Best Data Science Bootcamps NYC: Find Bootcamps & Certificates

Learn More About NYC Data Science Bootcamps

Data science is a multidisciplinary field that draws from data analysis, statistics, and informatics so that data can be used to analyze various phenomena. Data Scientists determine which questions need to be answered and then devise strategies to gather the data necessary for solutions. By using data science, organizations can identify patterns and trends, leading to better decision-making practices and risk mitigation. This interdisciplinary field incorporates algorithms, scientific methods, mathematics, and other processes to extract valuable information from both structured and unstructured data. This information is then applied to problem-solving across many domains. Data science is used in various industries, particularly those that rely on research, investment decisions, retail ventures, and big data analysis. This field is rapidly evolving and continues to be one of the most in-demand career paths for professionals in NYC and beyond.

What Can You Do with Data Science Training?

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. Data science is the backbone of fraud detection and helps provide customized recommendations, whether it’s regarding finances, entertainment, shopping, products, or services. Machine learning algorithms assist with risk analytics, stock trading, social media platforms, and creating patient care plans. You can visualize and analyze big data with confidence, making recommendations that drive business forward. Data science is all about making the most of an organization’s available data, whether that organization is in manufacturing, healthcare, or the public sector.

What Will I Learn in a Data Science Class?

Much of what you learn will be widely applicable to other areas, so you’ll have a choice in careers and industries. Skills like Python, machine learning, and data visualization are just a few of the skills you’ll learn in a data science class. Learning these skills will prepare you for the challenging and rewarding work that comes with a data science career.

Machine Learning

This is one of the most important branches of artificial intelligence. It involves Python, databases, and natural language processing (NLP). Data science uses machine learning algorithms to offer the personal recommendations users see in streaming apps like Netflix and retail sites like Amazon. It’s a vital part of computational linguistics, NLP, and design.

Coding

While there are multiple coding languages, Python, R, and SQL are some of the more commonly used programming languages in data science. These coding languages allow professionals in the field to collect, analyze, and interpret data. They’re also useful in mobile development and FinTech.

Data Visualization

Data visualization graphically depicts information or data so that it can be more easily interpreted. Data science uses it to identify fraudulent activity, routing transportation networks, and facilitate data-driven decision-making. There are multiple tools for visualizing data, and they are instrumental in translating complex data findings into a visual context, making it easier to spot patterns, trends, and correlations from large datasets.

Data Management

Data management systems sort complex data from numerous sources using artificial intelligence and machine learning, providing businesses with unparalleled insights. Anyone working in the field of data science will need data and database skills to navigate data successfully. Data Scientists need strong data management skills to process raw data with machine learning models.

How Hard is It to Learn Data Science?

Are you interested in learning data science but worried it might be too hard? Of course, the difficulty that comes with learning a new skill is somewhat subjective. The challenges of learning data science depend on factors like how you plan to use it and the field or sector in which you want to work. Data science can be challenging to learn in-depth, and expertise in the field takes years. Luckily, there are plenty of tools available to help make learning more manageable than you might think. Free supplemental resources, mentoring, articles, and videos can be leveraged to ease your data science training and give you the support you need to complete your training.

What Are the Most Challenging Parts of Learning Data Science?

Perhaps some of the more challenging parts of learning data science stem from its constant demand for innovation, industry-specific tools, and its need to combine hard and soft skills. There’s a lot of precision involved in this field, so it’s important to be attentive. Among the biggest challenges for Data Scientists are mining data, understanding what it all means, and communicating the results of their findings to key stakeholders. Indeed, while data science students may not encounter these challenges the same way as working professionals, it’s essential to understand that they are part of the process in real-world situations and prepare accordingly.

How Long Does It Take to Learn Data Science?

How long it takes to learn data science will depend on how much you want to know and your goals for learning. Some people opt for the longer route to data science by enrolling in a four-year college. This option gives you a broad range of skills and an immersive, in-depth education. Others enter data science through a bootcamp or certificate program. By taking this route, you can learn many of the fundamentals in six months or less. Accelerated, immersive training programs are designed to give beginners a leg up in the field and help you figure out where you want to fit in careerwise.

Because data science is such a broad field, the time you take to learn it truly depends on factors like your current skill set and how you intend to use the knowledge you gain. You can master data science fundamentals in less than a year, but your expertise will vary depending on the specific skills and role. Don’t expect to become an advanced data pro in a matter of months. Other factors that will play a role in your learning timeline include the learning format you choose, whether you decide to learn full-time or part-time, the level of difficulty, and prerequisites.

Should I Learn Data Science In-Person or Online?

Whether you choose to learn data science in-person or online is a choice you’ll have to make based on your personal preference, schedule, and learning needs. If you find that your schedule will not allow you to muddle through New York City traffic as you commute between your class and your previous engagement, an online Data Science Certificate class may be the better option for you. But if you’re not rattled by the commute and you thrive in a traditional classroom setting, an in-person AI & Data Science Certificate could be right for you. Other factors that can influence your decision include the need for a dedicated distraction-free learning space, appreciating peer-to-peer interactions, wanting to have a larger selection of classes to choose from, and being a social learner. Many factors will come into play as you decide which option you would prefer.

In-person classes are conducted like traditional classes. Students and instructors meet together at a location at the same time. Social cues are easier to pick up on, you receive hands-on instructor guidance, students can participate in group work, and you can receive personalized feedback. Live online classes are very similar, with the main difference being that students and instructors will only need to meet at the same time. Everyone does not have to be at the same location to conduct the class, and the class is typically recorded and accessible for review after the class session. These two learning formats share many of the same advantages. The drawbacks include fewer visual social cues and no literal hands-on instructor guidance. Students do receive hands-on guidance in live online courses, but it can look like screen sharing and remote computer access.

On-demand online courses are very different from the other two main learning formats. On-demand courses are not live, instead, they are pre-recorded classes that you can stop, start, and pause as you need to. Instructors and students do not need to meet at the same time or location. These courses are better suited to those who have erratic schedules and students who need greater flexibility with their education. The disadvantages to on-demand classes include not having access to real-time feedback, a lack of peer interaction, and less tech support. Some training centers will offer live Q-and-A sessions, live student discussion boards, and 24-hour tech support, however.

Can I Learn Data Science Free Online?

While it is possible to learn data science free online, it is not the optimal option. There’s no substitution for a knowledgeable instructor and real-time feedback. Your education is an investment that can greatly influence your earning potential. You’ll need to decide how much you want to learn before embarking on a free data science education. Everyone’s favorite free resource for learning new things from making compost to how to backpack through Peru, YouTube, has a host of videos on the topic. Some are from reputable and trustworthy sources while others are not. Beyond YouTube, many IT schools make their introductory live lessons available to the general public. This includes schools that specialize in live online instruction such as Noble Desktop, which offers a free seminar about Getting Started in Data Science that will introduce you to the core aspects of the field as well as give you a feel for what the school’s learning platform and instructors are like.

Free resources are a great supplemental resource to be used for studying, reinforcing, and gaining quick foundational knowledge. But if you want to get to know data science on a professional level and have a deep understanding, you’ll need more than free resources. There are several major issues with learning data science free online. Free resources have limitations like a superior lack of interaction. Without real-time feedback and the ability to ask questions and have them answered, it’s easy to find yourself feeling like a ship without a rudder. Finding a job when you’re self-taught is commendable, but it certainly isn’t easy. You will have to work hard to convince them and show them you’re the right candidate for the position. Another problem many people run into is the quality, accuracy, and currency of the content. Unreliable free resources abound on the free internet, and if you’re not careful, it’s easy to fall prey to inaccurate information.

What Should I Learn Alongside Data Science

If you’re interested in complementary skills to learn alongside data science, consider learning SQL and data visualization software like Tableau, Excel, or Power BI. Due to the intensity of some data science courses, these skills are better suited to learning simultaneously. SQL, although not a true coding language, is a language that helps data science professionals and other data handlers access the data in a relational database. It’s one of the easiest coding languages to learn, and most people can master it over a weekend. Data visualization software is extremely helpful when analyzing data and identifying patterns and trends. While these tools can be fairly simple to learn, every data science role will not require you to use them.

Is It Worth It to Learn Data Science?

If you’re looking to make the jump to a career that Forbes says will have a 31% increase in its workforce by 2030, then, yes, taking data science classes is probably worth the investment. More and more businesses are realizing what data science can do for them; as a result, more and more jobs are being created in the field. Don’t, however, get the impression that all you have to do is take a relatively short data science class, and you’ll be making a six-figure salary. Data science is an extremely competitive field, and even people with master’s degrees struggle to land a position. That’s not to say that there aren’t opportunities; but, whereas breaking into data science as recently as a few years ago wasn’t overly difficult, that no longer applies today. You’ll have to carefully evaluate the pros and cons to decide whether data science classes are worth it for you.

Industries That Use Data Science Skills

It may have taken several years for data science to catch on, but now most industries see the many advantages it offers and are finding ways to incorporate it. It won’t be long before every industry begins to get more out of their data as data science technology continues to improve, becoming more reliable and cost-effective.

Retail

In a city where Fashion Week is world renowned, it is no wonder that the NYC retail industry is a major industry making the most out of data science. Part of the magic of data science for retailers is being able to anticipate consumer wants and needs and supply those demands. Big data and data science are instrumental in getting the insights that give them a competitive advantage and keep customers coming back. Beyond mapping and analyzing consumer trends, data science is giving retailers the ability to personalize consumer shopping experiences like never before and offer more relevant shopping experiences. This leads to more purchase decisions and customer satisfaction. Data science is decoding the hidden truth behind, “If you build it, they will come.”

Finance

Perhaps one of the biggest things New York City is known for is finance. As a major epicenter of the finance industry, NYC businesses in this industry were perhaps among the first to leverage the power of data science to analyze huge amounts of data and help reduce losses. Since the early days of data science in the early 2000s, this industry has found more ways to let data science shine even more brightly. Data-driven decision-making is driving this industry forward. For instance, Citibank, headquartered in NYC, has developed a practical analytics system rooted in data science. They’re improving by using machine learning algorithms to target promotional spending, fraud detection, cybersecurity, web analytics, and other critical functionalities with data science. The finance industry is cutting its losses, pushing forward, and improving both customer and employee experiences as a result of incorporating data science.

Media and Entertainment

Hollywood has the West Coast and New York City covers much of the East Coast’s media and entertainment industry. Thanks to the evolution of the internet, technology, and social media, consumers now expect to have access to rich media at all times on any device in a variety of formats. Data science is playing a major role behind the scenes of media and entertainment by measuring content performance and personalizing recommendations. Whether it's news, music, television, or movies, data science techniques compile, organize, and analyze big data in real-time, allowing streaming services and social media to better curate content for consumers.

Data Science Job Titles and Salaries

You’ll find many roles within the field of data science that are both challenging and fulfilling. Data science jobs are found in almost every industry that collects, generates, or manages data. Here are a few data science jobs worth considering after completing data science training.

Data Scientist

Data scientists collect, organize, and analyze large sets of data, providing analysis that is key to decision-making. Governments, non-profits, and businesses of all types rely on data for forecasting, risk management, and resource allocation. Data scientists discover and analyze trends in data, and report their findings to stakeholders. They will use algorithms and models to simplify and mine datasets to create data-driven recommendations. Data scientists are needed across a handful of industries, especially the ubiquity of data and the reliance on it for business decision-making. Data Scientists should have strong analytical and communication skills. They must be proficient in SQL, Python, R, file management, machine learning and algorithms, natural language processing, linear algebra, calculus, statistics, and data modeling and visualization tools like Tableau. Data Scientists will need to be team players, good at pattern recognition, detail-oriented, and passionate problem-solvers. A Data Scientist in New York City can earn a salary ranging between $77,000 and $150,000.

Database Administrator

A Database Administrator uses software to securely store and organize data and make sure that the data is available to authorized users. Some Database Administrators specialize in Systems or Applications while others work on all aspects of database administration. They work in a variety of industries, including computer systems design, IT, insurance, finance, and healthcare. They work with Data Analysts, management, and developers. Some of the typical duties can include establishing performance baselines, analyzing database performance, coordinating data pushes or changes, working with developers on their code, working with management to determine their database needs, maintaining databases, troubleshooting issues, and planning security measures for databases. Knowing one or more of the major database systems, like MySQL, MongoDB, Hadoop, or IBM is necessary. A New York City Database Administrator typically earns a salary between $64,000 and $100,000.

Business Analyst

Business Analysts use business, technology, and project management skills to analyze business problems and propose data-driven solutions. Grounded in technical expertise, Business Analysts perform risk analyses, manage project plans, and translate technical information like diagrams and blueprints. An experienced Business Analyst can segue into business or project management, putting their professional expertise to work across a variety of industries, companies, and job functions. You’ll find them investigating a business’s goals, issues, and potential solutions; analyzing information; communicating with point people in the business; documenting their findings; presenting their findings; evaluating solutions, implementing solutions; and liaising between teams.

What is the Difference Between Data Science and Data Analytics?

Data science and data analytics are related topics, but they are not the same thing. The main difference between the two disciplines lies in how they treat the data. In data analytics, Data Analysts visualize the data and interpret the visualizations to solve complex problems. Their goal is to help facilitate data-driven decision-making. In data science, a Data Scientist organizes data and develops machine learning algorithms to build predictive models. Data science focuses on building, cleaning, and organizing datasets, while data analytics is more concerned with understanding the data to pull actionable insights from it. The role of a Data Scientist is to prepare data for the analyses.

Data Science Classes Near Me

Are you interested in enrolling in a data science class in NYC? You’ll find many options in and near the city that can give you the training you need to make your way into a career in data science. Check out this AI & Data Science Certificate from Noble Desktop. This two-month certificate program will help you become a Data Scientist or AI Specialist. During the beginner-friendly program, you’ll learn SQL, Python, and AI integration as well as associated skills that will be useful in working in data science. Not only will you learn basic to advanced Python programming, but also machine learning, automation, and data visualization with Python. You’ll be learning practical skills that are in-demand through engaging projects and hands-on exercises. You can take this course in-person or live online. Class sessions will be recorded and made available to you, and you’ll have eight 1-on-1 mentoring sessions outside of class to reinforce what you’re learning, get help with difficult concepts, receive professional advice on your job search, and polish your resume and portfolio.

Noble Desktop’s Data Science Certificate is designed for those who are interested in becoming entry-level Data Scientists or Python Engineers. This 84-hour certificate course covers the core programming skills required to manipulate databases and execute different levels of data analysis. After completing this course, you will have a strong foundation in analyzing data using NumPy, Pandas, and Matplotlib, as well as preparing and cleaning data for Python analysis. Students will have four 1-on-1 mentoring sessions outside of class available to them where they can receive assistance with their resume, portfolio, or LinkedIn profile, work on challenging concepts, and get advice on their job search. You can also retake the class for free within one year of attending the class.

Python Data Science & Machine Learning Bootcamp is designed to help you master Python programming and apply it to machine learning, data analysis, and automation. You’ll learn how to build predictive models and make the most out of data visualization in addition to Matplotlib, Plotly, NumPy, and Pandas. Students who complete this course will be poised to start a career in data science or Python engineering. There are multiple schedules available for this 96-hour bootcamp to accommodate a wide range of schedules. Students will have four 1-on-1 mentoring sessions outside of class available to them where they can receive assistance with their resume, portfolio, or LinkedIn profile, work on challenging concepts, and get advice on their job search, as well as the option to retake the course for free within one year.

When you enroll in this FinTech Bootcamp you’ll gain the necessary skills to begin a career as a Financial Analyst, Data Scientist, or Data Analyst. This course covers Python programming, SQL, data science, financial analysis, data visualization, and machine learning. Students can choose between Python for Finance or Financial Modeling as an elective. Although this is a beginner-friendly class, students should have an understanding of financial concepts such as NPV, IRR, financial statements, and stock fundamentals. Those without a background in finance should contact Noble Desktop after registration to access a free on-demand supplemental guide. This course also includes six 1-on-1 mentoring sessions where you can work on challenging concepts and receive assistance with your resume and job search. You can even retake the course for free within one year of attending the bootcamp.

Data Science Corporate Training

Are you looking to offer on-site data science corporate training to your team? Noble Desktop provides both in-person and online corporate training and mentoring to organizations of all sizes. Providing professional development opportunities to employees is an investment in both your employees and your organization. Having more talented and knowledgeable employees enables you to attract more talented employees while improving your employee retention rate and the workplace culture. If your organization has employees who could benefit from learning data science skills like statistics, Python, or SQL, consider Noble Desktop’s onsite corporate training opportunities.

Corporate data science training is offered virtually, onsite at your specified location, or as a publicly listed course. There are discount bulk vouchers available to those who prefer their employees to enroll in one of the open-enrollment public data science training classes. When you choose onsite corporate training, you’ll get an experienced instructor and the ability to customize the curriculum to meet the needs of your organization. Contact Noble Desktop for a free consultation to learn more about your options to invest in your employees with data science training classes.

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Key Insights

Learn More About NYC Data Science Bootcamps

What Can You Do with Data Science Training?

What Will I Learn in a Data Science Class?

Machine Learning

Coding

Data Visualization

Data Management

How Hard is It to Learn Data Science?

What Are the Most Challenging Parts of Learning Data Science?

How Long Does It Take to Learn Data Science?

Should I Learn Data Science In-Person or Online?

Can I Learn Data Science Free Online?

What Should I Learn Alongside Data Science

Is It Worth It to Learn Data Science?

Industries That Use Data Science Skills

Retail

Finance

Media and Entertainment

Data Science Job Titles and Salaries

Data Scientist

Database Administrator

Business Analyst

What is the Difference Between Data Science and Data Analytics?

Data Science Classes Near Me

Data Science Corporate Training

NYC Data Science Corporate & Onsite Training Noble Desktop

Data Science Training Locations in or Near NYC

NYC Careers & Salaries Related to Data Science

Related Bootcamps

NYC Data Science
Corporate & Onsite Training