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In today’s digital ecosystem, businesses and organizations collect unimaginable amounts of data on their customers, users, clients, and constituents. In 2020, approximately 40 zettabytes of data were created (or 40 billion terabytes, roughly 50 times the amount of sand on all the world’s beaches). Every day, internet users create as much data as was created in hundreds of years of human history, and data science is the field responsible for collecting, organizing, and using this data.

Data science is a broad term that encompasses how individuals and companies gather, clean, analyze, and communicate enormous datasets to make strategic business decisions. The field of data science continues to emerge as one of the most promising and in-demand career paths for talented professionals. Many companies today have an incredible amount of data but lack the human power and tools to gather meaningful insights from that data, so there is explosive demand for data science professionals. Learning data science is one of the best ways to boost your hiring potential and start a profitable career.

Data Science Skills

Excel

When you think about who uses Microsoft Excel, the first professionals you think of may be accountants or managers responsible for tracking budgets, but Excel is also vital for data science professionals. Excel spreadsheets and workbooks are used to construct and organize the basic databases that Data Scientists utilize and query. In addition to these foundational uses, Excel's functionality extends to more sophisticated data manipulation and analysis techniques such as pivot tables and advanced filtering, which are indispensable for exploratory data analysis. This allows Data Scientists to quickly identify trends, anomalies, and patterns in datasets before moving on to more complex analytical tools. Excel also has powerful data visualization features that make it useful for communicating information to invested parties. For instance, the ability to create custom charts and graphs can help in presenting complex data in an accessible way, communicating clearer insights for decision-makers. Furthermore, Excel's integration with other data analysis tools and platforms enhances its utility in the data science workflow.

Skilled Data Scientists continually build on their Excel databases using languages like SQL and Python, integrating Excel with these languages to automate data processing and analysis tasks. This seamless integration demonstrates the versatility of Excel in data science applications. Knowing advanced Excel functions is an important skill for Data Scientists and Data Analysts to learn as it lays the groundwork for understanding data structure and manipulation that are crucial in the field. Mastery of Excel can significantly speed up preliminary data analysis and preparation, serving as a bridge to more specialized data science tools and techniques.

SQL

SQL, or Standard Querying Language, is one of the most popular programming languages for building, accessing, and querying databases. Its ubiquity in the tech industry is due to its powerful, yet intuitive, syntax for managing and manipulating database information. Data science relies heavily on the ability to collect and organize huge amounts of data, far more than human beings can process, and SQL is one of the languages that make this possible. By facilitating the structuring of databases in a relational manner, SQL enables Data Scientists to efficiently organize, store, and retrieve vast datasets.

Not only does SQL help users build programs that store this data, but they can also use it to automatically query that data using complex, conditional statements and relational searches. This capability is crucial for performing in-depth analyses, as it allows users to execute sophisticated queries that combine data from multiple tables, filter records based on specific criteria, and aggregate data to produce summaries. Such functionality is indispensable for uncovering patterns, trends, and correlations in the data, which might not be evident without the ability to perform these complex queries. This allows users to find information within the database that would be otherwise invisible to someone who was simply attempting to look through the data manually. The power of SQL lies in its ability to sift through millions of records in seconds, identifying specific data points based on the query parameters set by the user. This level of efficiency and precision greatly enhances the productivity of data professionals and reduces the time needed to derive actionable insights from data.

In addition to making these queries directly, SQL is also used to create automated querying statements to help users discover new insights in their data. Through the use of stored procedures, triggers, and scheduled tasks, SQL enables automated data processing and analysis workflows. These automated processes can run complex queries at regular intervals, monitor data for specific conditions, and even alert users to significant events or changes within the database. By automating these tasks, organizations can ensure that their data analysis efforts are both consistent and scalable, allowing them to continuously search their databases for new opportunities, trends, and efficiencies without manual intervention.

Python

Arguably, the most important skill for aspiring data science professionals to learn is Python. Python is a versatile, general-purpose programming language that is, according to TIOBE, the most widely used programming language in the world. It is used heavily in data science projects for managing and organizing data, regardless of the kind of data in question. It is also important for the computational programs that allow machines to automatically read and organize data such as programs for scraping user data from web browsers or online traffic. Owing to these major functions, Python is also an important programming language in the emerging field of machine learning since it powers the algorithms that allow computers to read data and interpret data without the need for a human operator.

The significance of Python extends to various sub-disciplines of data science, including but not limited to, statistical analysis, artificial intelligence (AI), and deep learning. Python's extensive libraries such as NumPy for numerical computation, Pandas for data manipulation, Matplotlib and Seaborn for data visualization, and scikit-learn for machine learning, make it an indispensable tool for Data Scientists. These libraries, combined with Python's simplicity and readability, allow for the rapid prototyping of complex data models and algorithms, significantly reducing development time compared to other programming languages.

The active and ever-growing Python community also plays a pivotal role in this coding language’s dominance in data science. This community contributes to a vast ecosystem of open-source projects, tools, and libraries, continuously expanding Python's capabilities and applications. As a result, whether one is scraping web data, performing complex statistical analyses, visualizing trends, or developing cutting-edge AI algorithms, Python offers a comprehensive and accessible platform for data science professionals. This wide-ranging applicability and support make Python a critical tool for innovation and research in the data-driven world.

Data Visualization

Data isn’t particularly useful if one can’t communicate what inferences and conclusions others should draw from the data. This makes data visualization an important skill for Data Analysts and other professionals to learn. Data visualization tools help users transform raw numbers and data into visually compelling charts, graphs, and maps. Two of the most common data visualization tools are Tableau and Microsoft Power BI, each of which provides users with the tools to automatically and dynamically update their informational graphics with new or changing data. Both programs are designed to support non-programmers looking to visualize data, meaning that even professionals like journalists and policy advocates can benefit from learning these applications. However, professionals who know Python and SQL can write custom programs to empower these platforms to offer even more professional functionality.

Beyond the basics of creating interactive charts and dynamic dashboards, advanced knowledge of data visualization techniques can significantly enhance the ability to uncover and communicate complex insights from data. For instance, understanding the principles of good design and data storytelling can elevate a simple visualization to a compelling narrative that engages and informs the audience. This skill is crucial in fields ranging from business intelligence to public policy, where the ability to distill complex information into clear, actionable insights can drive decision-making and influence strategies. Furthermore, as data becomes increasingly central to organizational operations, the demand for professionals capable of not only analyzing but also effectively presenting data will continue to rise.

Common Uses for Data Science

Data Analytics

One of the most common professional uses for data science skills is data analytics. Given how much data is produced daily, businesses, institutions, and other organizations are realizing how much they can gain by collecting and analyzing this data. Data analytics refers to the process wherein professionals utilize data science tools to look at things like consumer data, web traffic, demographic information, or advanced analytic metrics to draw conclusions and make recommendations based on these conclusions. Data analytics is utilized in almost every field and industry today since the programs built by Data Scientists offer an incredibly robust set of tools for gathering information. Businesses use data analytics to track consumer behavior and improve their marketing strategies, government agencies utilize data to understand demographic behaviors better and make policy recommendations, and non-profit advocacy organizations use data analytics to build more persuasive targeted campaigns. Data analytics is even used in fields like professional sports and fitness training to help athletes and coaches make quantitative decisions.

The expansive reach of data analytics across various sectors highlights its critical role in driving innovation, enhancing efficiency, and fostering informed decision-making. In the healthcare sector, data analytics is revolutionizing patient care through predictive analytics, allowing medical professionals to anticipate patient needs and improve outcomes. In the financial industry, it aids in detecting fraud, assessing risk, and personalizing customer services. In regards to education, data analytics helps institutions tailor learning experiences to student needs, improving educational outcomes. The versatility of data analytics, powered by sophisticated algorithms and machine learning techniques, allows organizations to leverage their data in ways previously unimaginable, turning vast amounts of information into a strategic asset. As the volume of data continues to grow, the ability to efficiently analyze and extract meaningful insights from this data will remain a pivotal skill. No matter what field one wishes to start or advance a career in, there is a good chance that learning how to read and analyze data can help aspiring professionals break into the industry.

Machine Learning

In recent years, the data science landscape has been dramatically reshaped by advancements in machine learning and AI. At the heart of this revolution is machine learning—the craft of designing algorithms that empower computers to interpret data, learn from it, and then make informed decisions or predictions without needing step-by-step instructions from humans. This breakthrough is not just about algorithms that suggest what movie you might like next on Netflix or what product to buy on Amazon. It's about paving the way for more sophisticated technologies such as highly interactive chatbots that can mimic human conversation or AI-driven art programs that can create breathtaking visuals.

Perhaps even more exciting is that these technologies are still in their infancy, meaning there is still much about their capabilities to explore. From revolutionizing how companies understand and engage with their customers through predictive analytics to pioneering new approaches in healthcare that can anticipate patient needs, the impact of machine learning is vast and varied. Even more exciting is the potential for machine learning to transform entertainment, making digital interactions more immersive and personalized than ever before.

What Industries Use Data Science?

Data science is used in almost any industry you can think of, however, certain industries prioritize data science above others and offer more career opportunities for data science professionals.

Technology and Software

The technology and software industry uses data science to help drive innovation and shape the future of digital services. Data science in this realm is instrumental in analyzing vast amounts of data generated from user interactions, system logs, and network activities to enhance product offerings, optimize user experiences, and streamline operations. For instance, machine learning algorithms and predictive analytics are extensively used to personalize user experiences on platforms such as social media, ecommerce sites, and streaming services. By analyzing user behavior, preferences, and engagement patterns, companies can tailor content, recommend products, and predict trends that align closely with individual user needs, which in turn increases satisfaction and loyalty.

The technology and software industry also leverages data science for more technical advancements such as improving search engine algorithms, developing sophisticated cybersecurity measures, and enabling intelligent automation. In the realm of cloud computing, Data Scientists work on optimizing resource allocation, forecasting demand, and ensuring data security and privacy. The industry's drive towards innovation also sees the application of data science in emerging fields like augmented reality (AR), virtual reality (VR), and the Internet of Things (IoT), where data analysis provides insights into user interactions, improves system performance, and opens new avenues for technology development.

FinTech

One of the major industries that has been radically altered by the development of data science practices and technologies is the financial industry. Bankers, Financial Analysts, and Investment Analysts all utilize data analytics programs and tools to help them better understand the movement and trends of markets and assets. Using this data, financial specialists can provide more targeted recommendations for their clients, and they can more consistently make decisions that produce solid ROIs. Given the amount of money that is moved every day in the financial sector, it is imperative that analysts have a solid understanding of the tools and techniques that are used to help them identify trends that would otherwise be invisible within the data.

The emergence of FinTech, a blend of finance and technology, has innovated financial services and customer experiences, extending the impact of data science beyond traditional banking and investment strategies to areas like risk management, algorithmic trading, and personal finance management. Sophisticated algorithms now enable traders and fund managers to anticipate market trends with remarkable accuracy, facilitating strategic transactions less influenced by market volatility. Furthermore, the democratization of financial data and analytic tools equips individual investors with advanced insights and predictive capabilities, leveling the playing field between them and institutional investors. For anyone aiming to navigate the complexities of the financial sector or to cultivate their own investment portfolio, mastering FinTech programs and the principles of data science is crucial. This proficiency not only augments decision-making skills but also unveils novel avenues for innovation in a sector that is constantly on the forefront of technological advancement.

Healthcare

Data science is helping to revolutionize and personalize healthcare, offering unparalleled opportunities for improving patient outcomes, enhancing operational efficiencies, and driving medical innovations. In healthcare, data science applications range from predictive modeling and disease surveillance to personalized medicine and clinical decision support. By analyzing large datasets such as electronic health records (EHRs), genomic data, and wearable device data, healthcare professionals can identify patterns and correlations that lead to more accurate diagnoses, targeted treatment plans, and better patient care. For example, predictive analytics can forecast disease outbreaks, identify patients at high risk of chronic conditions, and help in preventing hospital readmissions by offering timely interventions.

Data science fuels advancements in medical research and drug development by enabling the analysis of complex biological data and streamlining clinical trials. Machine learning algorithms are increasingly used to sift through vast amounts of genomic data, identifying potential therapeutic targets faster and more efficiently than traditional methods. In personalized medicine, data science tools analyze patients' genetic information alongside their medical history to tailor treatments specifically to their unique genetic makeup, significantly improving treatment efficacy and reducing side effects. As healthcare continues to evolve, the integration of data science not only promises to enhance the quality and accessibility of care but also paves the way for innovations that could redefine health and wellness for future generations.

What Kind of Job Can I Get in Data Science?

While an understanding of data science is expected for many data-related career choices, there are still many other supplementary skills Data Analysts and data science experts need to know. Knowing this, it's important to understand which career path is best for you to better understand the amount of data science knowledge you need, as well as the other tools and programs you should explore. You should learn more about the salaries and careers you can achieve by learning data science before embarking on your new path.

Data Scientists are professionals who specialize in extracting meaningful insights from vast datasets through a combination of statistical analysis, machine learning, and data processing techniques. Their work involves cleaning and organizing data, performing complex analyses to uncover trends and patterns, and developing predictive models to support decision-making processes across various industries. According to the Bureau of Labor Statistics (the BLS), demand for Data Scientists is projected to grow by 35% between 2022 and 2032, making it one of the fastest growing occupations in the nation. Currently, the U.S. cities with the highest number of Data Scientists employed are New York City, San Francisco, and Los Angeles. The BLS estimates that Data Scientists in these areas earn an annual mean wage ranging from around $120,000 to $150,000.

Data Analysts primarily focus on interpreting existing data to provide actionable insights, often through the use of statistical tools, visualization techniques, and reporting. Unlike Data Scientists, who may delve into more complex algorithms and predictive modeling to explore new questions or forecast future trends, Data Analysts typically work with more structured data and concentrate on answering specific questions posed by the business or organization. They play a crucial role in helping companies understand performance metrics, customer behavior, and operational efficiency by analyzing datasets to find trends and patterns. While both roles are pivotal in data-driven decision-making, Data Analysts usually focus on the immediate analysis of data to support business decisions, whereas Data Scientists take a broader approach, using advanced techniques to extract insights and predictions from data. According to Indeed, the average base salary for Data Analysts in the United States is around $77,000 per year.

Machine Learning Engineers specialize in designing and implementing algorithms and models that enable machines to learn from and make decisions based on data. They focus on developing scalable and efficient software solutions that can process large volumes of data to improve automatic decision-making processes. Unlike Data Scientists, who may focus more on data analysis, exploration, and visualization, Machine Learning Engineers are primarily concerned with applying predictive models and using deep learning techniques to create and implement algorithms that can adapt and learn without human intervention. Their work is fundamental in the development of artificial intelligence applications such as recommendation systems, natural language processing, and autonomous vehicles, requiring a strong background in both software engineering and data science principles. Machine Learning Engineers in the United States earn upwards of $120,000 per year depending on experience and industry.

How Can I Learn Data Science?

There are several ways to go about learning data science. You may think that you have to earn a four-year degree to go into data science and while this is one option, it is not the only learning path that can lead to a data science career. In-person and online data science classes allow students to focus on the skills and knowledge needed to start or advance a data science career. These classes allow a streamlined learning process that focuses on real-world applications.

In-person data science classes are those taught at a physical location. Such classes are led by an instructor and provide the opportunity to network with local professionals who share your interests. The format works well for those who prefer to learn face-to-face and those who wish to use the class’s computer lab, which comes equipped with all the necessary software and tools required for the course. The main drawbacks to this class style are that you will need to factor in the time and cost required to commute to class, and your class options are limited to those courses offered close to where you live.

Live online data science classes offer similar benefits to in-person classes with a few additional conveniences. Live online classes are taught by experienced professionals in real-time, allowing students to ask questions, receive feedback, and collaborate in a manner similar to that of in-person classes. One advantage of a live online class is that you can learn from anywhere with a strong internet connection. This means that you can learn from the comfort of home and you can choose from classes offered around the world, maximizing your chances of finding a course that fits your learning goals, schedule, and budget. One thing to consider, however, is that live online classes tend to consist of students from many different cities, so you may not have the same networking opportunities afforded in an in-person class. You will also need to ensure your computer has all the necessary software installed prior to the start of class.

Virtual Data Science Classes from Noble Desktop

With live online data science training, you can learn this high-demand skill from anywhere. You’ll interact with the instructors in real-time and work on various hands-on projects. The cohort style of live online training also helps students to stay engaged and on track to complete the course. Students enrolled in live online classes will be able to receive personalized, real-time feedback from their instructors, which can be vital for students’ success. Students can even permit their instructors to interact directly with their devices remotely and guide them through difficult data science concepts. Once you have a firm understanding of data science, you’ll be able to start working on an impressive range of data-related projects.

Several schools, including Noble Desktop, the provider of the Classes Near Me tool, offer live interactive data science courses to help you meet your goals. For those interested in learning Python and Python’s many data science applications, Noble offers a Python for Data Science bootcamp taught by experienced professionals who cover many introductory programming concepts like loops, functions, and objects, as well as many common advanced data science concepts. In this class, students will learn the basics of using Python to wrangle, organize, and query data. They will also learn how to use Python libraries to create graphs and visualizations that communicate the content of this data. This wonderful introductory course offers students of all skill levels a way to start their data science training.

Noble also offers longer courses and comprehensive certificate programs catering to students with different interests, including a Python for Data Science & Machine Learning Bootcamp. This course not only covers the basics of Python programming but also explores various Python libraries, how to apply machine learning algorithms to data, data visualization, and other advanced skills. Students will learn how to write machine learning algorithms and how to train those algorithms so that the interpretations the machine makes are accurate, unbiased, and applicable to real-world problems. Like all Noble classes, it includes a free retake and a guarantee that you’ll learn the skills covered in the syllabus, subject to terms and conditions.

If you want to enter a career in data science, check out Noble’s Data Science Certificate program, which provides a comprehensive overview of the most important data science tools and concepts. The Data Science Certificate program introduces students to the field of data science and is heavily focused on utilizing Python for data science, but it also explores SQL applications and other concepts to help provide students with a deeper understanding of data science. This bootcamp is broken into four subject-specific units, so it is manageable and accommodating for those with busy schedules. For more details, check out the FAQ.

All of Noble’s courses come with a free retake option within one year, and the career-focused bootcamps, including the Data Science Certificate program, offer students professional development services to help ensure that they are ready for the job search. These professional development programs include focused portfolio-building seminars and one-on-one career mentorship sessions during which students can receive personalized assistance on any aspect of their job search process (from searching for jobs to writing cover letters to preparing for interviews).

Other Virtual Data Science Classes

Other schools, including Flatiron School, Metis, Thinkful, General Assembly, and others offer a variety of courses and bootcamps. Much like Noble Desktop, these schools offer their courses both in-person and online and are taught by industry experts. These other schools also offer short subject-specific courses as well as longer-form bootcamps, to best meet the needs of students. Since data science is such a wide and varied field, students have a range of options available to them in terms of the content and schedule of an online data science course. To learn more about the classes available, consider exploring your options for live online data science classes using Noble Desktop’s Classes Near Me tool.

For example, Flatiron School offers a comprehensive yet beginner-friendly Data Science Bootcamp that provides students with a comprehensive look at many important data science tools and concepts, including working with SQL and Python, running regression analysis, A/B testing, machine learning, and natural language processing. This course offers robust, career-focused instruction and aims to prepare students for a wide range of data science careers. This project’s capstone instruction emphasizes advanced machine learning algorithm programming.

Thinkful offers full-time and flexible part-time scheduling options for its Data Science Bootcamp. This is a beginner-friendly, career-focused course that aims to provide students with an immersive overview of the key concepts in data science. Students will learn the basics of programming with Python, SQL, and their important data science libraries. Then, students will build on these skills through guided, hands-on machine learning instruction. This course aims to provide students with the instruction that they need to begin work as Data Scientists or Data Analysts.

Students with a computer science or data analytics background may want to enroll in a more specialized training program. For example, General Assembly offers a Python and Machine Learning Bootcamp for students seeking training in machine learning practices. The course teaches students how to use Python to collect data from large datasets and internet traffic, and students will learn how to feed this data to a machine learning algorithm to start analyzing the data they’ve collected. This class is a good fit for students who are looking to learn a specific skill set that they can use to complement their existing knowledge.

Similarly, students may be interested in enrolling in a more focused career training class, like NYC Data Academy’s Data Science with Python: Data Analysis and Visualization class. In this course, students will learn how to apply data science and Python programming skills to practical data analysis tasks and exercises. They will learn the basics of programming with Python and receive guided instruction in using that Python knowledge to query, organize, and analyze data. Once students are comfortable with Python programming, they will learn how to use Matplotlib and Seaborn for visualizing data and producing evocative graphs and charts. This practical training is ideal for students looking to become data analysis professionals.

Other focused training programs aim to teach students how to apply their data science skills to specific professional fields. For example, Data Science Dojo offers a Data Science for Business Leaders course that teaches students how to use Python programming and other data analytics training in team management and business analysis. Courses such as these are ideal for students who know the field they want to pursue and build a robust skill set in that specific job role. Students enrolled in these courses will learn general-purpose data science skills, but they will be targeted at applying those skills to providing business management solutions.

Students who aren’t confident that they want to pursue a career in data science may want to take an introductory course such as Byte Academy’s Intro to Data Science program. In courses like this, students will receive accelerated training in the foundational principles of data science, including coding with Python and SQL and database management. These courses tend to be designed with beginners in mind, making them good places to start for students without a computer science background. Students enrolled in these courses will need additional training to become professional Data Scientists, but they will still receive targeted data science training that they can apply to additional skills training.

Students looking for an introductory course can also enroll in a program like Code Fellows Intro to Data Structures and Algorithms course. This course provides students with an introduction to working with data algorithms, particularly in the planning phase. This course teaches students how to work with data arrays, plan programming tasks, and learn important computer science concepts and vocabularies. This course aims to prepare students for practical interviewing skills, emphasizing teaching students how to demonstrate their proficiency with data-related concepts.

Finally, for students who aren’t entirely certain that data science is the field that they want to pursue, enrolling in a course like ONLC Training Center’s Python Programming Level 1: Introduction for Non-Programmers course is a good way to learn computer science skills while keeping your options open for additional data science training. Courses such as this one aim to teach students to be comfortable with the basic programming process with Python so that they can be prepared for more advanced data science training should they wish to continue their education. These classes are an ideal fit for students looking to learn computer science but unsure that they want to apply their Python programming skills to a data science career. These classes are also more flexible regarding scheduling since there isn’t a long-term commitment to the course.

Since data science is such a broad skill applicable across many roles and industries, you’ll want to be sure that you choose a course that meets your goals, whether you’re aiming to advance in your career or trying to learn the basics for your first data science position. When choosing a live online data science course, you’ll want to focus on the industry focus, job function, and course duration.

Remote Data Science Corporate Training

Data science training provides an invaluable asset to companies that work with data on an advanced level and use it to drive business decisions. Being able to read, interpret, and analyze data is a vital skill in virtually every professional industry and companies that ignore this data are leaving profits on the table. For companies looking to train their employees, virtual training is available through Noble Desktop. You can send participants to regularly scheduled group classes held virtually or schedule a private session for your team. Instructors can conduct the training for beginners to advanced users and customize the lesson plan to fit the needs of your team. Employers who are looking to provide alternative training options can also purchase discounted vouchers to let their employees sign-up for any of Noble Desktop’s open-enrollment data science classes. For more information about corporate training for your business, please email corporate@nobledesktop.com.

Elizabeth Gleesing

Elizabeth Gleesing is a university instructor and content writer at Noble Desktop. She earned her PhD in English from Syracuse University in 2023 with research focusing on how documentaries incorporate digital and new media technologies. Her research into artificial intelligence, data analysis, and emerging technologies informs her content writing as she frequently works on articles related to careers in data science, data analytics, AI, and related software.Elizabeth also has over ten years of experience teaching undergraduates in universities and colleges in New York and Washington state and won the Syracuse University English department’s Elson Teaching Award in 2019. Her experience in instruction allows her to coherently explain difficult topics to a variety of audiences.

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Key Insights

Learn More About Live Online Data Science Courses

Data Science Skills

Excel

SQL

Python

Data Visualization

Common Uses for Data Science

Data Analytics

Machine Learning

What Industries Use Data Science?

Technology and Software

FinTech

Healthcare

What Kind of Job Can I Get in Data Science?

How Can I Learn Data Science?

Virtual Data Science Classes from Noble Desktop

Other Virtual Data Science Classes

Remote Data Science Corporate Training

Live Online Data Science Corporate & Onsite Training Noble Desktop

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Live Online Data Science
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