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As the modern economy becomes more driven by digital activity, data has begun to play a larger role in many industries. Even “old guard” industries like manufacturing and retail are increasingly dependent on high-quality data, and the demand for data scientists is rising accordingly.
But what is data science, and what do careers in this field look like? Here’s what you need to know.
What Is Data Science?
Data science is a very broad field, encompassing everything from entry level data-wrangling positions to sophisticated data engineering posts requiring high-level degrees. Most data science positions involve some combination of organizing, storing, and analyzing data sets. Data scientists may also occasionally be tasked with collecting data.
Should You Consider a Career in Data Science?
Anyone with an affinity for technology or numbers should at least consider a career in data science. The demand for data scientists is exploding – analysts expect that the industry will have 1.5 million more open jobs than applicants by 2018, with each of those openings representing an excellent opportunity for qualified job seekers. You also don’t need a specific degree to be successful; data scientists come from a wide variety of backgrounds, including economics, mathematics, statistics, computer science, business, and engineering.
Career Paths in Data Science
If you find a job posting for a “data scientist,” it’s likely that the employer is hiring their first data-centric positions. In this case, as a “jack of all trades,” you may be responsible for a wide variety of tasks. However, most postings will specify a clearer focus – usually in one of the four areas outlined below.
Data Analyst
Data analysts are most often responsible for transforming data sets into usable forms, such as reports or presentations. Depending on the industry, that might mean gleaning insight from consumer data sets, making strategic recommendations based on dense financial data, or simply organizing messy data into a more accessible format. Data analyst positions don’t often require programming experience, but it can still be beneficial, depending on the employer.
A data analyst position is a good fit if you have a numbers-focused educational background but not much experience in programming or other technology disciplines. Experience with Excel and other consumer-level data presentation programs is also helpful. These positions are often on the lower end of the organizational chart – but if you’re just getting started in data science, they can also be among the easier roles to qualify for, and you’ll have plenty of opportunity to learn and advance into higher-level roles.
The career outlook for data analysts is excellent, and job openings are expected to grow significantly in the next few years. The average salary for data analysts in the U.S. is around $56,000, but you can expect that number to rise as demand for applicants grows.
Business Analyst
A business analyst is usually very similar to a data analyst, but with a clear directive to focus on advancing strategic business objectives.
A typical day in a business analyst’s job might include analyzing the impact of a marketing campaign, using complex data sets to predict future industry developments, or modeling likely outcomes from a range of potential business plans. Most business analysts are focused on producing usable deliverables, such as reports and presentations, that can be easily understood by others in the organization who aren’t data scientists themselves.
Business analysis can be an excellent career choice if you have a strong foundation in numbers and an active interest in business management or business development. The career outlook is very good, as employers in every industry are increasingly reliant on data to guide their business decisions. The average salary for business analysts in the U.S. is $59,000, with a good outlook for higher salaries in the future.
Machine Learning Engineer
As companies try to find meaningful signals in terabytes of data, automation of data organization and analysis is becoming increasingly important. Machine learning engineers are tasked with building data systems that can provide insights and intelligence with less direct human oversight, using what we often think of as “artificial intelligence.”
Modern search engines are a good example of machine learning, as their algorithms use information about how users respond to searches to improve future search results. More complicated machine learning systems are used to analyze legal documents, identify likely candidates in astronomy’s search for Earth-like planets, and more. In each case, the key characteristic of machine learning is that the system uses data about its past activity to improve its future performance.
Most machine learning engineers are focused much less on analysis, and much more on technology and programming. Programming skills are usually a top requirement for these roles, and a degree in programming, software development, or a related field will be very helpful.
The average salary for machine learning engineers is around $115,000 and can be expected to grow sharply over the next 10 years.
Data Infrastructure Engineer
Data infrastructure engineers are focused on the systems and hardware that facilitates a company’s data activities, rather than on analysis of the data itself.
Data infrastructure engineers typically work on “big data” projects, overseeing the hardware and software that stores, processes and analyzes a company’s vast repository of digital data. While data architects design those systems, the engineers are responsible for maintaining them and expanding them when necessary. Engineers are also often required to analyze systems to ensure they’re operating at peak efficiency and to identify areas that need improvement.
Technology and programming skills are essential for these positions, and most employers also expect engineers to be very comfortable working with computer hardware. Most job listings in this field are looking specifically for candidates with advanced technical degrees.
Salaries vary widely, but you can expect a range from $80,000 to $100,000 or more.
Start Learning
Data science is an exciting field with a huge range of job opportunities – get started today by exploring Coursera’s catalog of data science courses.
The post An Introduction to Data Science Careers appeared first on Coursera Blog.
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But what is data science, and what do careers in this field look like? Here’s what you need to know.
What Is Data Science?
Data science is a very broad field, encompassing everything from entry level data-wrangling positions to sophisticated data engineering posts requiring high-level degrees. Most data science positions involve some combination of organizing, storing, and analyzing data sets. Data scientists may also occasionally be tasked with collecting data.
Should You Consider a Career in Data Science?
Anyone with an affinity for technology or numbers should at least consider a career in data science. The demand for data scientists is exploding – analysts expect that the industry will have 1.5 million more open jobs than applicants by 2018, with each of those openings representing an excellent opportunity for qualified job seekers. You also don’t need a specific degree to be successful; data scientists come from a wide variety of backgrounds, including economics, mathematics, statistics, computer science, business, and engineering.
Career Paths in Data Science
If you find a job posting for a “data scientist,” it’s likely that the employer is hiring their first data-centric positions. In this case, as a “jack of all trades,” you may be responsible for a wide variety of tasks. However, most postings will specify a clearer focus – usually in one of the four areas outlined below.
Data Analyst
Data analysts are most often responsible for transforming data sets into usable forms, such as reports or presentations. Depending on the industry, that might mean gleaning insight from consumer data sets, making strategic recommendations based on dense financial data, or simply organizing messy data into a more accessible format. Data analyst positions don’t often require programming experience, but it can still be beneficial, depending on the employer.
A data analyst position is a good fit if you have a numbers-focused educational background but not much experience in programming or other technology disciplines. Experience with Excel and other consumer-level data presentation programs is also helpful. These positions are often on the lower end of the organizational chart – but if you’re just getting started in data science, they can also be among the easier roles to qualify for, and you’ll have plenty of opportunity to learn and advance into higher-level roles.
The career outlook for data analysts is excellent, and job openings are expected to grow significantly in the next few years. The average salary for data analysts in the U.S. is around $56,000, but you can expect that number to rise as demand for applicants grows.
Business Analyst
A business analyst is usually very similar to a data analyst, but with a clear directive to focus on advancing strategic business objectives.
A typical day in a business analyst’s job might include analyzing the impact of a marketing campaign, using complex data sets to predict future industry developments, or modeling likely outcomes from a range of potential business plans. Most business analysts are focused on producing usable deliverables, such as reports and presentations, that can be easily understood by others in the organization who aren’t data scientists themselves.
Business analysis can be an excellent career choice if you have a strong foundation in numbers and an active interest in business management or business development. The career outlook is very good, as employers in every industry are increasingly reliant on data to guide their business decisions. The average salary for business analysts in the U.S. is $59,000, with a good outlook for higher salaries in the future.
Machine Learning Engineer
As companies try to find meaningful signals in terabytes of data, automation of data organization and analysis is becoming increasingly important. Machine learning engineers are tasked with building data systems that can provide insights and intelligence with less direct human oversight, using what we often think of as “artificial intelligence.”
Modern search engines are a good example of machine learning, as their algorithms use information about how users respond to searches to improve future search results. More complicated machine learning systems are used to analyze legal documents, identify likely candidates in astronomy’s search for Earth-like planets, and more. In each case, the key characteristic of machine learning is that the system uses data about its past activity to improve its future performance.
Most machine learning engineers are focused much less on analysis, and much more on technology and programming. Programming skills are usually a top requirement for these roles, and a degree in programming, software development, or a related field will be very helpful.
The average salary for machine learning engineers is around $115,000 and can be expected to grow sharply over the next 10 years.
Data Infrastructure Engineer
Data infrastructure engineers are focused on the systems and hardware that facilitates a company’s data activities, rather than on analysis of the data itself.
Data infrastructure engineers typically work on “big data” projects, overseeing the hardware and software that stores, processes and analyzes a company’s vast repository of digital data. While data architects design those systems, the engineers are responsible for maintaining them and expanding them when necessary. Engineers are also often required to analyze systems to ensure they’re operating at peak efficiency and to identify areas that need improvement.
Technology and programming skills are essential for these positions, and most employers also expect engineers to be very comfortable working with computer hardware. Most job listings in this field are looking specifically for candidates with advanced technical degrees.
Salaries vary widely, but you can expect a range from $80,000 to $100,000 or more.
Start Learning
Data science is an exciting field with a huge range of job opportunities – get started today by exploring Coursera’s catalog of data science courses.
The post An Introduction to Data Science Careers appeared first on Coursera Blog.
Continue reading...
