How Difficult is it to Learn Data Science?

Are you curious about learning data science but worried it might be too hard? Of course, the difficulty that comes with learning a new skill is somewhat subjective. The challenges of learning data science depend on factors like how you plan to use it and the field or sector in which you work.

No matter your current schedule or comfort level with data science, plenty of tools are available to help make learning more manageable than you might think.

What is Data Science?

Data science is a broad field that encompasses the disciplines of mathematics, computer programming, and artificial intelligence (AI). Data professionals such as Data Scientists and Data Analysts use advanced techniques like machine learning algorithms to find patterns in a vast amount of information. This process can provide actionable insights to stakeholders, from Product Managers to C-suite executives.

Today nearly every sector requires data science expertise, whether public or private. Among the top sectors where data science is critical are health and wellness, retail, web and application development, banking and finance, and governmental agencies. The field continues to project dramatic growth over the next decade; Glassdoor even listed Data Scientist as number three in its 50 Best Jobs in America in 2022.

Read more about what data science is and why you should learn it. 

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.

What Are the Most Challenging Parts of Learning Data Science?

The built-in challenges of studying data science include a constant demand for innovation, industry-specific tools, and the need to combine hard and soft skills. Consider the following:

• Data science innovations - In the past decade alone, data science has grown thanks to innovations in artificial intelligence, machine learning, and deep learning. As the field grows, expect an even greater pace of innovation.

• Tools for industries - Data science pros in finance may use vastly different tools than those in healthcare, retail, or marketing. Not every industry requires specific tools: some roles require Power BI training, whereas others require Tableau skills. Know your industry.

• It’s all about the data - Among the biggest challenges for Data Scientists are mining data, understanding what it all means, and communicating the results of their findings to key stakeholders. While such challenges may not face students at the same level, it’s essential to understand that they are part of the process in real-world situations.

How Does Learning Data Science Compare to Other Fields?

Learning data science means different things to different people. For example, some students want to learn Python as part of a more comprehensive data science curriculum. Others, by contrast, will focus on Python or R only because they enroll in a back end development course. Programming languages like these apply to numerous fields, not just data science.

Data science can be challenging to learn in-depth: experts estimate around six to twelve months to master data science fundamentals, but expertise in the field takes years. For that reason, students interested in data science for its own sake often choose immersive bootcamps or certificate programs.

One area comparable to data science is management: you’ll need expertise in your sector to become a manager. Data science tools and skills vary among different sectors in much the same way. 

For more on tools and skills among different roles, check out Noble Desktop’s Learning Resources.

Making the Process Easier

Enrolling in a full-length training program is one of the most common ways data science novices make learning easier. Many students begin to familiarize themselves through a free online data science tutorial or seminar. A low- or no-cost class like this can be the perfect entry point, especially if you’re new to the discipline.

Successful data science students typically report enrollment in a comprehensive bootcamp or certificate program as the next logical step. These programs run anywhere between 30 to 126 hours and cover the topic from fundamentals to intermediate skill sets.

Other Considerations

You might wonder why data science is worth learning today despite the inevitable challenges. Consider the following points:

• Glassdoor listed Data Scientist at number three on its 50 Best Jobs in America for 2022.
• According to the U.S. Bureau of Labor Statistics, states with the highest employment level of Data Scientists include some of the biggest: New York, Texas, and California.
• Data Scientists and Data Analysts will continue to be in high demand and short supply, with higher-than-average pay.

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.