What does "machine learning" really mean? Step inside for a closer look.
We are now living in the age of big data. According to The Worldwide Semiannual Big Data and Analytics Spending Guide, revenues for big data and business analytics solutions are expected to reach $189.1 billion this year, which is a 12 percent increase from 2018. The report also predicts that worldwide data and business analytics revenue will reach $274.3 billion by 2022.
Clearly, more companies are looking to leverage information to make strategic, data-driven decisions, which is fueling demand for professionals with data skills. Data Scientists, in fact, were named the number one most promising job in 2019 by Linkedin. The profession is also one of the highest-paying entry-level jobs in America, with salaries ranging from $95,000 to $250,000.
To help you navigate the opportunities in this relatively new field, we’ve broken down four of the top data science jobs in demand today.
What they do: Data Engineers collect, store, and organize data. Job ads for Data Engineers will typically list a range of responsibilities, including the ability to source external data, build data warehouses, and design data models – three tasks that build a foundation for data analytics and machine learning (we’ll cover those roles next).
Skills needed: Data Engineers will benefit from a background in computer science, math, or engineering, knowledge of coding languages like Structured Query Language (SQL, a programming language used in database management) Python, Java or Ruby, and the ability to manage and design databases.
What they do: Once information is organized and accessible thanks to the work of a Data Engineer, Data Analysts transform data into insights that can solve problems, optimize products, and help make evidence-based decisions. Data Analysts can take complex information and turn it into stats that business execs can use to inform strategy and planning.
Analysts also create easy-to-understand data visualizations like charts and graphs. Similar jobs include Operations Research Analysts and Business Intelligence Analysts.
Skills needed: Knowledge of SQL is the foundation for a career in data analytics. Also essential is knowledge of other coding languages, including Python or R and the ability to create data visualizations using software like Tableau.
What they do: Depending on the company, a Data Scientist might be expected to do the work of a Data Engineer and Data Analyst (collect, organize, and analyze data) in addition to more strategic data work. Where the Data Scientist role differs from the Data Analyst and Engineer’s role is in the Data Scientist’s ability to lead a company’s big data strategy by asking the right questions and developing new ideas, products, and services.
Find out more about the difference between a Data Analyst and Data Scientist in this BrainStation blog post.
Skills needed: Data Scientists cited Python as the most frequently used tool in BrainStation’s 2019 Digital Skills Survey. Other skills needed include most of the tools and skills listed above (SQL, Tableau, other programming languages, and an understanding of how databases are built and maintained), as well as strong communication skills and business acumen.
Machine Learning Engineer
What they do: You probably won’t be surprised to learn that there are also similarities between Data Scientists and Machine Learning Engineers. Both work with data to produce insights. The difference is that Data Scientists uncover insights to present to people (for example, CEOs and other business leaders), while Machine Learning Engineers design the software that can uncover insights and learn from results as more and more data is gathered.
Skills needed: According to Robert Half, Machine Learning Engineers need advanced math skills, programming skills (Python, R, Java), knowledge of Hadoop, data modeling experience, and experience working in an Agile environment.
Interested in learning more about data science? Check out BrainStation’s data diploma programs and certificate courses.