A Complete Guide to Data Science Classes
What is Data Science?
Data science is an interdisciplinary field in which data is examined to solve a problem or provide useful insights for an organization. Data Scientists work with tools, computer programming, math, AI, statistics, and computer science to analyze vast amounts of data. The ultimate goal of the analytics process in data science is to determine what occurred, why it happened, what’s expected to transpire in the future, and what should be done with the results. This all involves finding meaning in data.
Data science has applications across industries. The field has grown increasingly popular in response to the ever-increasing abundance of data that is generated each day. Until these data are studied and someone makes sense of them, they’re not of use to the organization. This is why businesses around the globe are turning to data science and incorporating data-driven decision-making practices more than ever. In the words of Jodi Petersen, “Data is the language of the powerholders.”
Data Scientists strive to articulate the most relevant questions for their organization and gather data from a range of sources to begin to find answers. This process involves organizing and analyzing this information and translating the results into solutions that can be communicated with internal or external stakeholders. Because of the integral role they play in helping their employees solve issues, they are highly valued and typically earn a competitive salary since they directly contribute to their organization’s success.
A Brief History of Data Science
Although the history of data science as its own discipline is brief, its roots trace back hundreds, if not thousands, of years to the origins of statistical analysis. In the 1960s, the formal field of data science appeared as an emerging profession in response to a growing need from companies to find data professionals who could gather, organize, and analyze huge volumes of data and make predictions based on their findings. Little did they know at this time how much data would be created during the next half-century.
In 1947, Peter Naur then used the term “data science” repeatedly in his work, Concise Survey of Computer Methods, which made it a more well-known name. In the decades since, as computing technology revolutionized how data could be collected, stored, and manipulated, the field of data science has evolved far beyond statistics and transformed into a discipline that incorporates machine learning, the Internet of Things, and AI. Each day, more data than ever before is being created (an estimated 403 million terabytes in 2024). As more data becomes available for analysis, businesses of all types turn to Data Scientists to help them find ways to increase profits and make better decisions.
Data Science Training Locations
Data Science Classes Onsite
If you're interested in picking up a new skill to start a new career with (or a skill that will help you advance in your current field), it's worthwhile to think about which skills will actually make a difference in the long run. This is especially true if you're in a city where data and tech have a strong presence. These fields will have the most opportunities across various industries. Data science, for instance, is one of these fields. Whether you're already working in tech or curious about entering this line of work, learning data science can get you where you want to go. Of course, figuring out how to learn a new skill is just as important. Onsite training works because it gives you hands-on experience and interaction with the material. In many of the major cities across the U.S., you can find onsite data science classes to help you build your skills and stay on track with your learning journey.
Data Science Classes NYC
185 Madison Avenue, 3rd Floor, NYC, New York, 10016
New York City is a fast-paced place that evolves in new ways every day. With advancements in data-driven technology, companies have steadily adapted to new methods for making better business decisions. Today's data-driven world calls for more data professionals in NYC, like Data Scientists, who help uncover hidden trends in complex data and keep a competitive edge against other companies in the region. In a place like NYC, where competition is rife, this is especially important. From Wall Street and tech companies like Meta or healthcare institutions such as Mount Sinai, data science is needed nearly everywhere. You can learn data science at Noble Desktop, which offers in-person, online, and onsite training. Your team can request a group session at your location to get started learning data science and see where it can take you.
Data Science Classes San Diego
3904 Groton Street, San Diego, CA, 92110
Although many may think of San Diego as a laid-back region, there's a healthy amount of hustle culture, too. This is mainly due to the mix of industries in San Diego, such as healthcare, tourism, and even defense, as the region is home to several major naval bases. However, tech reigns supreme in this region. While not completely comparable to San Francisco, the city of San Diego has a booming tech scene that still manages to balance a competitive job market with a strong economy. Data Scientists and Machine Learning Engineers are in high demand and can typically find work in any number of industries in San Diego. VDCI offers data science courses that touch on essential concepts and programming languages. Their onsite training is available at your location or online, which gives your group a choice in the matter. From data cleansing and analysis to modeling and visualization, there's much to learn in the field of data science.
Data Science Classes Washington, D.C.
600 Maryland Avenue SW, Washington, D.C., 20024
The nation's capital is a hotbed for data science professionals, many of whom find work in the federal government or with government contracting companies, such as Booz Allen Hamilton or Deloitte. The U.S. government and its contractors handle unfathomable amounts of important data that inevitably shape public policy and indicate trends in citizen behavior. Data science has become a necessity for many working in the public sector. As a result, there are abundant training opportunities, much like the options at Graduate School USA. They offer onsite training in subjects like data analytics and Python, which are foundational for a data science career. This kind of learning allows your team to acquire skills at the same time and pace, so you can track real-world data and learn how to apply these skills back in the office. Those looking to take the next step in their career and make a real impact would benefit greatly from data science training.
Data Science Classes Chicago
29 E. Madison Street, STE 1620, Chicago, IL, 60602
The economic makeup of Chicago is both diverse and strong, with industries like finance, healthcare, and aviation reigning supreme. And, because Chicago's industries command a data-heavy environment, there's especially stability in data-based roles like Data Scientist or Financial Analyst. Data science skills help professionals uncover everything from trends in the market to consumer behavior -- both of which enable businesses to make better decisions. In truth, data science is one of the most beneficial skills to learn in a place like Chicago. Practical Programming, an affiliate of Noble Desktop, offers onsite training with a focus on data science, FinTech, and analytics. Your team can collaborate on projects and learn how to apply your new data science skills in a real-world context with the help of onsite training.
Data Science Classes Los Angeles
Most people associate Los Angeles with the entertainment industry, but the work that goes on behind the scenes in Hollywood is just as important as what ends up on the big screen. The vast majority of industries -- including entertainment -- are heavily reliant on data-based solutions to learn what's working and what needs to change. In film, television, and music, data professionals work diligently to uncover trends and optimize the performance of the service so that the right shows reach the right audience and that consumers continue to utilize the services. Take Spotify, for example: their data professionals work to provide carefully curated suggestions for users so that everything on the platform is personalized and up to the individual's liking. Whether you use your data science skills to manage budgets for an upcoming film or to provide personalized recommendations, data science is well worth exploring in L.A.
Data Science Classes Dallas
The Lone Star State has a growing tech hub: Dallas. With major companies like Texas Instruments and AT&T with headquarters directly in Dallas, and Alkami Technologies and the Sabre Corporation with headquarters in towns nearby, there are plenty of opportunities for data science professionals to find work and thrive in this ecosystem. Data science is especially valuable in a place like Dallas, where the industries are rather diverse. Tech is thriving and intersects with other major industries, like healthcare and telecommunications. If you have data science skills, you aren't limited to one industry in Dallas -- you have the flexibility to explore all kinds of fields that interest you. Learning data science in Dallas allows you to move freely through the job market and find something that suits you.
Data Science Classes Boston
Boston is known for having some of the top-rated universities in the world (think: Harvard), but there's more to the city than educational institutions. In fact, Boston also has a highly developed medical system and financial sector. In any of these industries, Data Scientists can find work that is both fulfilling and important to the organization as a whole. With proper data science professionals, institutions like Massachusetts General Hospital are able to analyze data and identify patterns in public health trends, which can essentially be the quickest, most effective way to prevent the spread of infectious diseases. In a city that thrives on knowledge and innovation, data science isn't merely a useful skill -- it's vital. Learning data science in a city like Boston opens doors to new opportunities where you can make a positive impact not just for your employer, but the community at large.
Data Science Classes Miami
Miami may be famous for its beaches, but it's gaining traction in the professional sphere as well. Industries like tourism and finance are thriving, largely in part due to the rise in tech. At the heart of this evolution is none other than data. Between tracking patient outcomes at hospitals like the Cleveland Clinic Weston and improving customer experiences at many of the city's luxury hotels, data science plays a huge role. What makes data science most appealing in a place like Miami is that it provides more versatility, which in turn gives you more choice. In a city like Miami, you can have the best of both worlds: gorgeous beaches, fun events, and exciting nightlife, all while having professional opportunities and success.
Data Science Classes Philadelphia
A historic city such as Philadelphia has been through many changes. Most recent is the expansion of data-driven solutions in industries like education and healthcare, which are rather prominent in the region. A Data Scientist could work with the University of Pennsylvania to determine ways to improve student performance. Other roles, such as a Machine Learning Engineer, may work at hospitals to build algorithms that create personalized treatment plans. All in all, data science roles help turn data into insights, ultimately leading to the best possible course of action for the company. In a city that values innovation, data science is a skill worth having in Philadelphia.
Data Science Classes Phoenix
Phoenix has seen considerable growth in the past few years, and with this growth is a demand for professionals with strong data science skills. If you're aiming to start a career in Phoenix and want to stand out against others in the competitive job market, learning data science is a smart choice. Whether you specialize in predictive modeling or machine learning, there's plenty of opportunity and stability in this field. You can also work across the many major industries in the Phoenix region, like healthcare, education, and local government.
The Data Science Project Lifecycle
In data science, the project lifecycle provides a structured outline for each stage of a project so that Data Scientists can be prepared to find data-driven solutions to business problems. This framework is intended to help them effectively and efficiently handle projects.
- Identify the problem. The first task a Data Scientist must do is identify the specific problem that has to be solved.
- Gather data. This stage involves collecting any data that will be used for analysis. It must be complete, accurate, and relevant.
- Prepare the data. Step three entails data cleansing and transformation to ensure the information can be properly analyzed. At this stage, any missing values, irrelevant data, or duplicates will be removed, and the data will be transformed into a format that allows it to be analyzed.
- Analyze the data. The fourth data science project lifecycle step involves applying different analytical methods to the data so patterns and insights can emerge. This stage may incorporate techniques like clustering, regression analysis, or machine learning algorithms.
- Build a model. The model-building phase involves creating a predictive model based on the data analysis results. This model will be used to predict possible outcomes.
- Evaluate the model. Step six entails testing how the predictive model is performing to make sure it’s accurate. A sample validation set is used to assess the model's reliability.
- Deploy the model. The last step in the project lifecycle requires deploying the predictive model into production before it can be used in real-world environments. At this time, the model will need to be integrated into the organization’s existing systems and processes to ensure its effective use.
What Tools Do Data Scientists Use?
Those who are interested in learning data science may wonder what tools they’ll use in this profession. The following are some of the most popular coding languages, skills, and software that aspiring data professionals will likely encounter:
- Python is the most commonly used coding language in data science. Its scientific computing libraries, such as Pandas, SciPy, and NumPy, help Data Scientists manipulate, analyze, and visualize data.
- R is another programming language commonly used in data science. This language can handle huge datasets and provides users with a robust collection of graphical and statistical methods.
- SQL, or structured query language, is another go-to tool for Data Scientists. It helps them communicate with and manage relational databases. SQL is an essential coding language for those who want to find and analyze data from complex databases.
- Tableau is a popular business software that can connect to a range of data sources, clean and prepare data, and create engaging visualizations like maps and charts.
- Jupyter Notebooks is an open-source web app that Data Scientists use to make shareable documents that contain data visualizations, live code, equations, and exploratory information.
- Chat GPT is a tool that’s powered by AI and used to perform a variety of data science tasks. It can create and execute Python code and provide analysis reports. This tool also comes with different plugins with applications for math, research, statistics, and automation.
- Microsoft Excel is a spreadsheet application that Data Scientists use to analyze data, create PivotTables and charts, and visualize data findings.
What Can I Do With Data Science Training?
Data science training is an in-demand skill set that can help you improve your work environment, help your organization make better decisions, and provide you with the tools and knowledge to solve pressing problems. Data science has a range of applications in various industries:
- Those who work in banking, financial services, and insurance turn to Data Scientists to perform tasks like providing customized financial devices, detecting fraud, and applying machine learning algorithms to stock trading and risk analysis.
- Marketing and advertising professionals rely on those with data science knowledge to create targeted ad copy, provide customized recommendations for services and products, and optimize their presence on social media platforms.
- In the healthcare sector, algorithms created by Data Scientists create individualized care plans and provide patients with better services. When data science is applied to medical imaging, it helps providers make more informed diagnoses and treatment options.
What is the Hardest Part About Learning Data Science?
Data science is like any other skill in that the difficulty level associated with learning it depends on a host of subjective factors, such as individual learning style and the type of training format selected. Because this field is broad and requires knowledge of various coding languages (R, Python, and SQL), data visualization tools (Excel and Tableau), and other software and applications, it takes time and practice to become a skilled Data Scientist.
Some of the most challenging aspects of learning data science pertain to the need for students to have both soft and hard skills, knowledge of industry-specific tools, and a drive to stay current in the ever-changing landscape of data science. Data Scientists can be found in many industries, from retail to healthcare. Each industry has its own tools that Data Scientists will need to learn, such as Power BI or Tableau. This can add time to the learning process. Additionally, some Data Scientists struggle to grasp skills like data mining, how to communicate data findings with stakeholders, and how to make sense of data findings.
Some learners may also find it difficult to think of data science as an ongoing learning process rather than a static set of skills. Over the past decade alone, this field has expanded in numerous ways thanks to innovations like deep learning, AI, and machine learning. As more technological advancements are introduced, data science will likely continue to evolve for years to come. Data Scientists need to stay abreast of current trends and software, but some learners may find that challenging.
One of the most effective ways to expedite the data science learning process and ensure you’re getting the training you need is to enroll in a comprehensive certificate or bootcamp program. These courses are available in person and online from many top educators around the country and provide students at all levels with training in a range of data science skills, programming languages, and techniques.
How Long Does it Take to Learn Data Science?
No two learners are the same in terms of the skills they bring to the data science learning process or their ultimate goals for training. The associated with learning data science is largely dependent on variables like what skills the learner brings to the table and the level of proficiency they ultimately hope to acquire. On average, it takes most individuals between six months and several years to truly become proficient in this field. Usually, learning the basics takes less than a year, whereas advanced-level skills may require years of study.
The method of training the learner opts for is a huge determinant of the speed at which they learn data science. Those who opt for self-guided resources like online tutorials or YouTube videos will likely take much longer to learn data science than those who enroll in a live course and can benefit from real-time instruction in just a few weeks or months.
How Does Data Science Differ from Data Analytics?
The terms data science and data analytics are often used interchangeably. Even though these disciplines have much in common, they differ in important ways. The first distinction is in their scope. Data science is a broad term that describes several fields that are used for mining large datasets. In data analytics, the tools and software used are typically more focused and contribute to a larger process. The data analytics process is done to provide actionable insights that can then be applied based on prior queries. Whereas data science focuses more on sorting through massive datasets to unearth insights, data analytics usually involves a more focused exploration of a specific question that must be answered based on prior data.
Ultimately, the goal of data science is to offer insights that inform which questions to ask, and data analytics strives to find answers to these questions. Data Scientists are more concerned with posing questions than finding answers.
Data Science Career Paths
The following are several possible career paths you can pursue with data science training:
- Data Scientists determine the questions that must be answered and where the data necessary to answer them is located. They must be skilled in data mining, cleaning, analysis, and presentation.
- Data Architects perform tasks like ensuring that the data used by Data Analysts and Data Scientists is properly formatted and accessible. These professionals also design and oversee database systems and their functionality.
- Environmental Data Scientists examine how elements of the natural environment relate to one another. They use tools to shed insight into data pertaining to water, land, air, and biodiversity and explore what drives different ecosystem processes. These professionals must not only manage environmental data but also draw inferences on various environmental challenges.
- Data Engineers are tasked with developing, deploying, managing, and optimizing data infrastructure and pipelines. They transform data and transfer it to Data Scientists, who will then use it for querying.
- Machine Learning Engineers work with machine learning algorithms, including classification and clustering, as well as software engineering, programming, and statistics.
- Data Analysts are given questions that must be answered. They analyze data to provide insights that can inform their organization’s business strategy.
- Database Administrators ensure that databases are performing properly. This involves tracking data flow and creating backups.
- Data Storytellers do more than just visualize data and communicate their findings; they provide a narrative that contextualizes their data findings so that audience members can understand the results.
Why Enroll in an Online Data Science Class?
One of the most effective ways to learn data science is to enroll in an online class. Two types of online training formats are available, live and asynchronous, and determining which one is a good match for you will depend on several factors.
Live online data science classes are available for those who want to receive real-time training in one or more data science tools. Students who opt for live online classes connect with the instructor using Zoom or another teleconferencing platform. They can complete all coursework remotely from their own space. The live training format allows them to ask questions as they occur and receive immediate support. Participants can even share their screen with the instructor (with permission) for additional help. In live classes, students also benefit from studying alongside others in the virtual classroom, which can provide a sense of community and support not possible with independent coursework. Live online data science classes often provide instruction in a range of in-demand skills and tools, such as Python, SQL, machine learning, and data visualization.
If you work full-time or have a busy personal life, live training may not be a viable study option since it requires classes that meet at regularly scheduled times. Instead, asynchronous classes may be more suited to your scheduling needs. This type of training is available from many providers, including Udemy, Skillshare, and Coursera. Coursework is pre-recorded and loaded online. Students can access lessons on their own time and dictate their learning speed. This flexible learning approach allows participants to pause, rewind, or even rewatch entire lessons to ensure they’re grasping the material. On-demand classes are available for learners at all levels. It’s important to remember, however, that since no instructor is present, it’s up to the student to find answers on their own to any questions that arise.