데이터 과학은 대다수의 업계에서 중요하게 활용되며, 컴퓨터 공학 분야에서 가장 수요가 높은 직무에 속합니다. 데이터 과학자는 빅 데이터 시대에서 대규모 데이터 세트를 분석하여 귀중한 데이터 인사이트를 도출하는 탐정과도 같은 역할을 담당합니다. 탐정이 실마리를 찾고 해석하며 법정에서 사건에 대해 진술하는 것처럼, 데이터 과학 분야는 데이터 수명 주기의 전체에 걸쳐 있습니다.
데이터 수명 주기의 출발은 데이터 수집 기법을 활용하여 다량의 원시 데이터를 수집하는 것이며, 데이터를 효율적으로 '정제'하여 대규모 분석이 가능한 형태로 가공하는 데이터 파이프라인과 데이터 웨어하우스를 구축하고 유지 관리하는 것으로 이어집니다. 데이터 과학자는 이러한 데이터 인프라를 통해 데이터 마이닝과 데이터 모델링 기술을 활용하여 데이터 세트를 효율적으로 처리하고, 예측 분석과 질적 분석 등의 정교한 기법을 사용해 이러한 결과를 분석할 수 있습니다. 마지막으로, 데이터 시각화와 데이터 보고 기술을 이용해 이러한 결과를 보고함으로써 비즈니스 의사결정자를 지원해야 합니다.
데이터 과학자는 기업 규모에 따라 이러한 데이터 수명 주기의 전체를 담당할 수도 있고, 보다 큰 규모의 데이터 과학 팀에 소속되어 수명 주기의 특정 부분을 전문적으로 맡게 될 수도 있습니다.
Computer science is one of the most common subjects that online learners study, and data science is no exception. While some learners may wish to study data science through a traditional on-campus degree program or an intensive “bootcamp” class or school, the cost of these options can add up quickly once tuition as well as the cost of books and transportation and sometimes even lodging are included.
As an alternative, you can pursue your data science learning plan online, which can be a flexible and affordable option. There are a wide range of popular online courses in subjects ranging from foundations like Python programming to advanced deep learning and artificial intelligence applications. Students can choose to get certifications in individual courses or specializations or even pursue entire computer science and data science degree programs online.
Best of all, these online courses include lecture videos, live office hour sessions, and opportunities to collaborate with other learners from all around the world, giving you the chance to ask questions and build teamwork skills just like you would on campus.
In today’s era of “big data”, data science has critical applications across most industries. This gives students with data science backgrounds a wide range of career opportunities, from general to highly specific. Some companies may hire data scientists to work on the entire data life cycle, while larger organizations may employ an entire team of data scientists with more specialized positions such as data engineers to build data infrastructure or data analysts, business intelligence analysts, decision scientists to interpret and use this data.
Some tech companies may employ much more specialized data scientists. For example, companies building internet of things (IoT) devices using speech recognition need natural language processing engineers. Public health organizations may need disease mappers to build predictive epidemiological models to forecast the spread of infectious diseases. And firms developing artificial intelligence (AI) applications will likely rely on machine learning engineers.
Coursera offers Professional Certificates, MasterTrack certificates, Specializations, Guided Projects, and courses in data science from top universities like Johns Hopkins University, University of Pennsylvania and companies like IBM. Popular online courses for data science include introductions to data science, data science in R, Python, SQL, and other programming languages, basic data mining techniques, and the use of data science in machine learning applications.
More and more students are looking to pursue entire degree programs in data science online. There are several reasons for this, starting with cost: with Coursera's degree programs, you can get the same high quality education and the same diploma as your on-campus colleagues at a fraction of the cost. Flexibility is another big reason; particularly if you're already working full-time, the ability to pursue your data science education on your own time instead of having to take time off from your job is a huge advantage.
The popularity of data science courses on campus are also increasing the appeal of online courses. Many students who want to take these courses on campus find them overenrolled, or else so crowded that lectures are challenging to follow and access to faculty is lacking. Thanks to videos of classes, online students can watch lectures on their own time in a focused environment, and virtual office hours provide regular access to faculty. Online courses can thus make learning more accessible for aspiring data scientists.
Learning online doesn't mean sacrificing when it comes to the name on your diploma, either. Coursera currently offers data science degrees from top-ranked colleges like University of Illinois, Imperial College London, University of Michigan, University of Colorado Boulder, and National Research University Higher School of Economics.
People who are starting to learn data science should have a basic understanding of statistics and coding. There’s no prior experience necessary to begin, but learners should have strong computer skills and an interest in gathering, interpreting, and presenting data.
Analytical thinkers who enjoy coding and working with data are prime candidates for learning data science. Data scientists spend most of their time working on a computer, so it’s important for learners to be comfortable learning various coding languages. People interested in machine learning, deep learning, and AI are also well suited for learning data science. Data scientists need to have strong communication skills and be comfortable working against a deadline. Teams of data scientists often work on one project, so people best suited to learning data science need to work well with colleagues and have superior organizational skills.
The most common career path for someone in data science is a job as a junior or associate data scientist. After gaining some work experience, the next path for a data scientist is to earn a master’s degree or PhD and become a senior data scientist or machine learning engineer. From there, you may earn a doctorate and become a principal data scientist or a data scientist architect.
Learners interested in programming self-driving cars, speech recognition, and web searches should consider topics exploring machine learning and deep learning. Topics that explain coding languages including Python are perfect for people who want to focus on data engineering. Beginner AI is a great way to explore topics that integrate machine learning and data science. Learners who want to brush up on their math skills should consider topics that explain probable theory and functions and graphs.
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