Their primary focus is to build free-flowing data pipelines by combining a variety of big data technologies that enable real-time analytics. Data engineers also write complex queries to ensure that data is easily accessible.<\/p>\n\n\n
Get To Know Other\tData Science Students<\/h4>![\"Hastings](\"https:\/\/res.cloudinary.com\/springboard-images\/image\/upload\/v1648517255\/Student%20Success\/Hastings_Reeves_3.png\")
<\/a>Hastings Reeves<\/p>
Business Intelligence Analyst at Velocity Global<\/p><\/div>
<\/div>Read Story<\/a><\/p><\/div><\/div>![\"Meghan](\"https:\/\/res.cloudinary.com\/springboard-images\/image\/upload\/v1629203464\/Student%20Success\/Megan_Thomason_125x125.png\")
<\/a>Meghan Thomason<\/p>
Data Scientist at Spin<\/p><\/div>
Read Story<\/a><\/p><\/div><\/div>![\"Jonah](\"https:\/\/res.cloudinary.com\/springboard-images\/image\/upload\/v1680561342\/Jonah_Winninghoff.png\")
<\/a>Jonah Winninghoff<\/p>
Statistician at Rochester Institute Of Technology<\/p><\/div>
Read Story<\/a><\/p><\/div><\/div><\/div><\/div>\n\n\n\nWhat Does a Data Scientist Do?<\/h4>\n\n\n\n
Data scientists<\/a> concentrate on finding new insights from the data that was prepared for them by data engineers. As part of their job, they conduct online experiments, develop hypotheses, and use their knowledge of statistics, data analytics, data visualization, and machine learning algorithms to identify trends and create forecasts for the business.
They also engage with business leaders to understand their specific needs and present complex findings, both verbally and visually, in a manner that can be followed by a general business audience.<\/p>\n\n\n\nEducation and Requirements<\/h3>\n\n\n\n![\"\"](\"https:\/\/www.springboard.com\/blog\/wp-content\/uploads\/2022\/02\/education-2-scaled.jpg\")
<\/figure>\n\n\n\nMany data engineers and data scientists hold a bachelor\u2019s degree in computer science or a related field such as mathematics, statistics, economics, or information technology. And while employers often look for candidates with advanced degrees, it is possible to land a role in data science<\/a> or data engineering without a degree.<\/p>\n\n\n\nWhat Are the Requirements To Become a Data Engineer?<\/h4>\n\n\n\n
Data engineers usually hail from a software engineering background and are proficient in programming languages like Java, Python, SQL, and Scala. Alternatively, they might have a degree in mathematics or statistics that helps them apply different analytical approaches to solve business problems.<\/p>\n\n\n\n
To get hired as a data engineer<\/a>, most companies look for candidates with a bachelor\u2019s degree in computer science, applied math, or information technology. Candidates may also be required to have a few data engineering certifications, like Google\u2019s Professional Data Engineer<\/a> or IBM Certified Data Engineer<\/a>. It also helps if they are experienced in building big data warehouses that can run some Extract, Transform, and Load, or ETL, on top of big data sets.<\/p>\n\n\n\nWhat Are the Requirements To Become a Data Scientist?<\/h4>\n\n\n\n
\u200b\u200bData scientists are usually presented with large volumes of data without any particular business problems to solve. In this scenario, the data scientist will be expected to explore the data, formulate the right questions, and present their findings. This makes it essential for data scientists to have a broad knowledge of different techniques in big data infrastructures, data mining, machine learning algorithms, and statistics. As they also have to work with data sets that come in various forms<\/a> to run their algorithms effectively and efficiently, they also need to be up-to-date with all the latest technologies.<\/p>\n\n\n\nData scientists are expected to be proficient in programming languages such as SQL, Python, R, and Java, and be familiar with tools such as Hive, Hadoop, Cassandra, and MongoDB. <\/p>\n\n\n\n
Data Scientist vs. Data Engineer Salary<\/h3>\n\n\n\n
For the analytical mind, both positions offer a highly rewarding and lucrative career. <\/p>\n\n\n\n
What Does a Data Engineer Earn?<\/h4>\n\n\n\n
Data engineers\u2019 salaries depend on variables such as the type of role, relevant experience, and where the job is located. According to Glassdoor<\/a>, the average salary for a data engineer is about $142,000 per year.<\/p>\n\n\n\nWhat Does a Data Scientist Earn?<\/h4>\n\n\n\n
Again, what data scientists earn also depends on the type of job, their skills, qualifications, and where it\u2019s located. According to Glassdoor<\/a>, on average, a data scientist makes about $139,000 per year.<\/p>\n\n\n\nCareer Paths<\/h3>\n\n\n\n
There is no one set path to becoming a data engineer or a data scientist, but below are some of the common ways in which people have navigated the field to get to their dream jobs.<\/p>\n\n\n\n
What\u2019s a Typical Career Path for a Data Engineer?<\/h4>\n\n\n\n
Data engineering is not usually an entry-level role. Because of this, many data engineers get their start in software engineering or business intelligence\/systems analytics \u2014 roles that give them exposure to the systems and infrastructure that are crucial to the field of data science.
Many data engineers take advantage of roles such as data architect, solutions architect, and database developer to perfect their data engineering skills, develop a deeper knowledge of data processing and cloud computing, and gain experience with ETL and data layers. Some may also work in data analytics to bolster their knowledge of what data analysts and data scientists need before transitioning into data engineering.<\/p>\n\n\n\n
What\u2019s a Typical Career Path for a Data Scientist?<\/h4>\n\n\n\n
Many data scientists get their start in an entry-level data science role, whether through an internship or getting hired as a junior data scientist. These entry-level positions give new data scientists the opportunity to continue developing their technical skills and to work on projects assigned to them before they advance to designing their own experiments and solving more ambitious business problems.
Data analysts commonly pivot into data science roles<\/a> either by teaching themselves the relevant skills or by enrolling in an online data science course or bootcamp<\/a>.\u00a0<\/p>\n\n\n\nRelated Read: <\/em>Data Analyst vs. Data Scientist: Salary, Skills, & Background <\/a><\/em><\/p>\n\n\n\nCan a Data Engineer Become a Data Scientist (or Vice Versa)?<\/h2>\n\n\n\n![\"Data](\"https:\/\/www.springboard.com\/blog\/wp-content\/uploads\/2021\/12\/Can-a-data-engineer-become-a-data-scientist-or-vice-versa-scaled.jpg\")
<\/figure>\n\n\n\nThe short answer is yes, data engineers can become data scientists and vice versa, with some additional training. The overlap in skills \u2014 from knowledge of programming languages to working with data pipelines \u2014 means that members of both professions are equipped with the foundational knowledge and vocabulary to have a relatively easy career transition. But, given that data engineers have a greater focus on the architecture and infrastructure that supports the work of data scientists, and data scientists are more concerned with developing and testing hypotheses through data, both professions would have to brush up on additional skills<\/a> before that can make the leap.<\/p>\n\n\n\nData Scientist vs. Data Engineer: Which Is Best for You?<\/h2>\n\n\n\n![\"Data](\"https:\/\/www.springboard.com\/blog\/wp-content\/uploads\/2021\/12\/Data-Scientist-vs.-Data-Engineer-Which-Is-Best-for-You-scaled.jpg\")
<\/figure>\n\n\n\nDespite the overlap in skills between the two professions, data scientists and data engineers have different responsibilities, and the roles may be better suited to certain personality types<\/a>.<\/p>\n\n\n\nConsider Being a Data Engineer if\u2026<\/h3>\n\n\n\n
Data engineers deal mostly with the infrastructure and architecture that stores and organizes data. They are strong coders who like learning and using new technologies, enjoy discovering new ways to make software and systems more efficient, and thrive on helping an organization save time and resources. If you\u2019re a tinkerer who\u2019s always looking for ways to improve the things you build, find purpose in creating the supportive tools that help others do their job, and love playing with the latest tools and technologies, then data engineering might be the right career for you. <\/p>\n\n\n\n
Consider Being a Data Scientist if\u2026<\/h3>\n\n\n\n
Data scientists are analytical thinkers who are curious, aren\u2019t afraid of asking questions, and live for putting their hypotheses to the test. Data scientists not only use data to make sense of things that have happened in the past, they also forecast trends and try to understand what might happen in the future. If you enjoy running advanced statistical analyses, writing machine learning algorithms, and using creativity to solve problems, then a career as a data scientist might be right for you.<\/p>\n\n\n\n
Since you\u2019re here\u2026
<\/strong>Curious about a career in data science? Experiment with our free data science learning path<\/a>, or join our Data Science Bootcamp<\/a>, where you\u2019ll get your tuition back if you don’t land a job after graduating. We\u2019re confident because our courses work \u2013 check out our student success stories<\/a> to get inspired.<\/p>\n","protected":false},"excerpt":{"rendered":"Careers within the field of data science have in recent years seen soaring demand, with the Bureau of Labor Statistics forecasting a 22% increase in job growth from 2020-2030\u2014much higher than the average growth of other occupations. As companies continue to focus on generating, collecting, and analyzing big data to help them run their businesses, […]<\/p>\n","protected":false},"author":100,"featured_media":14103,"comment_status":"closed","ping_status":"closed","sticky":true,"template":"","format":"standard","meta":{"_acf_changed":false,"_eb_attr":"","_eb_data_table":"","footnotes":""},"categories":[67],"tags":[],"marketing_tags":[1466],"class_list":{"0":"post-6113","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-data-science"},"acf":[],"_links":{"self":[{"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/posts\/6113"}],"collection":[{"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/users\/100"}],"replies":[{"embeddable":true,"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/comments?post=6113"}],"version-history":[{"count":4,"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/posts\/6113\/revisions"}],"predecessor-version":[{"id":56407,"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/posts\/6113\/revisions\/56407"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/media\/14103"}],"wp:attachment":[{"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/media?parent=6113"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/categories?post=6113"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/tags?post=6113"},{"taxonomy":"marketing_tags","embeddable":true,"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/marketing_tags?post=6113"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
<\/a>Hastings Reeves<\/p>
Business Intelligence Analyst at Velocity Global<\/p><\/div>
Read Story<\/a><\/p><\/div><\/div> Meghan Thomason<\/p> Data Scientist at Spin<\/p><\/div> Read Story<\/a><\/p><\/div><\/div> Jonah Winninghoff<\/p> Statistician at Rochester Institute Of Technology<\/p><\/div> Read Story<\/a><\/p><\/div><\/div><\/div><\/div>\n\n\n\n Data scientists<\/a> concentrate on finding new insights from the data that was prepared for them by data engineers. As part of their job, they conduct online experiments, develop hypotheses, and use their knowledge of statistics, data analytics, data visualization, and machine learning algorithms to identify trends and create forecasts for the business. Many data engineers and data scientists hold a bachelor\u2019s degree in computer science or a related field such as mathematics, statistics, economics, or information technology. And while employers often look for candidates with advanced degrees, it is possible to land a role in data science<\/a> or data engineering without a degree.<\/p>\n\n\n\n Data engineers usually hail from a software engineering background and are proficient in programming languages like Java, Python, SQL, and Scala. Alternatively, they might have a degree in mathematics or statistics that helps them apply different analytical approaches to solve business problems.<\/p>\n\n\n\n To get hired as a data engineer<\/a>, most companies look for candidates with a bachelor\u2019s degree in computer science, applied math, or information technology. Candidates may also be required to have a few data engineering certifications, like Google\u2019s Professional Data Engineer<\/a> or IBM Certified Data Engineer<\/a>. It also helps if they are experienced in building big data warehouses that can run some Extract, Transform, and Load, or ETL, on top of big data sets.<\/p>\n\n\n\n \u200b\u200bData scientists are usually presented with large volumes of data without any particular business problems to solve. In this scenario, the data scientist will be expected to explore the data, formulate the right questions, and present their findings. This makes it essential for data scientists to have a broad knowledge of different techniques in big data infrastructures, data mining, machine learning algorithms, and statistics. As they also have to work with data sets that come in various forms<\/a> to run their algorithms effectively and efficiently, they also need to be up-to-date with all the latest technologies.<\/p>\n\n\n\n Data scientists are expected to be proficient in programming languages such as SQL, Python, R, and Java, and be familiar with tools such as Hive, Hadoop, Cassandra, and MongoDB. <\/p>\n\n\n\n For the analytical mind, both positions offer a highly rewarding and lucrative career. <\/p>\n\n\n\n Data engineers\u2019 salaries depend on variables such as the type of role, relevant experience, and where the job is located. According to Glassdoor<\/a>, the average salary for a data engineer is about $142,000 per year.<\/p>\n\n\n\n Again, what data scientists earn also depends on the type of job, their skills, qualifications, and where it\u2019s located. According to Glassdoor<\/a>, on average, a data scientist makes about $139,000 per year.<\/p>\n\n\n\n There is no one set path to becoming a data engineer or a data scientist, but below are some of the common ways in which people have navigated the field to get to their dream jobs.<\/p>\n\n\n\n Data engineering is not usually an entry-level role. Because of this, many data engineers get their start in software engineering or business intelligence\/systems analytics \u2014 roles that give them exposure to the systems and infrastructure that are crucial to the field of data science. Many data scientists get their start in an entry-level data science role, whether through an internship or getting hired as a junior data scientist. These entry-level positions give new data scientists the opportunity to continue developing their technical skills and to work on projects assigned to them before they advance to designing their own experiments and solving more ambitious business problems. Related Read: <\/em>Data Analyst vs. Data Scientist: Salary, Skills, & Background <\/a><\/em><\/p>\n\n\n\n The short answer is yes, data engineers can become data scientists and vice versa, with some additional training. The overlap in skills \u2014 from knowledge of programming languages to working with data pipelines \u2014 means that members of both professions are equipped with the foundational knowledge and vocabulary to have a relatively easy career transition. But, given that data engineers have a greater focus on the architecture and infrastructure that supports the work of data scientists, and data scientists are more concerned with developing and testing hypotheses through data, both professions would have to brush up on additional skills<\/a> before that can make the leap.<\/p>\n\n\n\n Despite the overlap in skills between the two professions, data scientists and data engineers have different responsibilities, and the roles may be better suited to certain personality types<\/a>.<\/p>\n\n\n\n Data engineers deal mostly with the infrastructure and architecture that stores and organizes data. They are strong coders who like learning and using new technologies, enjoy discovering new ways to make software and systems more efficient, and thrive on helping an organization save time and resources. If you\u2019re a tinkerer who\u2019s always looking for ways to improve the things you build, find purpose in creating the supportive tools that help others do their job, and love playing with the latest tools and technologies, then data engineering might be the right career for you. <\/p>\n\n\n\n Data scientists are analytical thinkers who are curious, aren\u2019t afraid of asking questions, and live for putting their hypotheses to the test. Data scientists not only use data to make sense of things that have happened in the past, they also forecast trends and try to understand what might happen in the future. If you enjoy running advanced statistical analyses, writing machine learning algorithms, and using creativity to solve problems, then a career as a data scientist might be right for you.<\/p>\n\n\n\n Since you\u2019re here\u2026 Careers within the field of data science have in recent years seen soaring demand, with the Bureau of Labor Statistics forecasting a 22% increase in job growth from 2020-2030\u2014much higher than the average growth of other occupations. As companies continue to focus on generating, collecting, and analyzing big data to help them run their businesses, […]<\/p>\n","protected":false},"author":100,"featured_media":14103,"comment_status":"closed","ping_status":"closed","sticky":true,"template":"","format":"standard","meta":{"_acf_changed":false,"_eb_attr":"","_eb_data_table":"","footnotes":""},"categories":[67],"tags":[],"marketing_tags":[1466],"class_list":{"0":"post-6113","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-data-science"},"acf":[],"_links":{"self":[{"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/posts\/6113"}],"collection":[{"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/users\/100"}],"replies":[{"embeddable":true,"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/comments?post=6113"}],"version-history":[{"count":4,"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/posts\/6113\/revisions"}],"predecessor-version":[{"id":56407,"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/posts\/6113\/revisions\/56407"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/media\/14103"}],"wp:attachment":[{"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/media?parent=6113"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/categories?post=6113"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/tags?post=6113"},{"taxonomy":"marketing_tags","embeddable":true,"href":"https:\/\/www.springboard.com\/blog\/wp-json\/wp\/v2\/marketing_tags?post=6113"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}<\/a><\/a>What Does a Data Scientist Do?<\/h4>\n\n\n\n
They also engage with business leaders to understand their specific needs and present complex findings, both verbally and visually, in a manner that can be followed by a general business audience.<\/p>\n\n\n\nEducation and Requirements<\/h3>\n\n\n\n
<\/figure>\n\n\n\nWhat Are the Requirements To Become a Data Engineer?<\/h4>\n\n\n\n
What Are the Requirements To Become a Data Scientist?<\/h4>\n\n\n\n
Data Scientist vs. Data Engineer Salary<\/h3>\n\n\n\n
What Does a Data Engineer Earn?<\/h4>\n\n\n\n
What Does a Data Scientist Earn?<\/h4>\n\n\n\n
Career Paths<\/h3>\n\n\n\n
What\u2019s a Typical Career Path for a Data Engineer?<\/h4>\n\n\n\n
Many data engineers take advantage of roles such as data architect, solutions architect, and database developer to perfect their data engineering skills, develop a deeper knowledge of data processing and cloud computing, and gain experience with ETL and data layers. Some may also work in data analytics to bolster their knowledge of what data analysts and data scientists need before transitioning into data engineering.<\/p>\n\n\n\nWhat\u2019s a Typical Career Path for a Data Scientist?<\/h4>\n\n\n\n
Data analysts commonly pivot into data science roles<\/a> either by teaching themselves the relevant skills or by enrolling in an online data science course or bootcamp<\/a>.\u00a0<\/p>\n\n\n\nCan a Data Engineer Become a Data Scientist (or Vice Versa)?<\/h2>\n\n\n\n
<\/figure>\n\n\n\nData Scientist vs. Data Engineer: Which Is Best for You?<\/h2>\n\n\n\n
<\/figure>\n\n\n\nConsider Being a Data Engineer if\u2026<\/h3>\n\n\n\n
Consider Being a Data Scientist if\u2026<\/h3>\n\n\n\n
<\/strong>Curious about a career in data science? Experiment with our free data science learning path<\/a>, or join our Data Science Bootcamp<\/a>, where you\u2019ll get your tuition back if you don’t land a job after graduating. We\u2019re confident because our courses work \u2013 check out our student success stories<\/a> to get inspired.<\/p>\n","protected":false},"excerpt":{"rendered":"