What is Data Science?

Data science is a complex and exciting field, combining disciplines like math, programming, and artificial intelligence (AI). Data Scientists use their analytical and statistical skills to interpret large quantities of raw data and provide conclusions.

Glassdoor listed Data Scientist as one of the 50 Best Jobs in America in 2022, and the field continues to show a dramatic growth rate for the foreseeable future. If you’re interested in a career in data science, you’ll need comprehensive training.

In this overview, you’ll learn more about data science—what it can do, who uses it, and how to add this skill to your professional toolbox.

What Can You Do with Data Science?

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

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

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

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

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

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

How to Train for a Data Science Career: How Much Does it Cost?

Acquiring the right tools and software for data science training couldn’t be easier: many are open source, which means they’re free to download and use. These include Python, one of today's most popular programming languages.

Python’s worldwide development community offers helpful support to everyone, novices included. Free libraries like Scikit-learn, Matplotlib, and NumPy are readily available. And Python frameworks, including Django, Flask, and CherryPy, continue to be both in-demand and free or low-cost.

Other data science tools and programs may incur costs. Database systems like PostgreSQL are open source and free to use, and you can try many popular data visualization tools on a free trial basis before investing in a monthly license.

What Are the Benefits of Learning Data Science?

Your path to learning data science tools and languages depends heavily on how and where you plan to use your knowledge and skills. A Data Scientist or Data Analyst will need comprehensive training in math, computer science, probability, and statistics. However, a Back End Developer may need only a few tools applicable to data science, like Python and Django REST.

The most significant benefit to learning data science skills and tools is the breadth of their applicability. A top programming language like Python will be crucial in fields like machine learning (ML) or artificial intelligence (AI). At the same time, data visualization tools like Tableau can be essential for everyone from Business Analysts to SQL Server Developers.

Whether you plan a role as a Cybersecurity Analyst, a Machine Learning Engineer, or a Business IT Analyst, data science skills will prepare you to analyze information, gain insights, offer conclusions, and even make predictions.

Read more about why you should learn data science.

Data Science Careers

Data science cuts across nearly every industry, with professions in manufacturing, retail, government, and cybersecurity, to name a few. Whereas Data Scientists use their knowledge and skills in one way, Marketing Analysts may use theirs in quite another.

Banking services, web development, and healthcare are among the top sectors where data science is more critical than ever. The following points emphasize how they use data science in vastly different business models.

• Banking, Financial Services, & Insurance (BFSI) - The BFSI sector covers everything from Software-as-a-Service (SaaS) to auto insurance, credit reporting, and wealth management services. Cybersecurity and identity theft also figure largely into this picture, and data science is essential to all these functions.

• Web and application development - The rise of data science means artificial intelligence and machine learning have become more crucial than ever. Data Scientists and Data Analysts often collaborate with Web and App Developers, and when a new app goes public, data on its popularity or efficacy requires analysis. As a result, data science is now a critical part of the development life cycle.

• Health & Wellness - Data science can help doctors fight disease through a specialty called predictive medicine. Machine learning algorithms help with everything from imaging studies to clinical trials and even with predicting which drugs will (or will not) most likely prove effective. Big data benefits big pharma in a host of new ways.

How to Learn Data Science

Live data science classes, either in-person or online, are the most popular options for learning this essential programming language. You can find in-person data science programs near you using Noble Desktop’s Classes Near Me search tool. Check out the Data Science Certificate for training in Python and SQL or the Data Analytics Certificate to learn Python machine learning and Tableau. For virtual live training options, look for the best course for your goals, as you can take it from anywhere. Online live data science courses include a FinTech Bootcamp and Python Data Science & Machine Learning Bootcamp.

On-demand or self-paced data science courses are also available, although they aren’t nearly as thorough as live bootcamps or certificate programs. The Get Started in Data Science video tutorial from Noble Desktop provides a free two-hour introduction, while course providers like Udacity or Skillsoft offer training with your paid subscription to their platforms. Other on-demand alternatives require payment.

Noble offers additional free seminars on data science, along with blog posts and tutorials. You’ll find samples in the data science section of the Learn Hub. Their website's Free Seminars page hosts an Intro to Python Fundamentals seminar. And check out their YouTube page for a playlist on Python, Data Science, and SQL.

Read the full guide on how to learn Data Science.

A Brief History of Data Science

Data science combines computer programming with probability and statistics. Data Scientists and other professionals analyze data and use it to provide solutions for entities as diverse as multinational corporations, laboratories, and governmental agencies.

The terms data science and data analysis emerged in the 1960s out of the discipline of statistical analysis. In the 1970s, some scientists began using the term data science as an alternative to the term computer science. By the 1990s, however, more and more professionals agreed that computer processes like data mining and machine learning fall into the data science category.

Over time, the term’s meaning has changed considerably, and each application led to disagreement among scientists and scholars alike. Some credit Purdue University professor William Swain Cleveland II with changing the name of the field of statistics to data science in his paper “Data Science: An Action Plan for Expanding the Technical Areas of the Field of Statistics.” FiveThirtyEight founder Nate Silver, arguably America’s most famous statistician, agrees.

By 2003, the term data science found acceptance in the scientific community through the then-new Journal of Data Science. Their expansive definition included “almost everything that has something to do with data: collecting, analyzing, modeling, [and] most importantly, its applications.” 

However contentious the term’s origins and applications, today’s professionals generally agree that data science is an interdisciplinary field. Data science in the digital age encompasses a host of skills, including:

• Artificial Intelligence (AI)
• Computer Science
• Data Extraction
• Data Visualization
• Data Wrangling
• Deep Learning
• Machine Learning (ML)
• Probability
• Python Programming
• Software Engineering
• Statistics

While it may sound incredibly complex or overwhelming, the training needed for disciplines within the broad data science category differs among various roles. Machine Learning Engineers require some skills not needed by Data Analysts, and vice versa. Consider a program like Noble Desktop’s Data Science Certificate or Python Data Science Machine Learning Bootcamp for training that encompasses Python programming, machine learning, and automation.

Comparable Fields

It may sound like a cliché, but it’s true: there’s nothing like data science. Nearly every field today has a massive demand for data science professionals with expertise. Manufacturing, healthcare, finance, energy, and even sports are all sectors where data science can help drive decisions that save money, grow the business, and improve customer service.

A brief list of data science roles tells the story. Consider the following titles:

• Business Analyst
• Cloud Architect
• Cloud Engineer
• Data Analyst
• Data Engineer
• Data Scientist
• Database Administrator
• Enterprise Architect
• Financial Analyst
• Machine Learning Engineer
• Product Manager
• Software Developer

Another 20 titles could apply to data science engineers, thanks to terms like artificial intelligence, machine learning, and cloud-based roles. In small firms, a Business Analyst or Data Analyst might be tasked with all data analysis and thus all related recommendations for C-suite executives. On the other hand, in a multinational corporation, a Data Analyst might be a small part of a team of Data Scientists, Machine Learning Engineers, and Software Engineers.

One general field that can be considered comparable to data science: management. Why? The answer is simpler than you might expect. However, it has nothing to do with data science or management skills. Instead, it’s all about the sector. Other than sole proprietorships, virtually every company requires one or more managers. But training for an ecommerce Product Manager for a retail chain will look radically different from training for a Software Project Manager in a telecommunications firm.

What differentiates one management pro’s resume from another’s may be as simple as experience in that field. One of the most important reasons companies can promote current employees to management positions is their knowledge of that business within that particular sector. If you’re a Data Analyst, a Data Analytics Project Manager position at your current company may be well within reach.

Consider professional certifications as a final factor. A Data Scientist’s resume might benefit from adding the Senior Data Scientist credential from the Data Science Council of America DASCA). But Product Managers and Project Managers might want to qualify for Project Management Professional (PMP) or Certified Associate in Project Management (CAPM) certification. You can find training for these project management certification exams through Noble Desktop bootcamp programs.

Learn Data Science with Hands-on Training at Noble Desktop

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

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

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

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

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

Key Insights

• Data science combines a range of disciplines like math, programming, and artificial intelligence (AI).

• The data science and data analysis fields emerged out of the discipline of statistical analysis in the 1960s.
• Top data science and data analytics positions include:
• Business Analyst
• Cloud Architect
• Data Analyst
• Data Scientist
• Enterprise Architect
• Financial Analyst
• Machine Learning Engineer
• The cost of learning data science depends on the specific role. Training for a financial analysis position will differ from training for a data engineering position.
• Many data science pros qualify for entry-level positions through immersive bootcamps or certificate programs. These courses provide in-depth training in a concentrated timeframe.
• You can get comprehensive data science training through an in-person or live online course with Noble Desktop.