Data Science Classes & Bootcamps Near Me

Today, there are more ways than ever for companies to collect data on their operations. From user behavior on social media to complex trends in the stock market, quantitative data offers businesses in virtually every industry the power to better understand and streamline their operations. However, actually harnessing this data in a useful manner is another matter entirely. In addition to the sheer amount of data often at the fingertips of many companies, it takes a trained eye to filter out the static and draw definitive conclusions from immense datasets. Here, data scientists have made a massive impact in today’s digital economy, providing incredible value for companies worldwide.

Data scientists rely on a unique intersection of skills to get their job done. In addition to harnessing industry-specific information and statistical expertise, data scientists also foster coding knowledge to build tools that automate data analysis and make tasks such as machine learning possible. As a result, starting a data science career allows individuals to specialize in a diverse skill set while introducing them to a highly in-demand career field.

Why Learn Data Science?

Learning data science is an excellent way to open the door to many new career opportunities. Collecting, manipulating, and analyzing data is a vital part of remaining competitive in the business world, and data science is becoming increasingly common in several other fields and industries. Almost every professional working environment will benefit from employing specialists who know how to collect and manage data. 

In addition, data science is at the bleeding edge of new technological developments in fields like artificial intelligence and machine learning. These newly developing technologies have the potential to revolutionize the way businesses operate, and consumers interact with the world around them. Data science skills are at the center of “teaching” machines to “learn” how to operate independently of a human operator.

Data Science Tools

Learning data science is equal parts learning theories of data collection and manipulation and learning how to use important tools for the collection and analysis of data. Most data scientists will work with massive amounts of data stored in databases built with programming languages like Structured Query Language (SQL) or Python. Thus, almost all data science training programs will teach computer science, programming, and coding since scientists will need to write their own programs and database queries to make sense of the data they are working with. Thankfully, the primary language of data science, Python, is arguably the best first programming language for a new coder to learn, so students coming into the field as novice programmers will have an easier time mastering the basics.

Data scientists will also use data tracking and organization programs, most commonly Microsoft Excel, to help store and organize data. Keeping accurate, organized records is essential for data science careers. Most students will need to become familiar with the more advanced features of spreadsheet programs like Excel.

Finally, data is only really useful once it can be translated into a form non-experts can understand. This means that most data scientists will need to learn how to use data visualization tools like Tableau or Microsoft Power BI to turn their datasets into understandable visual representations that invested stakeholders can understand and use to make informed decisions about a project. This communicative aspect of the process is essential since it is the step that makes data a usable commodity businesses can use to create actionable plans and strategies.

Data Science Vs. Data Analytics

One major aspect that distinguishes data science from traditional data analytics is the ability to conduct predictive and prescriptive analysis. There are four types of analytics: descriptive, diagnostic, predictive, and prescriptive. Predictive analytics detects problems, anomalies, or most likely outcomes before an event occurs. This is made possible by training statistical models on historical data. Fortunately, we now have the computing and storage capabilities to compare real-time data against models trained on historical data. Predictive analytics is useful, but it relies on descriptive and diagnostic analytics to build a foundation. Descriptive analytics identifies the current state of data, while diagnostic analytics determines why it occurred.

Three types of analytics must work together to provide feedback to statistical models. This will generate better predictions. Once all three types of analytics are running smoothly, you can focus on prescriptive analytics. Prescriptive analytics recommends specific actions organizations can take to solve or mitigate problems foreseen during predictive analytics. The capability to take advantage of historical data, see the future, and change the present to avoid potential problems is the crème de la crème of data analytics.

The future of data science looks very promising, and it is an exciting career field where you can be on the bleeding edge of scientific breakthroughs. Many companies are currently transitioning from on-premise data servers to the cloud. This is indicative of companies storing and collecting more data and the increasing demand for data scientists who can interpret and take advantage of it. Data science is a growing field with high salaries and roles across all commercial and industrial sectors.

Data Science Careers

A career in data science will give you a highly sought skill set and help you make sense of living in a world where data is becoming increasingly ubiquitous. As a career field, data science is interdisciplinary, combining three disciplines: programming, mathematics, and business. Programming elements include pattern recognition and learning, signal processing, data mining, machine learning, and modeling. Mathematics focuses on probability, statistics, linear algebra, and calculus, which allows you to determine whether a test is statistically significant, design A/B tests, and conduct predictive analytics. Last but not least, business acumen is an often overlooked skill, but it enables data scientists to communicate with business stakeholders and translate findings into actionable insights through storytelling and data visualization. Data scientists must have these three skills in interdisciplinary areas to work effectively. Its hybrid nature is one of the main reasons why the Harvard Business Review announced data science as the “sexiest job of the 21st century.”

Even though the “data science” terminology is relatively new, the origin of the profession could be traced back to 1962 when a mathematician named John Tukey predicted the rise of data analysis. As personal computers and memory technology improved, collecting, storing, and processing data became easier and the gap among the interdisciplinary fields mentioned above inched closer. Currently, the fields overlap and resemble a three-Venn diagram with data science in the middle.

The demand for data scientists will be increasing because as more organizations and businesses collect data, they need people who can make sense of it all. Data by itself won’t help solve questions. It takes data scientists who can clean the data, formulate hypotheses, and create models to make data useful. As a data science expert, you will be in-demand for diverse roles such as medical informatics, artificial intelligence, social sciences, and much more. Data scientists are employed in industries as far-reaching as the entertainment and promotional industry, healthcare, pro sports, and political lobbying and advocacy. As data science technologies improve, innovators in virtually every field are realizing that they can benefit from employing professionals who understand how to turn raw data into actionable insights.

Given their importance in deciphering the immense amounts of data captured every single day, data scientists are sure to find their skills in high demand across the country. This is especially true since data has revolutionized businesses in virtually every industry, meaning that data scientists can specialize in an industry that interests them and find meaningful career options.

Data science is particularly important in the financial sector, as Financial Analysts and Investment Analysts use complicated data science principles to predict market movements and make informed decisions about how companies and individuals should invest their money. No one should feel safe investing in a stock option based on a person’s gut feeling, and data science aims to take as much of the guesswork out of investing as possible. 

While the specific availability of data science jobs may vary by region, most major cities host fulfilling and well-paying data science opportunities. According to recent salary figures from Indeed and Glassdoor, data science professionals can expect to make anywhere from $115,000 to $145,000 per year in careers like Data Scientist, Data Engineer, or Machine Learning Engineer. In addition, many data scientists will have the skill foundations to transition to careers such as database administration, which pays similarly well in many cases.

What Will You Learn in a ||CPN412||? 

A data science course equips students with the knowledge and skills necessary to extract valuable data, make data-driven decisions, and solve complex problems across various industries. Here’s some of what you can expect to learn. 

• Foundations of data science. You’ll start with the basics, understanding what data science is, its significance in today’s world, and its applications. This section covers key concepts, terminologies, and the data science process. 
• Data collection and cleaning. One of the crucial steps in data science is gathering and preparing data for analysis. You’ll learn how to collect data from various sources, clean and preprocess it to ensure it’s accurate, complete, and suitable for analysis. 
• Data exploration and visualization. Data exploration involves understanding the structure of your data, identifying patterns, and spotting outliers. You’ll also learn data visualization techniques to present your findings effectively using tools like Python libraries (e.g., Matplotlib, Seaborn) or R. 
• Statistical analysis. Statistical methods are fundamental to data science. You’ll delve into descriptive and inferential statistics to draw insights from data and make informed decisions. Topics may include hypothesis testing, probability, and regression analysis. 
• Machine learning. This is a core component of data science. You’ll explore various machine learning algorithms for tasks like classification, regression, clustering, and recommendation systems. Popular libraries such as scikit-learn and TensorFlow are often used for hands-on practice. 
• Big data and distributed computing. In today’s data-driven landscape, handling large datasets is crucial. You’ll be introduced to tools like Hadoop and Spark for distributed data processing and storage. 
• Feature engineering. Learn how to select and engineer features from your data to improve model performance. Feature selection, transformation, and scaling techniques will be covered. 
• Model evaluation and selection. Discover how to evaluate the performance of machine learning models using metrics like accuracy, precision, recall, F1-score, and ROC curves. You’ll also learn strategies for model selection and tuning. 
• Deep learning. Dive into deep neural networks and their applications in computer vision, natural language processing (NLP), and other domains. Frameworks like TensorFlow and PyTorch are commonly used. 
• Time series analysis. Understand how to analyze data with a temporal component such as stock prices or weather data. You’ll learn techniques for forecasting and anomaly detection. 
• Natural language processing (NLP). Explore the world of text data by learning techniques to process, analyze, and derive insights from textual information. Topics include sentiment analysis, text classification, and language modeling. 
• Data ethics and privacy. In an era of increasing data privacy concerns, you’ll delve into ethical considerations related to data collection, usage, and sharing. Compliance with regulations like GDPR may also be covered. 
• Data visualization tools. Beyond basic data visualization, you’ll learn to use specialized tools like Tableau or Power BI to create interactive and insightful dashboards. 
• Communication skills. Data scientists need to effectively communicate their findings to non-technical stakeholders. You’ll learn how to create clear and compelling data-driven narratives and reports. 
• Career development. Some courses offer guidance on job strategies, resume building, and interview preparation to help you kickstart your data science career. 

How Hard is It to Learn Data Science?

Learning data science can be both challenging and rewarding, and the level of difficulty can vary widely depending on your background, goals, and approach to learning. Here’s a comprehensive look at the factors that influence the difficulty of learning data science. 

• Prior knowledge and skills. Your existing knowledge of mathematics, statistics, programming, and related fields can greatly affect the difficulty of learning data science. If you already have a strong foundation in these areas, you may find certain aspects of data science more accessible. 
• Mathematics and statistics. Data science relies heavily on mathematical concepts, including linear algebra, calculus, and probability theory. Proficiency in these areas is crucial for understanding and implementing data science algorithms and models. If you have a strong math background, you’ll have a head start. However, even if your math skills are rusty, you can improve them over time with practice and dedicated learning. 
• Programming skills. Data science is primarily done using programming languages like Python and R. If you have prior programming experience, especially in these languages, you’ll find it easier to get started. Beginners may encounter a steeper learning curve, but both Python and R are considered beginner-friendly languages for data science. 
• Conceptual understanding. Data science involves complex concepts like machine learning, deep learning, and statistical modeling. Developing a deep conceptual understanding of these topics can be challenging, but it’s crucial for applying data science effectively. 
• Tools and libraries. Familiarity with data science libraries and tools such as scikit-learn, TensorFlow, and pandas, can streamline your workflow and make the learning process smoother. However, learning to use these tools is part of the journey, and there are abundant resources available to help. 
• Domain knowledge. Depending on your specific data science projects, having domain-specific knowledge in areas like finance, healthcare, or marketing can be a significant advantage. Acquiring this knowledge can add complexity to your learning path. 
• Learning resources. The availability and quality of learning resources can greatly impact the difficulty of learning data science. High-quality online courses, textbooks, tutorials, and forums can provide structured guidance and support. 
• Practice and projects. Data science is a hands-on field, and practical experience is crucial. Building and working on real-world projects can be challenging but incredibly valuable for gaining proficiency. 
• Time commitment. Learning data science takes time. The more time you can dedicate to studying, practicing, and working on projects, the faster you’ll progress. 
• Mentorship and guidance. Having access to mentors or instructors who can provide feedback and answer your questions can significantly reduce the difficulty of learning data science. 
• Adaptability. The field of data science is constantly evolving. Staying current with new techniques, tools, and technologies is essential, which may require ongoing learning and adaptation. 
• Problem-solving skills. Data science often involves solving complex, real-world problems. Developing strong problem-solving skills and a systematic approach to tackling challenges can make learning data science more manageable. 

Is It Worth It to Attend a ||CPN412||? 

Attending a data science course can be an invaluable investment in your professional development. These courses offer structured, expert-led learning which can significantly accelerate your journey into the dynamic world of data science. One of the most compelling reasons to opt for a data science course is the structured learning environment it provides. Data science is a multifaceted field, encompassing statistics, programming, machine learning, and data analysis. Navigating this complex landscape can be challenging on your own. A structured course lays out a clear path, guiding you through essential concepts and gradually building your skills. This structured approach ensures that you gain a comprehensive understanding of data science—from the fundamentals and progressing to more advanced topics. 

Expert instruction is another compelling reason to consider a data science course. Quality courses are often led by experienced instructors who have a deep understanding of the subject matter. These instructors bring practical insights, real-world examples, and a wealth of knowledge to the classroom. Their expertise can help you grasp complex concepts, navigate challenges, and stay up-to-date with industry trends. They provide a valuable resource for asking questions, seeking clarification, and receiving guidance throughout your learning journey. 

Hands-on experience is a cornerstone of data science education. Data science courses typically incorporate practical exercises and projects that allow you to apply what you’ve learned in real-world scenarios. This hands-on experience is invaluable for building skills and confidence. It enables you to work with actual datasets, apply data analysis techniques, and develop solutions to real problems. By actively engaging with data and projects, you not only reinforce your learning but also gain practical skills that are immediately applicable in your career. 

Access to a wide range of resources is another advantage of enrolling in a data science course. Many courses provide students with essential resources such as textbooks, software licenses, datasets, and access to online forums and communities. These resources enhance your learning experience and provide ongoing support. Textbooks and software enable you to delve deeper into specific topics and practice using industry-standard tools. Datasets allow you to explore real data and develop your data analysis skills. Online forums and communities provide opportunities to connect with fellow learners, ask questions, and seek help when you encounter challenges. 

Networking opportunities abound in data science courses. These courses attract individuals from diverse backgrounds and industries, all with a common interest in data science. Interacting with fellow students, instructors, and guest speakers can lead to valuable connections and collaborations. Networking can open doors to job opportunities, provide insights into industry trends, and foster a sense of community among data science enthusiasts. 

Many data science courses offer certification upon completion. These certificates can serve as a valuable credential when seeking job opportunities or advancing in your current career. They demonstrate your commitment to learning and your proficiency in data science concepts and techniques. Certificates may help your résumé stand out and give you a competitive edge in a job market where data science skills are in high demand. 

However, the decision to attend a data science course should take several key factors into account. First, reflect on your career goals and aspirations. Consider how data science fits into your desired career path. If data science is a significant component of your professional journey, a course can provide the necessary foundation. Second, assess your learning style and preferences. Data science courses offer structured and guided learning, which can be beneficial for some learners. However, if you prefer a self-paced or project-based approach, you may find online resources and tutorials equally effective.

Your Guide to In-Person & Virtual Data Science Training

In-Person Data Science Classes

With educational journeys for data science ranging from college degrees to in-person classes and virtual courses, it can be incredibly difficult for a beginner or continuing learner to decide on the best option for them. To help learners find the best opportunity for their learning style, Noble Desktop offers a tool that allows users to search and compare data science classes available in-person in their area, as well as live online.

In-person classes may be a good solution for learners looking to master data science in a traditional classroom environment. Many students find that they benefit from the design of the classroom space mitigating outside distractions, and the presence of a live instructor can be incredibly helpful for students who need to ask questions and receive guided feedback on their work. Although data science is accessible, it remains a computer science discipline, so students may find that they need assistance early on in the process. As a bonus, students may benefit from working alongside and collaborating with their fellow classmates on data science projects. These courses tend to be taught at training centers located in major cities, so some students may find that their options for in-person training are limited.

For these students, providers such as the Flatiron School and General Assembly offer in-person data science training in many major cities. General Assembly’s data science options range from a seven-hour introductory course to a 12-week immersive class, all of which cover select topics in data science and data analytics. Meanwhile, the Flatiron School’s offerings, which are available in cities like Austin, Denver, San Francisco, Washington D.C., and more, include three- and five-month intensive data science courses.

Virtual Data Science Classes

Aspiring data science experts should also consider the benefits that a virtual course can offer them. In addition to providing the same high-quality educational opportunities one would expect from an in-person class, virtual classes allow learners to attend lessons and complete assignments entirely remotely. This makes these classes perfect for students who prize a more flexible learning path than an in-person class may be able to provide. Students will still have access to a live instructor who can respond to their concerns and guide them through difficult aspects of the course, but they won’t be limited to the courses they can take based on their location. Students may need to acquire their own hardware and software, though many training programs offer assistance in setting up a remote learning system.

As a leading provider of in-person and online tech education for over 30 years, Noble Desktop is proud to offer virtual data science classes and data science bootcamps at a number of different levels. Noble’s ||CPN411|| program provides students with an immersive learning experience that fosters mastery and job skills for aspiring data scientists. In this career-focused course, students will learn the foundational principles of data science and receive expert instruction in practical uses of SQL and Python for data science projects. Working with real-world datasets, students will receive a comprehensive education in data wrangling, manipulation, and analysis. They will also learn how to maintain and query databases—a vital skill for all data scientists. Since this is a career-focused course, students will also receive career assistance through one-on-one mentorship sessions and professional development training. By the end of the program, students will be prepared to enter the job market as qualified professional data scientists.

Noble also offers a collection of shorter courses focused on specific data science fundamentals such as their Python for Data Science Bootcamp. In this class, students will be introduced to the Python programming language and its important data science functionalities. This is an ideal course for novice programmers looking to learn the basics of computer programming with Python while simultaneously picking up valuable data science training. Students will learn how to write conditional statements to control the flow of a program, optimize their code with object-oriented programming, and use common Python data science modules like Pandas, NumPy, and Matplotlib. Once students have successfully completed this course, they can enroll in more advanced data science courses such as Noble’s Python ||CPN633|| or their ||CPN767|| course. These courses focus on Python’s function in machine learning algorithms and automated data collection processes.

Thinkful offers a Data Science Flex program that teaches students how to manipulate, organize, query, and interpret large datasets. This course teaches students foundational data science principles in a career-focused learning environment. Students receive training in more advanced, in-demand data science skills such as machine learning algorithms, making this a desirable course for students looking to stay on the cutting edge of the field. Over the course of this program, students have one-on-one mentorship sessions with data science professionals who help prepare them for the job search.

New York Data Science Academy offers various courses for students looking to learn data science skills. These courses range from an Introductory Python course that teaches students the basics of programming data science applications and databases using Python, up to a more advanced ||CPN416||: Machine Learning course that teaches students how to use Python to train machines to “learn” from datasets without a human operator inputting commands. Since New York Data Science Academy specializes in data science training, they offer robust training options for students of all levels. Their flagship Data Science Bootcamp provides students with a comprehensive data science education to prepare them for a career in the field. Enrolled students receive career-focused training in hands-on modules that prepare them for the kinds of data science work they can expect to handle in their professional lives.

To learn more about the various options available for data science training in your area, please consider exploring Noble’s Classes Near Me tool.

Noble Desktop’s affiliate companies also offer virtual data science learning opportunities. For example, NYIM Training’s data science classes include courses in Python’s applications for automation and machine learning. In contrast, NYC Career Centers’ data science courses include a popular “||CPN773||” course for complete beginners looking to understand the fundamentals.

Noble Desktop is also proud to offer data science classes for high school students. Interested learners should consider enrolling in NextGen Bootcamp virtual Summer Data Science Classes for High Schoolers.

What Should You Consider Learning Alongside Data Science? 

Learning data science is a multifaceted journey that benefits from complementary knowledge and skills. For example, a strong foundation in statistics is fundamental for data science. Concepts like probability, hypothesis testing, and regression analysis are regularly used in data analysis and modeling. Expanding your statistical knowledge enhances your ability to derive meaningful insights from data.

SkyPlus Research offers a course called Business Statistics—Basic. In this business training, students will gain a solid introduction to the practical applications of descriptive statistics. They’ll explore fundamental concepts such as different types of statistics and variables, as well as data types like cross-sectional and time-series data. They’ll dive into the art of describing data effectively, encompassing topics like frequency distributions, graphic presentation, and measures of central tendency. 

Proficiency in programming languages such as Python and R is essential for data science. These languages are widely used for data manipulation, analysis, and machine learning. Learning to code allows you to automate tasks, work with large datasets, and develop custom solutions.

Practical Programming offers a course called Introduction to Python Programming. In this course, students will embark on a journey into the world of programming, mastering the syntax, principles, and problem-solving strategies underlying this essential skill. Throughout the course, they’ll develop proficiency in using a text editor and terminal, enabling them to craft their own Python code for data iteration and problem-solving. Through hands-on exercises, they’ll learn how to think like a programmer, gaining confidence in Python’s syntax and crafting solutions for real-life challenges. 

A solid grasp of mathematics, particularly linear algebra and calculus, underpins many data science algorithms and models. Understanding matrix operations and derivatives is crucial for advanced machine learning techniques. 

The New York City College of Technology offers a course called Basic Math Refresher. This comprehensive course serves a dual purpose. First, it provides a fundamental refresher in pre-algebra and basic algebra, making it accessible and beneficial to individuals across various fields. Secondly, it customizes the mathematical content to align with students’ specific career aspirations, whether they’re venturing into healthcare, finance, or accounting. With tailored instruction, they’ll acquire the math skills necessary for their chosen career path. 

Data science and machine learning go hand in hand. Exploring machine learning algorithms such as decision trees, neural networks, and support vector machines, expands your predictive modeling capabilities and opens doors to various applications. 

Noble Desktop offers a class called Python ||CPN633||. Students will master machine learning in this hands-on bootcamp, where they’ll create algorithms for pattern recognition and decision-making. They’ll explore key tools like linear and logistic regression, and delve into diverse algorithms such as k-nearest neighbors, decision trees, and random forests. Students will learn how to assess model accuracy, select optimal features, and solve real-world problems. 

Deep learning is a subfield of machine learning, and it focuses on neural networks with multiple layers. Learning about deep learning architectures and frameworks like TensorFlow and PyTorch enables you to tackle complex tasks like image recognition and natural language processing. 

Chainhaus offers a course called Introduction to Deep Learning. In this course, students will dive into machine learning without needing prior advanced math knowledge. They’ll explore core concepts, neural networks, matrix math, activation functions, and gradient descent. They’ll learn how to use PyTorch for Convolutional Neural Networks, generative art, and even build a ChatGPT-like language model and chatbot. By the end, they'll create image classifiers and language models while using tools like Hugging Face and LangChain. 

Effective data visualization is vital for conveying insights to non-technical stakeholders. Learning tools like Tableau, Matplotlib, Seaborn, or ggplot2 helps you create compelling visualizations that tell a story from your data. 

Noble Desktop offers a course called ||CPN848||. Students will explore the world of data visualization and master the powerful tools of Tableau Public in this comprehensive course. They’ll begin by understanding the fundamentals of data connection, exploration, analysis, filtering, and data structuring to craft compelling visualizations. As they progress, they’ll delve deeper into Tableau’s capabilities, harnessing geographic data for dynamic maps and customizing their visualizations to perfection. By course completion, they’ll possess the expertise to confidently manipulate and publish a diverse range of visualizations, unlocking new insights and storytelling potential with data. 

Structured Query Language (SQL) is used for managing and querying databases. Proficiency in SQL is valuable for accessing, transforming, and extracting data from relational databases, a common task in data science projects. 

Noble Desktop also offers a course called ||CPN834||. Students will unlock the power of data through SQL in this hands-on course, starting with the fundamentals of table navigation. They’ll swiftly progress to crafting SQL queries to extract precise information. They’ll discover how to filter, group, and transform raw data into actionable insights.

As data scales, so do the tools and technologies used to handle it. Learning about big data platforms like Hadoop and Spark enables you to work with large datasets efficiently and perform distributed computing tasks. 

Sprintzeal Americas Inc offers a course called Big Data Hadoop Analyst. Students will gain invaluable knowledge and skills in this Big Data course, designed to elevate organizational prowess. They’ll enhance their security expertise, boost productivity and open doors to new opportunities by mastering the Hadoop framework. They’ll dive into the world of Big Data tools, gaining hands-on experience to tackle real-life projects with confidence.

With increasing concerns about data privacy and ethics, understanding ethical considerations related to data collection, usage, and sharing is essential. Compliance with regulations like GDPR is also critical. General Assembly offers a course called Ethics by Design Series: Ethics of Algorithms & AI. Students will delve into the ethical dimensions of Artificial Intelligence (AI) with this comprehensive course. They’ll gain insights into the ethical considerations surrounding data collection and algorithmic processes and engage in thought-provoking exercises and scenario planning to navigate complex AI ethics. They’ll develop a profound understanding of the discourse surrounding privacy and democracy within the realm of algorithms and artificial intelligence.