How To Become a Data Scientist: Salary & Career Paths for 2025

Co-Founder and Chief Data Scientist

Data analysis and gathering insights make up the backbone of business intelligence, which empowers businesses to raise their performance in the process. For this reason, a projected 97.2% of organizations are geared to invest in big data and artificial intelligence by 2025. As the central analysts of big data, data scientists and their capabilities are more relevant than ever in today's business landscape.

This guide explores the steps on how to become a data scientist and the role's core responsibilities. To add value for aspirants, the article also imparts information on possible salaries, relevant skills, top certifications, and job outlook.

Key Things You Should Know About Becoming a Data Scientist in 2025

A data scientist is a professional who collects, processes, and analyzes large datasets to extract meaningful insights that drive decision-making and solve business challenges.
51% of data scientists in the United States have a bachelor's degree, 34% earned a master's degree, 13% obtained a doctorate degree, and 1% hold an associate degree.
Python is the most popular programming language for data science, with 90.6% of professionals leveraging it for data science tasks, followed by Structured Query Language (SQL) (53%), and R (38%).
The employment of these professionals is expected to rise by an impressive 36% from 2023 to 2033.
Data scientists in the United States receive an average annual salary of $119,040 and a median annual salary of $108,020.
The US states that provide the highest average annual salaries are Washington ($148,730), California ($140,490), Virginia ($139,080), New York ($134,830), and New Jersey ($134,140).

What does a data scientist do?
How do you become a data scientist in 2025?
What is the degree needed to become a data scientist?
What skills do data scientists need?
What are the types of software used by data scientists?
What are the programming languages used by data scientists?
What are the best certifications for data scientists?
What are common jobs for aspiring data scientists?
Which online master's programs offer the best value for data science?
What is the job outlook for data scientists?
How Can I Land My First Data Science Job?
What is the average salary of data scientists?
What are the biggest trends shaping data science currently?
How Can Online Learning Platforms Accelerate My Data Science Journey?
Other Things You Should About Becoming a Data Scientist

What does a data scientist do?

A data scientist is a professional who collects, processes, and analyzes large datasets to extract meaningful insights that drive decision-making and solve business challenges. They routinely conduct several activities, which include data cleaning, developing predictive models using machine learning algorithms, and visualizing results for stakeholders in a clear and actionable manner. Additionally, they collaborate with cross-functional teams, stay updated on emerging technologies, and ensure compliance with data privacy regulations to maximize the value of data-driven strategies.

These are part of their key responsibilities:

Data Collection & Cleaning: Data scientists begin by collecting raw data from various sources—databases, APIs, web scraping—and then refine it through cleaning and preprocessing to achieve accurate and consistent datasets.
Exploratory Data Analysis: They analyze datasets to identify patterns, trends, and correlations, helping organizations understand their data before building predictive models.
Machine Learning & AI: Using algorithms and statistical techniques, data scientists create models that can predict outcomes, automate decision-making, and optimize processes. You may want to learn more about how to become an AI trainer if artificial intelligence is something that greatly interests you.
Data Visualization & Communication: They translate complex data into easy-to-understand charts, graphs, and reports, allowing non-technical stakeholders to make informed decisions.
Business Problem Solving: By applying data-driven approaches, data scientists help businesses optimize marketing campaigns, streamline operations, improve customer experiences, and increase profitability. In this regard, you may want to know the difference between data science vs business analytics.

How do you become a data scientist in 2025?

A career in data science combines education, technical expertise, and practical experience to unlock opportunities in one of the most in-demand fields today. While there is no single path, following these key steps can help aspirants build a successful career in data science.

Obtain Foundational Education: Pursue a bachelor's degree in a quantitative field like computer science, statistics, or data science. Consider a master's for specialization in AI or machine learning.
Develop Core Technical Skills: Master programming languages (Python, R, SQL), machine learning, data wrangling, and visualization tools (Tableau, Power BI). Learners should update their knowledge of big data technologies (Hadoop, Spark). You can look into affordable online CompTIA A bootcamps to develop essential IT skills.
Gain Practical Experience: Build a portfolio through personal projects, Kaggle competitions, and internships. Apply theoretical knowledge to real-world datasets.
Earn Relevant Certifications (Optional): Validate skills with industry-recognized certifications like Google Data Analytics or AWS Machine Learning.
Create a Strong Portfolio and Resume: Showcase projects, machine learning models, and data visualizations. Data scientists should leverage platforms like GitHub to demonstrate their work.
Apply for Jobs and Prepare for Interviews: Practice coding challenges, SQL queries, and machine learning case studies for technical interviews.
Engage in Continuous Learning: Stay updated with the latest tools and trends in AI, deep learning, and big data for long-term career growth.

What is the degree needed to become a data scientist?

A bachelor’s degree in computer science, statistics, mathematics, or data science is the most common starting point for a career in data science, providing essential skills in programming, statistical modeling, and data visualization.

For those looking to advance quickly, a master’s degree in data science, AI, or some of the fastest online MBA programs can offer deeper expertise and access to higher-paying roles. That said, many successful data scientists have entered the field through self-study, bootcamps, and certifications, proving that hands-on experience and strong technical skills can sometimes outweigh formal education.

Degree Breakdown

Bachelor’s Degree (Entry-Level Roles): A bachelor's degree in computer science, statistics, mathematics, or data science provides foundational knowledge in programming, data analysis, and statistical modeling. Graduates with this degree can land junior data analyst or data scientist roles but may need additional certifications or experience to advance.
Master’s Degree (Preferred by Employers): A master's degree in data science, machine learning, or business analytics, including accelerated MBA programs, significantly enhances career prospects, providing specialized training that employers value for mid-level and senior roles.
Ph.D. (For Research & AI Roles): A Ph.D. in machine learning, artificial intelligence, or statistics is beneficial for those aiming to work in cutting-edge AI research, academia, or highly technical roles that require deep expertise in algorithm development. While not required for most industry jobs, it is valuable in research-intensive fields.
Alternative Paths (Bootcamps & Certifications): Some professionals transition into data science through coding bootcamps, online courses, or industry-recognized certifications like Google Data Analytics, IBM Data Science, or AWS Machine Learning. These alternatives provide a faster and more flexible way to learn data science skills, especially for career changers. You can also look for the cheapest online IT training bootcamps if you would like more affordable options.

According to Zippia, 51% of data scientists in the United States have a bachelor's degree, 34% earned a master's degree, 13% obtained a doctorate degree, and 1% hold an associate degree.

What skills do data scientists need?

Becoming a data scientist requires a strong foundation in technical knowledge and critical soft skills to extract insights, develop models, and predict results. A postsecondary degree and practical experience help develop both key areas. Below are the essential skills that enable professionals to navigate and excel in this growing industry.

Technical Skills

Programming: Mastery of Python, R, and SQL is crucial for efficient data manipulation, statistical modeling, and algorithm development. These languages empower data scientists to process and analyze large datasets effectively.
Statistics & Mathematics: A strong foundation in probability, regression analysis, linear algebra, and hypothesis testing is vital for validating findings and building accurate predictive models.
Machine Learning: Proficiency in machine learning techniques, including supervised and unsupervised learning, neural networks, decision trees, and random forests, allows for the creation of sophisticated models that support data-driven decision-making.
Big Data Tools: Efficiently processing and managing large-scale data necessitates proficiency in distributed computing frameworks and big data tools such as Hadoop and Spark.
Data Visualization: Proficiency in tools like Tableau, Power BI, Matplotlib, and Seaborn facilitates clear communication of data insights.

Soft Skills

Problem-Solving: The ability to analyze data-driven challenges and develop innovative, effective solutions is fundamental to the data scientist's role.
Critical Thinking: Evaluating data validity, questioning assumptions, and forming unbiased conclusions ensures accuracy in analysis and decision-making.
Communication: Translating complex technical findings into clear, concise insights enables non-technical stakeholders to make informed business decisions.
Collaboration: Working effectively with cross-functional teams enhances project outcomes and promotes innovation.
Curiosity & Continuous Learning: A passion for exploring data and staying updated on evolving tools and trends ensures adaptability in the field.

According to Zippia, the skills most commonly found on the CVs of data scientists are Python (13.5%), data science (9.9%), visualization (5.3%), Java (4.5%), Hadoop (4.3%), Tableau (3.3%), and data analytics (3.3%).

What are the types of software used by data scientists?

Data scientists rely on various software solutions to manage data, build machine learning models, and generate insights. These solutions streamline workflows, enhance analytical capabilities, and support decision-making across industries. Below are the key types of software solutions used in data science:

Data Science & Analytics Platforms: These platforms provide an integrated environment for coding, data exploration, and machine learning model development. They often include built-in libraries for statistical analysis, visualization, and AI-driven automation. These are also used by graduates of online MBA programs no GMAT in their various career options.
Development Environments: Integrated development environments (IDEs) and interactive coding notebooks help data scientists write, test, and debug code efficiently. These tools often support multiple programming languages and provide features like package management and version control.
Machine Learning & AI Frameworks: Machine learning frameworks allow data scientists to build, train, and deploy predictive models. They offer pre-built algorithms, deep learning capabilities, and optimization techniques for working with structured and unstructured data.
Big Data Processing & Distributed Computing: For handling large datasets, data scientists use distributed computing solutions that enable parallel processing and real-time data analytics. These tools support large-scale data storage, retrieval, and processing across multiple servers.
Data Visualization: Visualization software helps transform raw data into meaningful insights through interactive charts, graphs, and dashboards. These tools allow users to explore trends, compare datasets, and communicate findings effectively to stakeholders. As such, they are also leveraged by graduates of online medical billing and coding programs.
Cloud Computing & AI Services: Cloud-based solutions provide scalable computing power for data storage, model training, and analytics. These platforms offer infrastructure for machine learning, automated pipelines, and real-time data processing.
Data Engineering & Workflow Automation: To streamline data workflows, data scientists use tools for managing and automating data pipelines. These solutions facilitate data extraction, transformation, and loading (ETL) while integrating with various databases and APIs.

It is important to note that many comprehensive platforms incorporate the core features of some of the given software, reducing the number of digital tools needed and potential costs.

The chart below shows the sectors that have hired the most data scientists.

What are the programming languages used by data scientists?

Data scientists leverage various programming languages, each suited to different aspects of modeling, visualization, and data analysis. The choice of language is based on data volume, computational requirements, and other pertinent factors. Some languages are optimized for statistical analysis, while others excel in machine learning, big data processing, or real-time data visualization. Below are the most commonly used languages in the field:

Python: Python's versatility and user-friendly nature, combined with its extensive libraries for machine learning, data analysis, and visualization, make it the most popular language among data scientists.
R: R is known for its statistical capabilities and is ideal for data mining, statistical modeling, and creating high-quality visualizations.
SQL: SQL is essential for querying and managing relational databases, making it a foundational skill for handling structured data.
Java: Java is often used for building scalable applications and integrating machine learning models into production environments.
Scala: Scala is preferred for big data processing and working with distributed computing frameworks like Apache Spark.
Julia: Julia is designed for numerical analysis and scientific computing, and is valued for its speed and efficiency in handling large datasets.
C/C++: C/C++ is useful for high-performance computing tasks, enabling fine-tuned control over algorithms and applications requiring speed.
MATLAB: MATLAB is commonly used in academia and engineering fields for numerical computations and algorithm development.
JavaScript: JavaScript is increasingly applied in real-time data visualization and web-based analytics tools.
Go: Go is known for its speed and simplicity, and is gaining traction in handling large-scale data processing tasks efficiently.

Python is the most popular programming language for data science, with 90.6% of professionals leveraging it for data science tasks, followed by Structured Query Language (SQL) (53%), and R (38%).

What are the best certifications for data scientists?

Earning a data science certification can help professionals enhance their skills, validate their expertise, and improve job prospects, similar to students of accelerated accounting degree online programs. Certifications vary in focus, covering areas like machine learning, big data, cloud computing, and statistical analysis. Below are some of the best certifications for data scientists:

General Data Science Certifications

Certified Data Scientist (CDS): The CDS certification assesses a candidate’s proficiency in data analysis, machine learning, and data visualization.
IBM Data Science Professional Certificate: A beginner-friendly certification covering Python, SQL, data analysis, and machine learning fundamentals.
HarvardX Data Science Professional Certificate: A comprehensive online program that includes R programming, probability, inference, and machine learning.

Machine Learning & AI Certifications

TensorFlow Developer Certificate: Focuses on deep learning, neural networks, and TensorFlow framework for AI model development.
AWS Certified Machine Learning – Specialty: Covers machine learning model deployment, data engineering, and AI applications on AWS.
Microsoft Certified: Azure AI Engineer Associate: Designed for professionals working with AI services, machine learning, and automation on Microsoft Azure.

Big Data & Cloud Certifications

Google Professional Data Engineer: Validates expertise in designing, building, and managing big data solutions on Google Cloud.
Cloudera Certified Data Scientist: Covers big data analytics, machine learning, and cloud-based data processing.
AWS Certified Data Analytics – Specialty: Focuses on big data storage, processing, and analytics on AWS.

Business & Analytics Certifications

SAS Certified Data Scientist: A strong credential for professionals working with statistical modeling, machine learning, and big data tools.
Microsoft Certified: Power BI Data Analyst Associate: Ideal for data professionals who specialize in data visualization and business intelligence.
Google Data Analytics Professional Certificate: A beginner-friendly certification that covers SQL, visualization, and statistical analysis.

Specialized & Advanced Certifications

Data Science Council of America (DASCA) Senior Data Scientist: An advanced certification for experienced professionals focusing on predictive modeling and AI applications.
Stanford Machine Learning Certificate: A well-respected online program that teaches machine learning fundamentals with a strong mathematical foundation.
MIT Professional Certificate in Machine Learning & AI: A high-level certification focusing on deep learning, natural language processing, and AI strategies.

What are common jobs for aspiring data scientists?

Aspiring data scientists face various career paths they can pursue based on their preferences, skill level, and experience level, like graduates of cheap graphic design schools. The flexibility of data analytics enables data scientists to enter most industries and companies of varying scales. Here are some common jobs for those starting out in the field of data science:

Junior Data Scientist: Gathers and analyzes data to generate insights for decision-making, contributing to machine learning model development, statistical analysis, and visualization, while developing skills under senior guidance
Data Analyst: Interprets data to reveal trends and actionable insights, using tools like spreadsheets, SQL, and visualization software to inform organizational strategy
Junior Data Engineer: Builds and maintains data pipelines for efficient information flow, transforming raw data for analysis and improving database performance
Machine Learning Engineer (Entry-Level): Implements algorithms and creates predictive models to solve specific problems, focusing on feature engineering and model deployment
Business Intelligence Analyst: Develops dashboards and reports to track key metrics, providing organizations with clear business insights derived from raw data
Database Administrator: Ensures database security and performance, managing tasks like query optimization and data backup
Operations Analyst: Analyzes operational data to identify inefficiencies and drive process improvements
Marketing Analyst: Analyzes marketing data to optimize strategies and improve ROI
Financial Data Analyst: Forecasts financial trends and assesses risks to support investment and budgeting decisions
Quantitative Analyst (Quant): Develops mathematical models for financial problem-solving, including risk assessment and market prediction

Which online master's programs offer the best value for data science?

Data science professionals seeking to deepen their expertise often pursue advanced degrees to bridge theory and practical application. When evaluating online master's programs, consider factors such as curriculum relevance, faculty industry experience, practical project opportunities, and career support services. Additionally, a detailed review of tuition costs versus overall program benefits can help ensure a solid return on investment. For candidates prioritizing affordability without compromising quality, review the cheapest online data science masters to compare program offerings.

What is the job outlook for data scientists?

Data scientists are particularly in demand since most industries need the services they provide. According to the United States Bureau of Labor Statistics (BLS), the employment of these professionals is expected to rise by an impressive 36% from 2023 to 2033, dwarfing the national average of 4%.

Data science or computer science graduates will not have a hard time finding gainful employment. Job portal Indeed shows that there are currently over 21,000 job openings associated with data science in the country. The BLS adds that graduates and job seekers can expect about 20,800 data science openings each year from 2023 to 2033.

Some sectors and US states hire more data scientists than others. The industries with the highest employment levels are computer systems design and related services (23,150); company management (19,060); technical, scientific, and management consulting services (12,720); insurance carriers (11,590); and scientific research and development services (10,130).

As for the US states that employ the most data scientists, California (33,220), Texas (20,560), New York (16,390), Florida (8,400), and Pennsylvania (7,490) round up the leaderboard.

How Can I Land My First Data Science Job?

Crafting a compelling professional profile is essential. Create a project portfolio that showcases real-world applications of advanced analytics, robust machine learning models, and data visualization skills. Employers value demonstrable expertise, so include case studies, competitive hackathon results, and contributions to open-source projects. Expand your network by engaging with industry-specific groups and leveraging professional platforms to learn about niche opportunities, such as bioinformatics job demand. Preparing for technical interviews and staying updated on industry standards can further solidify your candidacy in this competitive market.

What is the average salary of data scientists?

Data scientists are well-paid professionals. According to the BLS, they receive an average annual salary of $119,040 and a median annual salary of $108,020, more than double the collective median annual wages of all US workers ($48,060). Remarkably, these numbers can go even higher if a data scientist finds employment in certain sectors and locations.

The industries that furnish the highest average annual salaries, as shown in the visual below, are taxi and limousine services ($172,970); computer and peripheral equipment manufacturing ($171,760); media streaming distribution services, social networks, and other content providers ($164,690); web search portals and other information services ($163,000); and semiconductor and other electronic component manufacturing ($157,670).

Meanwhile, the US states that provide the highest average annual salaries are Washington ($148,730), California ($140,490), Virginia ($139,080), New York ($134,830), and New Jersey ($134,140).

What are the biggest trends shaping data science currently?

Data science is advancing rapidly, fueled by technological innovations and the increasing need for data-driven insights. As the field continues to evolve, new trends are shaping how data is collected, analyzed, and applied to solve complex problems across industries. Here are some of those trends:

Generative AI: Generative AI continues to grow, impacting industries such as advertising, healthcare, and software development. It enables the creation of realistic images, text, and simulated data for training models, driving innovation across sectors. However, ethical concerns and over-reliance on AI remain challenges.
Big Data on the Cloud: The shift to cloud-based solutions for big data management is accelerating due to scalability, cost-effectiveness, and accessibility. Organizations increasingly use private and public cloud services for predictive analytics and efficient handling of massive datasets.
Augmented Analytics: Augmented analytics leverages AI and machine learning to automate data analysis processes, making insights accessible to non-technical users. This trend accelerates decision-making and democratizes analytics across industries.
TinyML: TinyML focuses on deploying machine learning models on low-power devices like IoT sensors, enabling real-time edge computing. This trend supports faster data processing closer to its source while reducing energy consumption.
Small Data and Scalable AI: Organizations are increasingly prioritizing small data combined with scalable AI models to extract insights from limited datasets. This approach addresses privacy concerns and supports quicker decision-making.

How Can Online Learning Platforms Accelerate My Data Science Journey?

Modern online learning platforms provide structured pathways that blend theory with real-world projects, enhancing both technical and analytical skills. These programs are crafted to keep professionals current with evolving technologies and methodologies in data science, bridging the gap between academic knowledge and industry demands. Enrolling in programs that offer hands-on experience, mentorship, and collaborative learning can lead to faster career advancement and more competitive candidate profiles. For those seeking quality education that aligns with career goals, exploring fast accredited online tech programs can be a decisive step forward.

Here's What Data Scientists Have to Say About Their Jobs

“Being a data scientist means solving complex problems with data-driven insights that make a real impact. I love the constant challenge of working with AI and machine learning to uncover patterns that drive business decisions. Every project feels like a puzzle, and the thrill of cracking it never gets old!” – Phil

"One of the best things about being a data scientist is the versatility of the role. I’ve had the chance to work across healthcare, finance, and marketing, applying my skills to solve unique challenges in each field. This career has opened countless doors for me and keeps me constantly learning and growing." – Roman

"The flexibility and the intellectual challenge of data science are unmatched; I'm always exploring new technologies and methodologies. Knowing that my work contributes to meaningful advancements in various fields is truly inspiring." – Seth

Other Things You Should About Becoming a Data Scientist

What is the difference between a data scientist and a data engineer?

Data scientists primarily analyze and interpret data to extract insights and build predictive models, focusing on the "what" and "why." Data engineers, on the other hand, build and maintain the infrastructure and pipelines that enable data scientists to access and use data, focusing on the "how" of data management. Essentially, data scientists use the data, while data engineers provide and organize it.

What is the difference between a data scientist and a data analyst?

A data scientist develops machine learning models, conducts advanced statistical analysis, and uses predictive modeling to extract deeper insights from data. In contrast, a data analyst focuses on examining structured data, identifying trends, and creating reports or visualizations to support business decisions. While both roles involve working with data, data scientists typically require stronger programming and machine learning expertise, whereas data analysts emphasize data interpretation and reporting.

Is data science considered CS or IT?

Data science is not strictly considered computer science (CS) or information technology (IT), but it overlaps with both fields. Data science is an interdisciplinary field that combines elements of computer science, statistics, and domain-specific knowledge to analyze and interpret data. While it utilizes tools and techniques from computer science, such as programming and algorithms, its focus on extracting insights from data distinguishes it as a distinct discipline.

Does data science require coding?

Yes, coding is essential in data science because it enables data processing, statistical analysis, and machine learning model development. Popular languages like Python, R, and SQL are widely used for tasks such as cleaning data, building algorithms, and generating visualizations. While no-code tools exist, coding proficiency allows for more flexibility, scalability, and advanced problem-solving.

References:

BLS (2024, April 3). 15-2051 Data Scientists. BLS
BLS (2024, August 29). Data Scientists. BLS
JAIN (2024, November 18). Top Programming Languages for Data Scientists to Master. JAIN
Sapra,Y. (2025, January 15). How Big Data is Revolutionizing Industries in 2025. Hash Studioz
Zippia (2025, January 8). Data Scientist Education Requirements. Zippia
Zippia (2025, January 8). Data Scientist skills for your resume and career. Zippia