Data Science vs Statistics: How do they compare?

Ekene Eze · Improve this Guide

Data science and statistics are promising career paths that offer great opportunities to work with data to solve problems and guide decision-making in various industries. But how do you determine which one is a good career for you? If you’re new to these fields, learning about their core concepts and career opportunities can help you get started. If you are a student or professional exploring data-driven careers, this guide will help clear up confusion and determine the path that aligns with your goals.

When choosing between the two, think about the types of problems you like to solve. If you’re interested in understanding why and how an issue occurred, uncovering insights from raw data, and building models to predict future outcomes, data science may be the right fit for you. On the other hand, if you prefer analyzing and interpreting quantitative data, identifying patterns, and making statistical predictions, statistics could be the better path.

At first, data science and statistics might seem interchangeable. But as we dig deeper, you’ll see how they differ. Statistics provides the foundation by defining methods, probabilities, and models. Data science builds on that foundation to extract insights from complex data and solve real-world problems.

In this guide, I’ll explain where data science and statistics connect and differ. You’ll learn more about the skills and tools necessary for each role and gain more clarity into the career prospects and opportunities they offer. By the time you finish, you’ll have a clear vision of your ideal role and the confidence to start shaping your future.

The following table provides a comparison of data science and statistics.

	Data Science	Statistics
Key role	Uses statistics, computer science, and domain expertise to interpret complex datasets.	Uses statistical methods to analyze and make predictions for small, structured datasets.
Data processing	Handles large and complex datasets (big data).	Works with smaller datasets, emphasizes sampling techniques.
Tools	Python, R, SQL, Hadoop, and TensorFlow.	Statistical software like SAS, R, SPSS, and mathematical models.
Skills	Machine learning knowledge, programming skills, data mining, data visualization, business intelligence, and predictive analytics.	Proficient in probability, mathematical theory, statistical modeling, data analysis, and survey design.
Career paths	Data scientist, machine learning engineer, data analysts.	Statistician, biostatistician, research analyst.
Salary range	For data science career options, salary ranges from $81,273 to $161,715 per year.	For statistics career trajectories, salary ranges from $75,789 to $101,789 per year.

Data science vs. statistics: What sets them apart?

Data science and statistics are two different areas of analytics, each with its role and methodology. Both use statistical methods, but the key difference lies in their application and purpose.

Data science is a blend of statistics, computer science, and domain expertise. It focuses on analyzing large, complex datasets to uncover trends, predict outcomes, and drive business decisions. As a data scientist, you’ll use machine learning, programming, and big data tools to solve real-world problems.

Statistics, on the other hand, is rooted in mathematical theory and inference. As a statistician, you’ll design experiments, develop models, and test hypotheses to draw conclusions from structured datasets. You’ll work in industries like finance, healthcare, government, and research.

The key difference is application. Statistics is theoretical and inference-driven, while data science applies statistical techniques in a computational, business-focused way.

To get a clearer picture of both roles, let’s look at the day-to-day tasks involved in each field.

Job roles and responsibilities

Both data science and statistics rely on statistical methods and tools, but your day-to-day activities in each field will differ significantly. As a statistician, you focus on collecting, analyzing, and interpreting data to uncover patterns. As a data scientist, you go beyond that by leveraging machine learning, big data technologies, and programming to extract actionable insights.

Now, let’s take a closer look at their specific roles and responsibilities through a shared case study.

Key responsibilities of data scientists

As a data scientist, you’ll extract actionable insights from raw data to solve complex business problems. For example, imagine a pharmaceutical company is developing a new drug. As a data scientist, you might be analyzing clinical trial data to predict treatment efficacy for different patient groups. By applying machine learning models, you can find hidden patterns in patient responses, segment populations, and optimize treatment recommendations.

The following are some of your day-to-day responsibilities as a data scientist:

• Discovery: Understanding patient demographics, health conditions, and genetic factors to segment target groups.
• Data collection: Gathering patient records, clinical trial results, and external medical research data for analysis.
• Generate insights: Finding trends in treatment responses using advanced analytics and predictive modeling.
• Model large data sets: Using machine learning to forecast drug effectiveness across different conditions.
• Design algorithms: Building AI-driven models to personalize medication plans and optimize trial outcomes.

Key responsibilities of a statistician

As a statistician, your focus is on designing experiments, analyzing data and applying statistical methods to evaluate results.

Using the same pharmaceutical company case, as a statistician, you might be responsible for ensuring the clinical trial design is statistically sound and the results are statistically valid before regulatory approval. You would be conducting hypothesis testing, confidence interval analysis, and regression models to confirm that the drug is effective and safe.

Your day-to-day responsibilities might include:

• Designing surveys and experiments: Design a clinical trial or questionnaires for the new drug to verify it is statistically sound
• Testing hypotheses: Choosing the right statistical test based on the data type.
• Analyzing structured datasets: Summarizing data, analyzing, and modeling relationships between variables to predict outcomes.
• Data reporting: Using charts, graphs, and other visual aids to report complex information effectively.
• Business recommendations: Provide valuable insight and production strategies based on trial results.

Let’s dig into a few tools and skills you must be proficient in for your selected job role.

Essential skills and tools

Whether you are planning to choose data science or statistics as your career, it is important to know the right tools and essential skills for future challenges.

Both data science and statistics require a strong foundation in mathematics and analytical thinking skills. Professionals in these fields often pursue advanced degrees, such as a Master’s or Ph.D., to enhance their expertise. Let’s examine a few key skills and tools essential for each job profile.

Skills and tools of a data scientist

Handling complex datasets, including both unstructured and structured data, requires a data scientist to possess strong technical and problem-solving skills. You should have the following skills for a data scientist job profile:

• Programming Skills: To perform data science tasks smoothly, you must be proficient in Python, SQL, Java, and R programming languages. Additionally, you must be good at statistical analysis and mathematics.
• Machine learning knowledge: You should be able to choose and implement appropriate algorithms, such as linear regression, logistic regression, decision trees, random forests, and neural networks, based on the specific problem and dataset.
• Data mining: Knowledge of techniques like data cleaning, preprocessing, feature engineering, and pattern recognition are essential to extracting meaningful insights from big data.
• Data visualization: Tools like Tableau and Power BI can help create compelling charts and graphs for your big data. You must know how to use these tools effectively to communicate data insights.
• Business intelligence: Understanding business contexts and translating data insights into actionable business decisions is key. You must have strong communication skills and domain knowledge.

Hadoop, Docker, TensorFlow, GitHub/Git, SQL, and Tableau are important for a data science job profile. Interviewers often prioritize candidates with educational qualifications in computer science, programming, and practical applications of data analysis techniques.

Skills and tools of a statistician

A statistician’s main responsibility is to analyze data to predict future events or trends. To achieve this goal, you must excel in the following skills and tools:

• Mathematical theory: A strong understanding of mathematical concepts like linear algebra, calculus, etc., is essential to measure quantitative data.
• Probability: Proficiency in probability theory is essential for understanding and modeling uncertainty in data.
• Statistical modeling: Experience with various statistical models, such as time series analysis and linear regression, is also valuable.
• Survey design: Knowledge of designing questionnaires or sampling techniques is necessary to collect information from a group of people.

R, SAS, SPSS, and Excel are a few popular tools you should master to perform statistical analysis and generate graphics. Also, candidates with qualifications in advanced statistical modeling, mathematical and statistical theory, and research methodologies have a better chance of getting selected.

Let’s move forward to different career options available under data science and statistics. Learning about their salary ranges will provide valuable insight into earning potential, helping you make an informed career decision. By comparing salaries, you can assess financial stability, ensuring that your chosen path aligns with your long-term financial goals.

Career prospects for both fields

Data science is experiencing explosive growth across numerous industries, whereas the demand for statisticians remains strong and consistent. Let’s explore the various job roles available in both fields and their salary ranges.

Data science career paths and salary insights

You can choose from the following careers if you are planning to proceed with data science:

• Data scientist
• Machine learning engineer
• Data analyst

Data scientist: A career in data science offers opportunities in various industries, including finance, healthcare, and technology. As a data scientist, you’ll tackle unique problems, using statistical modeling and machine learning to drive business decisions and outcomes. You need to have a deep understanding of data manipulation and how data relates to the real world.

Many data scientists use machine learning algorithms to build predictive models, identify trends, and make data-driven decisions. Strong communication skills and expertise in statistical modeling are essential for success in this field. You may start as a junior data scientist and improve your knowledge to lead as Head of Data Science over time.

In the United States, the average salary of a data scientist is $123,069 per year.

Machine learning engineer: A career as a machine learning engineer involves designing, building, and optimizing machine learning models used in various applications, from recommendation systems to fraud detection. Professionals in this field work on improving machine learning frameworks, monitoring model performance, and solving complex computational problems. To succeed, you need expertise in advanced machine learning, cloud computing, and the software development lifecycle (SDLC).

Machine learning engineers are in high demand across finance, healthcare, e-commerce, and technology industries. You can start by working on implementing basic machine learning models; as you improve your skills and gain more knowledge, you can work towards a role overseeing ML infrastructure, defining best practices, and leading technical teams.

In the United States, the average salary for a machine learning engineer is $161,715 per year.

Data analyst: Data analysts play a crucial role in helping businesses make informed decisions by collecting, filtering, and analyzing data to answer specific business questions. You can use data visualization tools to present insights clearly to stakeholders.

A career as a data analyst offers opportunities across various industries, including marketing, finance, and healthcare, making it a versatile and in-demand profession. The data analyst job profile includes various positions and levels, from basic reporting and performing deeper data analysis to making data-driven decisions for an organization.

According to Indeed, the average salary for a data analyst job profile in the United States is $81,273 annually.

Statistics career paths and salary insights

If you’re interested in statistics, here are a few job positions you can choose from:

• Statistician
• Biostatistician
• Research Analyst

Statistician: As a statistician, you will design and conduct statistical analysis, interpret data, and communicate findings to researchers and other stakeholders. Many statisticians work on research projects, clinical trials, and large-scale surveys, helping organizations make data-driven decisions.

There are different levels in the statistician job profile, from applying statistical techniques for data interpretation to leading statistical research and influencing policy decisions. Strong analytical skills and a solid understanding of statistical methods are essential for this career path.

According to Indeed, the average salary of a statistician in the United States is $89,126 per year.

Biostatistician: Biostatisticians apply statistical methods to biological and medical research, making significant contributions to the healthcare and pharmaceutical industries. If you are planning to pursue a career in this field, you will design and analyze clinical trials, ensuring the accuracy and reliability of biomedical research. This career path offers opportunities in hospitals, government agencies, research institutions, and pharmaceutical companies.

The average salary for a biostatistician is $101,789 per year in the United States.

Research analyst: In this role, you will use statistical and analytical methods to collect and interpret information to support business decisions. You will also identify trends, generate insights, and present detailed reports summarizing your research for stakeholders.

According to Indeed, the average salary for a research analyst in the United States is $75,789 per year.

Are data science and statistics interconnected?

A statistician uses statistical tools and mathematical models to analyze customer data and distinct customer segments with similar characteristics. This analysis will help stakeholders understand the factors responsible for customer churn rates. Data scientists, meanwhile, will collect big data and use machine learning models to perform predictive analysis.

Statistics answer questions like “Why did customer churn get high in the first quarter?” or “What is the reason behind the sudden downfall”? Data science, on the other hand, answers “How can this issue be fixed?”.

So yes, data science and statistics are deeply interconnected.

Statistics provides the foundation for analyzing data, such as finding patterns and trends. Data science professionals, on the other hand, create sophisticated models and present insights through data visualization. Data science is an extended version of statistics that uses statistical methods, scientific methods, machine learning, and artificial intelligence to manage unstructured or semi-structured data.

Data science further leverages predictive modeling and other advanced techniques to understand past events, predict future trends, identify potential risks, solve real-world problems, and develop improvement strategies.

Additionally, statistics doesn’t require computational skills but an understanding of basic mathematical models to analyze and predict results.

Next Step?

Choosing between data science and statistics can be a tough call. Hopefully, this guide has helped clarify which career path is the best for you. Once you’ve made a decision, the next step is to get started.

To help you with your journey, I encourage you to explore the comprehensive resources that delve deeper into the specifics of each path. Whether you are interested in programming and solving complex datasets or theoretical analysis and working with structured data, we have great resources for each career option.

You can apply for internships or join online courses to gain more experience or sharpen your knowledge. If you still have queries or doubts about these fields, join the Discord community to stay up-to-date!