Want to Become a Data Scientist? Here’s Everything You Need to Know

Ever wonder how Netflix knows exactly what show to recommend next? Or how Amazon predicts what you might want to buy?

Behind these everyday experiences are data scientists – experts who transform complex data into valuable insights that help organizations make smarter decisions.

We’re living in an exciting time where data touches almost every part of our lives. From the apps on your phone to the research fighting climate change, data helps us understand our world better and answer pressing questions. Think about it: every time you stream music, order food, or check social media, you’re creating data that tells a story about what people want and need.

If you’re passionate about problem-solving, computer science, math, or programming, you can turn these skills into a career as a data scientist.

If the idea of using data to make a real difference in the world fascinates you—this guide is for you. We’ll walk you through everything you need to know about becoming a data scientist—from the hard and soft skills you’ll need to develop and the education paths you can take to what your day-to-day work might look like and how much you could earn.

Read on to learn more about data scientists, their responsibilities, how they contribute to organizations, and more.

What is a Data Scientist?

Think of a data scientist much like a digital detective. Data serves as clues, and data scientists use it to answer questions and make recommendations.

Companies hire data scientists to help them make more informed decisions, understand their customers or potential customers better, run operations more efficiently, or just come up with new ideas.

What makes data scientists unique is their ability to blend different skills together. One day, they might write code to analyze customer behavior; the next day, they might create visual presentations to help company leaders understand market trends; and the following week, they might build AI models to predict future sales.

Take this real-world example, for instance. Think, for instance, of how many times you have streamed your favorite show, and that suggested ‘because you watched this, you might like…’ All in all, that ‘s data science in action. When a healthcare system predicts which patients are most likely to need preventive care or if a city plans better schedules for buses and trains, data scientists are making it happen.

Data Science Adapting to Change

As technology changes, the role of data scientists changes, too. As artificial intelligence and machine learning have soared in popularity, data scientists remain at the forefront of innovation, helping shape how technology will be used in the future. They’re not just analyzing past events—they’re helping organizations (and society) prepare for what’s coming next.

Whether helping businesses grow, contributing to scientific discoveries, or solving social challenges, data scientists have one thing in common: they use data to make an impact.

What Are the Responsibilities of a Data Scientist?

The day-to-day work of a data scientist involves collecting and validating large datasets from various sources. This ensures the quality and reliability of the information they’ll be working with. Data scientists typically combine several skills. They often possess a strong foundation in statistics, programming, and business. Forecasting future trends and outcomes is another key component of the job.

Some typical responsibilities data scientists handle at work include, but are not limited to:

• Collecting, cleaning, and authenticating datasets
• Developing predictive models to forecast trends and outcomes
• Creating and installing machine learning models and algorithms
• Finding interesting ways to visually present data
• Converting complex data into actionable insights
• Monitoring and improving data models and performance

Data scientists are genuine problem solvers who examine what happened in the past and, in turn, focus on what might happen in the future.

While data analysts look at current and historical data to explain what happened and why, data scientists take it a step further by creating predictive models and using advanced techniques like machine learning to uncover deeper insights. You’ll find data scientists building models to predict customer behavior, developing recommendation systems, or detecting unusual patterns that could signal fraud.

Behind the scenes, data engineers and architects lay the groundwork that makes this analysis possible. Data engineers build and maintain the systems that collect and process data – they’re like the construction crew, ensuring everything runs smoothly. Data architects take an even broader view, designing the blueprint for how an organization’s data systems work together, much like city planners mapping out new developments.

These roles often collaborate closely. For example, when a company wants to improve its customer experience, the data architect designs the plan for collecting and storing customer data, the data engineer builds the systems to gather this information, the data analyst examines current customer behavior patterns, and the data scientist creates models to predict future trends and personalize recommendations.

While each role has its specialty, there’s room to grow and evolve between positions as you develop new skills and discover your interests. Many start as data analysts and grow into other roles, developing new capabilities and finding their niche in the data world.

Essential Skills for Data Scientists

Programming Skills

The technical foundation of a data scientist’s skill set is broad and deep. Proficiency in programming languages such as Python and SQL forms the core of their technical abilities, enabling them to manipulate and analyze data effectively. R programming has also become important, given its distinctive statistical computing capabilities and specific data analysis and visualization packages complementary to Python’s strengths. Together, these languages form the cornerstone of modern data science work.

While SQL remains fundamental to database management, the possibilities of working in TensorFlow and PyTorch open many doors in advanced machine learning. The field keeps moving fast, and natural language processing is very much in high demand.

Data Visualization

Mastering data visualization with tools like Tableau and Power BI helps data scientists communicate findings to colleagues in other departments. Knowledge of open-source frameworks like Apache Hadoop and Apache Spark is increasingly important as organizations deal with larger datasets.

Database management skills enable them to work with various data storage systems, while version control systems like Git help them maintain and collaborate on code efficiently.

Natural Language Processing and Machine Learning

According to 365 Data, natural language processing—the technology behind chatbots and AI assistants like Siri and Alexa—has seen its demand skyrocket nearly fourfold in job postings, jumping from 5% to 19% in the past year alone. This boom proves how quickly the data science field is embracing AI-powered technologies.

Mathematics and Statistics

Most good data scientists have a foundation in mathematics. It’s valuable for data scientists to have a strong foundation in algebra in order to deal with matrices and vectors in ML algorithms. Calculus is helpful so that they can optimize data models and know the mathematics of deep learning. Probability theory and statistical inference are key to predicting and interpreting uncertainty in their analyses. An understanding of experimental design allows them to design experiments and studies that produce useful, practical results.

Soft Skills

While technical expertise is crucial, top data scientists also possess soft skills like communication, emotional intelligence, and business acumen. Strong problem-solving abilities allow data scientists to address challenges in a creative and organized fashion. Critical thinking helps them evaluate problems from multiple angles and develop innovative solutions. Excellence in communication enables them to explain complex technical concepts to non-technical stakeholders and present findings clearly and convincingly.

Business acumen helps them understand how their work impacts organizational goals and decision-making. Project management capabilities allow them to handle multiple projects efficiently and meet deadlines. Strong collaboration skills enable them to work effectively with various teams. Perhaps most importantly, intellectual curiosity drives them to learn and adapt continuously as technology and methodologies evolve.

Where Do Data Scientists Work?

If you choose a career in data science, there’s a good chance you’ll work in a traditional office setting, hybrid, or remotely. It all depends on the preferences of the organization you end up working for.

Data scientists work across all kinds of industries, not just tech companies. They help:

• Sports teams examine draft picks and player performance
• Environmental organizations track climate change patterns
• Retailers understand shopping habits and revamp inventory management and ordering
• Government agencies analyze policies and make improvements to public service programs
• Medical researchers discover patterns in disease diagnoses and treatment
• Financial institutions detect fraud and assess risk
• Entertainment companies predict which shows will be hits

Let’s look at some of the above examples a bit more closely. Financial institutions rely on data scientists for risk assessment, fraud detection, and market analysis. Retail companies utilize their expertise to understand customer behavior, optimize inventory management, and personalize marketing strategies. Manufacturing firms employ data scientists to enhance process efficiency and quality control through predictive maintenance and production optimization.

Government agencies leverage data science for policy analysis and public service improvement while consulting firms employ data scientists to provide specialized analytical services to their clients. Data scientists typically work on cross-functional teams, working closely with business managers, software developers, and others to enhance data-informed decision-making.

Data Scientist Salaries and Job Opportunity Forecasts

According to the U.S. Bureau of Labor Statistics (BLS), in 2023, the median data scientist’s yearly salary was $108,020, or $51.93 per hour. If you’re considering a career in data science, the future looks even brighter. According to the BLS, the data science field is expected to grow 36% through 2033. With approximately 13,500 job openings yearly, data scientists can expect continued demand for this highly sought-after skillset. This growth rate is significantly faster than the 4% average for all occupations. This disparity reflects the increasing importance of data-driven decision-making across industries.

365 Data supports these enticing claims and presents an even more promising outlook for years to come. After analyzing 1,000 job postings this year, 365 Data estimates a data scientist’s average yearly salary range is between $160,000 and $200,000.

Several factors can influence a data scientist’s earning potential. Work and internship experience, degrees and academic background, level of technical expertise, and the industry in which one chooses to work all contribute to a data scientist’s earning potential.

These figures show that organizations across every industry recognize the value of skilled data scientists. As companies continue to realize that their data holds the key to future success, they’re ready to reward the people who can unlock those insights.

How to Become a Data Scientist

One way to explore the exciting field of data science is through Eastern Connecticut State University’s online Master of Science in Applied Data Science program. This program is specifically designed to equip you with the advanced skills that today’s employers are looking for—from machine learning and statistical analysis to data visualization and predictive modeling.

The online format means you can continue working while you build your expertise, applying what you learn in real-time to solve real-world problems.

The program is designed with busy professionals in mind:

• 100% online
• Self-paced learning within 7-week courses
• Complete the program in as little as 12 months
• 30 credits total (6 credits per 7-week session)
• No set sign-on times—complete assignments on your schedule
• Students can expect to spend 10-15 hours on coursework per week per course

Instead of traditional lectures with fixed schedules, expert faculty have created comprehensive, video-based, self-paced coursework that you can complete whenever convenient. This approach maximizes flexibility for working professionals while maintaining the high academic standards that have earned Eastern its place among the Top Tier of Regional Universities in the North by U.S. News and World Report.

Gaining Practical Experience

As you progress through your studies at Eastern, it’s important to complement your academic learning with hands-on experience. Here’s how you can start building your portfolio:

• Apply what you’ve learned in your coursework to real-world situations
• Join online competitions
• Develop your own personal data projects
• Contribute to open-source initiatives

The path to becoming a data scientist combines education, practical experience, and continuous learning. Eastern’s program provides the foundation and flexibility you need to succeed while fitting into your busy life and budget.

Ready to get started? New cohorts start every seven weeks, and financial aid options, including federal and state aid, plus military benefits, are available to qualified students. Apply online today to join Eastern’s nationally recognized program.