in October 2012, was a data scientist. This still holds true, but the sexiness is now replaced by an engineering regor that has only added to its allure.