How to Become a Data Scientist With an Online Education

Data drives decision-making in almost all modern industries and the data scientist's role is very important. Data scientists extract insights from data and handle vast amounts of data, known as big data, that can be used to make effective decisions and discoveries. As a result, they’re highly in demand, with no letup in sight. The U.S. Bureau of Labor Statistics predicts that data scientist jobs will grow by 36% by 2033–much faster than other occupations.¹

Data scientists need a combination of technical, mathematical and communication skills to succeed in the competitive data science field. Their job responsibilities include communicating with stakeholders, managing teams and projects, designing analytics models to solve problems and developing strategies based on data-driven insights.²

This article will explore how you can become a data scientist through an online education, including developing the necessary skills through online courses, and share career tips for getting started.

Students have many reasons for choosing to learn data science online. Flexibility and affordability often top the list. Online education programs that go beyond undergraduate degree skills and/or skills learned in data science boot camps allow you to learn at your own pace, which is ideal for older or nontraditional students who may have family or work obligations that make attending school in person difficult.³

Today’s educational platforms offer interactive and immersive learning experiences powered by advanced technology, giving students the opportunity to engage in hands-on projects and develop practical skills. This type of learning is often comparable to, if not better than, traditional classroom environments.³

In many online programs, you’ll gain experience with data manipulation, programming languages like Python and SQL and data visualization tools. The availability of these tools means students can perform exploratory data analysis and develop machine-learning models that mirror real-world applications.

One of the most significant advantages of studying data science online is cost savings. Online master’s programs can prepare you for a career in data science jobs at a lower overall cost than many in-person programs. By eliminating commuting expenses and reducing the need for on-campus fees, online programs make education more accessible. Additionally, some universities offer year-round courses and accelerated learning paths, allowing you to complete your degree faster, which can reduce your total tuition costs. To further manage expenses, inquire about credit transfer policies or check with your employer about tuition reimbursement options.

Online programs provide unique networking opportunities that extend beyond your geographic area.³ Through virtual forum discussions, live Q&A sessions and collaborative breakout groups, online students often connect more comfortably with peers and instructors. These networks are valuable resources for professional development, helping you build relationships that may lead to job opportunities or mentorships in data science fields.

Data scientists need a wide range of technical and soft skills to succeed in complex roles. Technical requirements for data scientist roles include the ability to write code using programming languages such as Python, R and Structured Query Language (SQL). Certain online master's degree programs, like the William & Mary Online Master of Science in Business Analytics taught through the Mason School of Business gets you up to speed with prerequisites covering these much-needed skills.

You’ll also need to be able to prepare data for analysis by finding, gathering, cleaning, processing and modeling it. To effectively analyze data, you’ll have to draw on your understanding of math and statistics.⁴

Many modern models are built on artificial intelligence and machine learning. You’ll use these technologies to train and deploy models for predictive and prescriptive analytics.⁴ You’ll also need non-technical skills such as critical thinking to figure out what you need to know and what types and sources of data will provide the best answers.⁴

Data scientists need top-notch communication skills to understand stakeholders’ needs and convey the results of their data investigations. In this role, you’ll prepare reports that underscore the need for action and explain how you reached your conclusions. Because you’ll need to proactively identify opportunities and challenges, you’ll also need to be an effective problem-solver and leverage the latest advanced data science techniques as a data scientist.⁴

In addition to earning an advanced degree, creating an outstanding portfolio will help you get noticed by employers when you’re applying for a job as a data scientist. However, don’t view your data science portfolio as a tool to use only when you’re applying for a job. Keep your data science portfolio up to date as a way to document your growth as your career progresses.⁵

You can set up your portfolio on GitHub, a blogging platform or on your own website. Include projects that demonstrate your skills and interests. You can choose data visualization projects, machine learning case studies, analysis reports and tutorials to showcase your expertise. Showcase only your best work, particularly early in your career. Quality is more important than quantity in your portfolio.⁵

Although you need a bachelor’s degree as a minimum data science qualification, you’ll be much more competitive with a master’s degree. You can also use your advanced degree as an opportunity to develop an area of specialization, such as machine learning or business analytics.⁶

Consider what career path you want to take and plan accordingly. You’ll have many avenues available. From an entry-level position as a junior data scientist, you can progress to a specialized role as a machine learning engineer or a business analyst. From there, you can pursue senior-level roles such as director of data engineering or senior business analyst. Deciding which route you want to take can help you succeed faster, but you can also change course if you decide you’re interested in another specialization.⁶

In addition to creating an outstanding portfolio, you should brush up on your interview skills before you start applying for jobs. You’ll have to go through several phases and interviews to land your dream job, so it’s best to be prepared. Typically, these phases include an initial screening, a data challenge, technical interviews and experience interviews. Each company will have its own processes for conducting these interviews and screens.⁷

You can prepare by brushing up on the technical skills you’re likely to be tested on, including coding, machine learning, probability and statistics, data challenges and case interviews. Though you won’t know the exact questions ahead of time, understanding the general types will help you prepare. Don’t forget to prepare for questions regarding your soft skills as well. Rehearse your answers to demonstrate your communication, problem-solving and conflict-resolution skills.⁷

The field of data science offers a variety of career paths, each requiring distinct skills and expertise that potential employers are seeking to add to their teams.

By understanding these roles, aspiring data scientists can better align their education and experiences with their career aspirations. Below are some of the key positions in the data science landscape:

As a data scientist, you'll be responsible for analyzing complex datasets to extract actionable insights that inform business decisions. Key skills include:

• Proficiency in programming languages such as Python and R
• Strong statistical knowledge and mathematical modeling.
• The ability to interpret data findings and communicate results to stakeholders
• Experience with machine learning algorithms and data visualization tools

Data engineers focus on the architecture and infrastructure required for data generation, management and processing. They ensure that data pipelines are efficient and scalable. Important skills include:

• Expertise in data warehousing solutions and ETL (Extract, Transform, Load) processes
• Proficiency in programming languages like SQL, Python or Scala
• Experience with big data technologies such as Apache Hadoop or Spark
• Knowledge of cloud platforms like AWS or Google Cloud for data storage and processing

A machine learning engineer designs and implements machine learning models that enable predictive analytics and automation. Skills essential for this role include:

• Strong coding skills in Python or Java, specifically for machine learning frameworks like TensorFlow or PyTorch
• A solid understanding of algorithms, data structures and software engineering principles
• Experience with model training and evaluation techniques
• Familiarity with both supervised and unsupervised learning methodologies

Data analysts play a crucial role in interpreting existing data and helping organizations make data-driven decisions. Skills needed include:

• Proficiency in data analysis tools like Excel, Tableau or Power BI
• Strong analytical and critical thinking skills to derive insights from data
• Ability to communicate findings clearly to non-technical stakeholders
• Basic knowledge of statistical analysis and visualization techniques

This role focuses on transforming data into strategic insights that drive business growth. Business intelligence analysts work closely with management teams to shape data strategies. Key skills include:

• Expertise in business intelligence tools like Tableau or Qlik
• Understanding of data modeling and database management
• Ability to present complex data in an understandable format
• Strong problem-solving skills and a business-oriented mindset

As organizations increasingly rely on data, ensuring its integrity and security becomes essential. Data governance specialists manage policies and procedures related to data management. Skills required include:

• Knowledge of data governance frameworks and compliance regulations (e.g., GDPR)
• Strong communication skills to collaborate with various teams
• Ability to implement data quality and security protocols
• Familiarity with data management tools and technologies

By exploring various career paths in data science, aspiring data scientists can pinpoint the role that best aligns with their interests and skills. Whether your goal is to become a data scientist, data engineer or machine learning engineer, understanding the data scientist requirements and expectations for each position will inform your education and training, paving the way for success in this dynamic field.
As advancements in AI and machine learning unfold, new and exciting opportunities arise, including specialized areas such as ethical data science, MLOps and industry-specific roles. To remain competitive, it's crucial to embrace continuous learning, adapt to emerging technologies and cultivate a robust professional network within the data science community.

An Online Master of Science in Business Analytics from William & Mary will help you develop the analytical skills you need to source, process and identify patterns, trends and correlations in data. Our top-ranked program is taught by industry experts, and you can learn from the comfort and convenience of your own home, at your own pace. Get the degree you need to reach your career goals even if you can’t attend in-person classes.

You can earn your MSBA in as little as 15 months, and you can get a Foundations in Business Analytics Certificate in just five. You don’t need to put your career on hold for years to start seeing results. Contact one of our admissions outreach advisors today to apply and take the next step to advance your career.