数据科学

数据科学在大多数行业中都有重要的应用，并且是计算机科学中需求最大的职业之一。数据科学家是大数据时代的侦探，负责通过分析海量数据集来发掘有价值的数据见解。就像侦探负责寻找线索、解释线索并最终在法庭上争辩案件一样，数据科学领域涵盖了整个数据生命周期。

整个周期从使用数据收集技术捕获大量原始数据开始，然后是建立和维护数据管道及数据仓库，以高效地'清理'数据并使之可用于大规模分析。此数据基础设施使数据科学家能够运用数据挖掘和数据建模技能高效地处理数据集，并使用预测分析和定性分析等先进技术来分析这些输出。最后，必须运用数据可视化和数据报告技能来呈现这些发现，以帮助业务决策者。

根据公司的规模，数据科学家可能负责处理整个数据生命周期，或者可能作为大型数据科学团队的一部分专门处理生命周期的特定部分。‎

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.‎