Data science is a multidisciplinary field that involves examining data to locate meaningful insights that a business or organization can use this information to make decisions. The data science process involves determining which questions a business or organization should ask and answering them using data. To do so, Data Scientists draw from a background in fields like statistics, AI, math, machine learning, Python, SQL, R, and computer engineering when analyzing vast amounts of data. Data Scientists look for trends and patterns in datasets that contain useful insights. They create data models and algorithms designed to forecast different outcomes. These professionals seek to use data to answer specific questions, such as what happened and why, what is expected to happen in the future, and what actions can be taken based on analysis results. Because of how much data most organizations gather and store, those with a background in data science can transform these numbers into useful insights for their organization. 

If you’re interested in studying this popular field, a great way to do so is to enroll in a data science bootcamp. Bootcamp study provides hands-on training in a small class environment. Participants can complete coursework online or in person. This popular training format not only offers instruction in a range of useful data science skills and tools, but it also helps participants develop professionally. The job outlook for those employed in data science is expected to grow 36% by 2031, which is significantly above the national average. The skills learned through data science can help those enrolled perform better at their current job or explore new career options. In addition to classroom lessons, these programs often include career support, which can open new professional doors in data science. These short programs provide a focused, pragmatic way for learners at all levels to study data science.

What is Data Science?

Data science is a broad field that encompasses the disciplines of mathematics, computer programming, and artificial intelligence (AI). Data professionals such as Data Scientists and Data Analysts use advanced techniques like machine learning algorithms to find patterns in a vast amount of information. This process can provide actionable insights to stakeholders, from Product Managers to C-suite executives.

Today nearly every sector requires data science expertise, whether public or private. Among the top sectors where data science is critical are health and wellness, retail, web and application development, banking and finance, and governmental agencies. The field continues to project dramatic growth over the next decade; Glassdoor even listed Data Scientist as number three in its 50 Best Jobs in America in 2022.

Read more about what Data Science is and why you should learn it. 

What Can You Do With Data Science?

Data science has so many applications in different industries that a comprehensive review could fill a book. Professionals as diverse as Business Analysts, Machine Learning Engineers, and Enterprise Architects use data science in their day-to-day activities.

Top sectors for data science include banking and finance, marketing and advertising, and healthcare:

• Banking, Financial Services & Insurance (BFSI) - Business Analysts and Data Scientists use data for everything from fraud detection to customized financial advice. Machine learning algorithms can assist with risk analytics, stock trading, and other tasks.

• Marketing & Advertising - Data Analysts and Marketing Analysts use data science in advertising to create targeted ad copy, recommend products and services, and leverage social media platforms. Programming languages like Python and R, often key to data science positions, help experts analyze data and make recommendations.

• In healthcare, Data Scientists create algorithms to create care plans and improve patient services. Using data analysis in medical imaging can help care providers with diagnoses and treatment decisions.

Data science has proven crucial to many other sectors, from retail and manufacturing to the public sector. If you want to combine challenging work with job security, start with data science.

Why Learn Data Science in a Bootcamp or Class?

One of the most effective and engaging ways to learn data science is through bootcamp study. Participants that opt for this type of training learn data science through accelerated, hands-on training. In the small class environment, participants are taught by an instructor with real-world data science experience. Learners can choose to study in person at a designated training facility or to complete coursework remotely using a live teleconferencing platform like Zoom or Teams. 

Bootcamps in data science are comprehensive programs that cover a range of related skills and tools. Although course content and length can vary depending on the provider, most programs provide instruction in a combination of data analytics, machine learning, SQL, Python, R, predictive analytics, data visualization, statistics, and natural language processing. Because data science draws from so many skills, bootcamps in this field are typically longer and more intensive than those focusing on just one tool or data technique. Bootcamp study can take several months to complete. Participants can usually opt to complete their studies at a full-time or part-time pace, depending on their availability. Cost ranges from $10,000-$20,000 depending on course length and training format. 

Bootcamps in data science aren’t only designed to teach students practical skills, they’re intended to prepare them to use these skills professionally. These programs teach more than different programming languages and analytics tools; they often include career support services as well. Some bootcamps include incentives like one-on-one mentoring, the option of a free course retake for up to a year, or professional portfolio or resume help. This combination of intensive skills training and professional development is intended to help participants become well-rounded data science professionals who can explore exciting career options in data science or data analytics.

Bootcamps Compared to College

Once you’ve committed to studying data science, you may wonder whether it’s more effective to study this set of skills through college coursework or in a bootcamp. Both learning options have their own unique benefits. Ultimately, the path you choose will depend on how much you want to spend on studying data science, as well as how much time you wish to invest in acquiring this skill set.

College or university study is the longest learning format for studying data science. Those who enroll in this kind of training commit at least four years to study. During this time, most students complete a range of courses since colleges and universities typically require that those enrolled complete general education coursework to graduate. These classes are in other fields, such as history, English, or philosophy. This means that college students interested in learning data science will also have to complete classes in fields that aren’t related to this discipline. Universities that offer a data science major require that participants complete classes in statistics, business, computer science, math, and social sciences. However, not all colleges offer a major in data science. This means learners interested in studying this topic may have to select a related field, such as computer science or statistics.

Bootcamp study is a much shorter and more focused way to acquire data science training. Over the course of several months, participants receive training entirely in data science. There is no need to complete unrelated coursework to graduate. This means that learners can devote all their time and focus to acquiring data science training rather than being distracted by other coursework they may not need to climb professionally. Many bootcamps also offer professional development such as career counseling or one-on-one mentoring, which ensures that those enrolled can apply their data science training professionally and even pursue new career options with their knowledge.

Cost is another important consideration when deciding between college or bootcamp study. Attending college is more expensive than ever. The current average cost per year of university study in the US is $35,000 for public schools and $55,000 for private schools. This means that it could cost between $140,000 and $220,000 to study data science at a four-year college or university. While college study provides training in a range of skills, this cost is prohibitive for some learners. Bootcamp study is a much less expensive way to learn data science. Many educational providers offer bootcamps for $10,000-$20,000. Additionally, bootcamp graduates can immediately apply their training in a professional setting instead of having to wait four or more years to graduate.

Bootcamps Compared to Self-Paced Courses

Self-paced coursework is another training format some learners opt for when studying data science. Unlike bootcamp study, which takes place in the live environment, self-paced coursework is recorded at an earlier time and uploaded. Participants complete lessons independently from any space with a reliable internet connection. This flexible learning format is a good option for those whose schedules don’t allow them to commit to coursework that takes place at specific times. Those who study data science through asynchronous material can determine when it is best to complete lessons and how much time per day or week they wish to devote to data science study. Additionally, self-paced coursework provides students with the ability to pause or rewind video tutorials as often as necessary. Learners can even rewatch entire lessons to reinforce challenging data science skills. 

Self-paced data science content is often a more affordable learning option than bootcamp study. Bootcamps usually cost between $10,000 and $20,000, depending on the provider. Asynchronous material is available for less than $200. Some coursework requires a platform subscription, whereas other content can be purchased as a stand-alone course. Some educators even offer free data science content. Asynchronous training material is usually shorter than bootcamp study. Some online videos require an hour or less to finish, and other self-paced data science coursework may take ten or more hours to complete. Because so much self-paced data science training material is available online, it’s important to research the provider to ensure the material they post is current. 

Self-motivated learners who are interested in getting started with data science may find self-paced material a good way to learn basic concepts. However, because data science is a multidisciplinary field, it requires studying a range of skills and topics, from computer programming languages to machine learning. Acquiring this entire range of skills through self-paced materials may be challenging for some learners. In bootcamps, participants learn data science through live, instructor-guided coursework. They can ask questions in real-time and receive immediate clarification. Even in the live online format, participants can share their screen with the instructor (with permission) for additional support. Since no instructor or cohort is present in self-paced study, those who wish to learn data science in this format have to be motivated to find answers to questions on their own. This is why asynchronous content can be a good starting place for those interested in learning basic data science concepts. To acquire more advanced training or learn data science for professional reasons, though, live data science training in a bootcamp may ultimately be a more effective learning format.

Bootcamps Compared to Free Training Options

Free data science training material is another way some learners opt to study this set of skills. Unlike bootcamps, which are taught in the live environment, free training material is pre-recorded and posted online. This means that students can complete it at their own pace, on their own time, and do so from any location. Bootcamp study requires committing to courses that occur at regularly scheduled times, which may be difficult for those who have full work schedules or travel. 

Different kinds of data science training material are available online. Learners can choose from short YouTube videos that cover one skill, such as writing an SQL query or automating web scraping. These videos are relatively short and may require only a few minutes to watch. Other content is longer and more involved and may provide an overview of the field of data science, as well as instruction on basic data science skills and concepts. Some educational providers post free courses that take an hour or longer to complete and include online tutorials and quizzes. 

Because there is no cost to study with free online data science material, it’s a low-stakes way to get started in this field. Those who start learning data science through free material and find that it’s not a good match for their learning needs can stop studying at any point without accruing any financial consequences. Bootcamp study, on the other hand, requires at least a partial payment upfront. Those who wish to stop bootcamp study will not receive a refund and may have already invested thousands of dollars into study.

Before studying with free training materials, it’s always a good idea to research the educator to ensure the posted lessons reflect the current best practices in data science. Since free data science material is asynchronous, learners don’t have the benefit of a live instructor to help them master complex concepts or tools. This is why free content is a good place to start your data science learning journey. However, if you want to truly master the range of skills, programming languages, and tools needed to pursue a career as a Data Scientist, in-person or live online training is a more effective way to study.

Learn Data Science Skills with Noble Desktop

Because data science is a broad field, targeted training can prepare you for a data-centered position or even help you choose a specific role. You might think you’ll need a four-year data science degree, but this isn’t necessarily so. The bootcamp or certificate educational model has become increasingly popular for data professionals, thanks to features like small class sizes, hands-on training from industry experts, and individual mentoring. Noble Desktop offers a wide range of data science programs to help get you started.

• Data Science Certificate - The comprehensive Data Science Certificate provides all the skills required for entry-level data science, data analytics, or software engineering roles. Students learn how to write complex queries and build machine learning models while preparing a portfolio on a real-world basis. Skills covered include Python, SQL, NumPy, Pandas, and Jupyter Notebook, to name a few.

• Data Analytics Certificate - The comprehensive Data Analytics Certificate program offers the perfect training ground for Data Analysts, Business Intelligence Analysts, and Marketing Analysts. With a heavy emphasis on Tableau data visualization software, you’ll learn skills like Python programming, SQL, and machine learning, among others. Registrants of the Data Analytics Certificate or Data Science Certificate can also attend Noble’s Power BI Bootcamp at no additional charge.

• Python for Data Science Bootcamp - The Python for Data Science Bootcamp covers everything from programming fundamentals to data visualization. Students can save by taking this course as part of Noble’s Data Science Certificate, Data Analytics Certificate, or FinTech Bootcamp.

Check out all the Noble data science classes and bootcamps for additional options, like the Python Data Science & Machine Learning Bootcamp, Python for Data Science Bootcamp, or Python Machine Learning Bootcamp.

Key Takeaways

• Data science is an in-demand skill in the US. In addition to high-paying career options, this profession has an above-average job outlook through 2031. Those with data science training have a range of professional opportunities.
• Those interested in studying data science can choose between in-person and live online bootcamps. Both options provide real-time access to an instructor. They can also learn this skill in college, though this is a much more expensive and time-intensive format for study.
• Asynchronous data science training material is also available for those interested in deciding when and where they study, as well as the pace at which they wish to learn. 
• If you’re ready to learn data science, let Noble Desktop help. Noble offers comprehensive data science training in person and live online.