What is Data Science?

Table of Contents

• What is big data?
• A Brief History of Data Science
• How Has Data Science Evolved?
• The Increasing Demand for Data Science
• What is a Data Scientist?
• The 6 Largest Data Science Companies of 2021

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. 

What is big data?

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. 

A Brief History of Data Science

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. 

How Has Data Science Evolved?

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. 

The Increasing Demand for Data Science

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. 

What is a Data Scientist?

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:

• SQL – Structured Query Language (SQL) is used to communicate with relational database management systems (RDBMS) and extract data from large databases to use for reports and data analytics. 

• Apache Spark – Spark is used to write parallel programs that run in clusters (on a network of computers in a data center). Its powerful machine learning library, mllib, can be used with R to efficiently solve a variety of problems.

• Hadoop – Hadoop is a whole suite of technologies built for managing data and executing programs in a cluster (on a network of computers in a data center). This includes a file system designed for big data, MapReduce for running programs in parallel, and the HIVE database that is similar to SQL, along with many other components. 

• R – R is a standard programming language used in data science for statistics focused problems. With its libraries, R can be an efficient way to conduct mathematical data science practices. 

• Python – Python is a general purpose programming language used by data scientists for algorithms, automation, and more. It can also be used for some web development tasks. It’s one of the most beginner-friendly programming languages and offers a massive community who have created helpful libraries and frameworks for data science.

• Machine Learning – Machine learning refers to the practice of using algorithms to teach a machine how to improve and analyze large sets of data and automate some data science practices. Rising in popularity, machine learning allows data scientists to make predictions about future events that far exceeds the capabilities of traditional statistics. 

The 6 Largest Data Science Companies of 2021

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). 

• IBM – IBM supplies analytics solutions. They focus on ensuring a solid foundation for data while making simple and accessible scaled insights for their customers. 

• Salesforce – Salesforce is a well-known Customer Relationship Management (CRM) platform for large companies. They provide a way for companies to log, manage, and analyze customer data and activity. 

• Alteryx – Alteryx is an analytics platform that provides end-to-end solutions for Business Analysts and big data scientists. Multiple teams can work together through their programs to find solutions within their data. 

• Cloudera – Cloudera is a cloud platform provider for analytics and machine learning. Their technology gives companies a comprehensive understanding of the data they collect in one dashboard with clearer insights and better data protection. 

• Google - Google is obviously more than a search engine nowadays. They store data, provide organization applications, collect big data, create artificial intelligence programs, and use machine learning to continue to improve their products and innovation. 

• Oracle - Oracle builds relational database management systems (RDBMS) for large corporations. Their enterprise cloud platform allows companies to make use of their data with visualizations, machine learning models, and predictive analytics. 

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!