Data science concepts are the building blocks for a variety of careers that work with data. Data science is behind everything from modern marketing tactics to video streaming recommendation engines. It’s also used to train self-driving cars and analyze the results of drug trials.
Big data, which we as humans produce at a rate of over 1 megabyte per second for every person on the planet, has transformed data collection and analysis rapidly. It’s creating millions of jobs as businesses and government entities want to use data to transform the way that they work. Someone needs to harness all of that data.
This massive transformation makes data science one of the most important skills that you can learn right now. The supply of data scientists is much lower than the demand for their skills. Job growth for data analysts is expected to increase exponentially by 2026 with a projection of over 11.5 million job openings in the US.
Data careers can be broken down into a few categories: scientists, analysts, and engineers. Scientists work with the mathematical and scientific components of data within research and business realms. Analysts work with data to make predictions and provide insights. Engineers work with data to create systems and teach machines how to automate tasks.
Data Scientists and Data Analysts apply data science concepts every day. Some data scientists specialize in a certain type of data science such as machine learning or data analysis. They rely on algorithms and models to create data-driven recommendations, all of which can be categorized as data science.
Data Engineers create the infrastructure for data systems. They store, organize, and make data accessible for their teams. They utilize data science to understand how data works and what their fellow data professionals will need from their systems.
A Machine Learning Engineer is technically a Data Scientist, but one who dives deeper. Machine Learning Engineers train computers using datasets and algorithms. They harness data science to teach machines how to recognize patterns or automate data science tasks to make systems more efficient and helpful.
Data science is the core foundation for these jobs, but you’ll also need to learn the technology that comes along with these concepts. This includes programming languages such as Python or R and tools like Tableau and Jupyter Notebooks.
Data Science Career Options
Here are a few career paths for which you’ll likely need Data Science skills in order to succeed. We also include additional info about their average salaries, their day-to-day responsibilities, and a link to learn more about each career.
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Machine Learning Engineer
Indeed.com Avg. Salary
$170K / year
Glassdoor Avg. Salary
$120K / year
Machine Learning Engineers Create computer programs that enable machines to take actions without being specifically directed to perform those tasks. This job combines computer programming and data science to enable systems to learn and improve from experience automatically using machine learning—a subset of artificial intelligence.
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Data Engineer
Indeed.com Avg. Salary
$128K / year
Glassdoor Avg. Salary
$105K / year
Data Engineers create the infrastructure for data and format data into a useful system that Data Scientists use to analyze large amounts of data. Data Engineers can specialize in pipelines, databases, platforms, data warehouses, infrastructure, or be generalists.
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Data Scientist
Indeed.com Avg. Salary
$127K / year
Glassdoor Avg. Salary
$114K / year
Data Scientists collect, organize, and analyze large sets of data, providing analysis that is key to decision-making. Governments, non-profits, and businesses of all types rely on data for forecasting, risk management, and resource allocation. Data Scientists discover and analyze trends in data and report their findings to stakeholders. They use algorithms and models to simplify and mine datasets, creating data-driven recommendations. Data Scientists are needed across a variety of industries, especially with the ubiquity of data and its critical role in business decision-making.