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 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 careers: 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 essentially store, organize, and make data accessible for their teammates. 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 that dives deeper. Machine Learning Engineers train computers through data sets and algorithms. They harness data science to teach machines how to recognize patterns or automate data science tasks in order 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.