Written By Rachel Meltzer
What is data science? Math or science? Data science is a multidisciplinary field that combines computer science and math. The objective of data science is to pull insightful and useful knowledge out of datasets using programming languages like Python, R, Hadoop, and Spark. It’s an in-demand skillset that companies are seeking out to make smart business decisions. Data science is an umbrella term that includes data analysis, data engineering, machine learning, and more.
Around 2005, the term “big data” was coined to describe data sets that are too big to be analyzed on one computer. Back then, that seemed like a ton of data. Nowadays, internet, streaming services, wearable devices, smartphones, shopping services, and any other technology we use constantly collect data. The amount of data that humans now produce in a single day is equivalent to all of the data ever created up to the year 2003.
This data contains precious insights that can be useful for driving sales, making businesses more efficient, conducting research and more. But the massive volume of this data in combination with the complexity of processing the data can make it challenging to use and convert into business insights. Tools have been created to use in combination with programming languages to process data, sometimes even automatically.
The rise of data science all began with the combination of computer science and statistics. As early as 1962, mathematician John W. Tukey predicted the use of computers to revolutionize data analysis as an empirical science. It took nearly two decades for computers to evolve to the point of making efficient use of “big data.” Throughout the 2000s, data science gained traction as a vital emerging discipline.
Within the past decade, data science has evolved and permeated almost every single industry. Data has transformed into “big data,” computers and data centers have changed astronomically, and algorithms have become an essential part of data science.
At one point, data science jobs were reserved for those with master’s degrees or higher in statistics or computer science. Today, data scientists are invaluable to any company and are migrating from a variety of other careers and backgrounds. They come from architecture, high school teaching, marketing, graduate from a data science bootcamp like Metis, Galvanize, or Springboard, and hit the ground running in data.
Data scientists typically work in the major industries that are experiencing big growth. Since 2009, there has been a deficit of skilled and qualified data analysts entering the job market. Over the next 10 years, the Bureau of Labor Statistics expects data science occupations to grow by 31%. It’s one of the fastest growing occupations in the U.S. Some industries value data scientists more than others.
The majority of these roles are found in the tech sector. The finance industry also provides high-paying data science jobs that provide investment bankers with predictions and loss prevention strategies. Other industries that provide major roles for data scientists include manufacturing, energy, healthcare, cybersecurity, telecommunications, retail, construction, transportation, education, and government organizations.
A data scientist is someone who analyzes, organizes, and interprets complex digital data. They combine the skills of a coder, statistician, and storyteller to extract the important inferences from mountains of data and relay them in a way that non-technical people can understand. Most data scientists are employed by a company to assist in their decision-making processes.
Data scientists need to have strong mathematical skills, including linear algebra, calculus, statistics, and probability. They also need to be able to communicate to explain their findings and methods. These foundational skills are equally important but won’t get you a career on their own. Data scientists who know both Python and R along with SQL and some data science tools have the best shot at a strong career.
Data Scientists rely on one or all of these languages and tools to do their job effectively:
There are a lot of big players in the data sphere of the tech world. These companies offer solutions for data wrangling when it comes to “big data” and ways to understand relevant information within those datasets. Some data companies also provide data analysis tools or relational database management systems (RDBMS).
These are all major players in the data game, but they each serve their own purpose. Some provide services like an RDBMS (that’s a Relational Database Management System), others give enterprises a way to track customer data, while still others simply track and store user activity to provide insights to advertisers. There are a variety of exciting ways that data scientists and data analysts can get involved in projects at companies like these!
Rachel Meltzer is the founder of MeltzerSeltzer, where she is the lead writer, podcast host, and freelance writing coach.
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