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Career Guide - Data Science as a Profession

Career Guide: Data Science as a Profession

The analysis of enormous amounts of information is the domain of data scientists, who employ statistical approaches. They frequently contribute to initiatives that need the analysis of large data sets, which may comprise anything from posts on social networking platforms to medical information.

What is Data Science?

Data science is a discipline that combines computer programming and statistics. Data collection, storage, processing, and analysis are all done using computers. This kind of analysis gives businesses more precise predictions, which aids in better decision-making.
This essay will cover the fundamentals of data science and how it can assist you in making an excellent career decision.
What exactly is Data Science?
The term “data science” was first coined by Thomas H. Davenport in his book “Big Data: A Revolution That Will Transform How We Live, Work, and Think” (2014). In this book, he defines “data science” as the application of advanced analytics to extract knowledge from large amounts of data.
Data scientists are trained in statistics, mathematics, computer programming, and other disciplines, enabling them to analyze large datasets and make sense of them. They use a variety of tools, such as machine learning algorithms, statistical models, and visualization techniques, to solve problems.

What do Data Scientists do?

The following are some of the fundamental activities that fall under a data scientist’s purview:

  • collecting and preparing data for an analysis that can be used in many different ways
  • using many different types of analysis to find hidden patterns, trends, and connections in data sets;
  • creating statistical and predictive models that can be tested on the data sets; and
  • producing reports, and data visualizations

According to Glassdoor, a Data Scientist will

  • Collaborate with key stakeholders located throughout the organization to locate potential avenues for utilizing data held by the company to drive business solutions.
  • Use data mining to analyze the data, so that product development, marketing methods, and business strategies can be optimized and made better.
  • Evaluate how useful and accurate the new data sources and ways of collecting data are.
  • Construct individualized data models and algorithms for application to data sets. Use predictive modeling to improve and maximize the number of business outcomes, such as interactions with customers, the creation of revenue, the targeting of ads, and other things.
  • Build a framework for A/B testing for the company and evaluate how well it works.
  • Keep in touch with the different functional teams so that you can apply models and keep an eye on the results.
  • Make systems and tools that can track and analyze both how well the model works and whether or not the data is correct.

Why is Data Science a good career choice?

One of the professions that are expanding the quickest in the United States is data science. According to projections by the Bureau of Labor Statistics (BLS), the number of available positions for data scientists is anticipated to increase by 31% between 2014 and 2024. This growth could be because big data analytics are becoming more important in the private and public sectors.

According to the Bureau of Labor Statistics (BLS), the median annual income for a data scientist in 2021 was $100,910. However, this number does not consider the significant disparity in pay between the various types of businesses. Google, for example, pays its employees an average annual salary of 212,054 dollars [source: Glassdoor]. On the other hand, Amazon only pays a yearly sum of 188,209 dollars [source: Glassdoor].

Furthermore, numerous benefits come with being a data scientist at one of the world’s most successful businesses. For example, you will have the opportunity to work on projects that have a global impact and assist individuals in various countries. And of course, most of us want to make money off of our expertise and knowledge.

Technical skills required for data scientists

  • Statistical analysis and computing.
  • Machine Learning.
  • Deep Learning.
  • Processing large data sets.
  • Data Visualization.
  • Data Wrangling.
  • Mathematics.
  • Programming.

Best YouTube channels for Data Science

Author: Chris Pahla, in partnership with Touching Lives Inc