A data scientist in 2026 builds predictive models, designs experiments, and turns raw data into strategic decisions. The skills to learn, in order: Python (pandas + NumPy), SQL, statistics and probability, machine learning (scikit-learn), and deep learning fundamentals (PyTorch or TensorFlow). A master's degree helps but isn't required — a portfolio with 3 end-to-end projects and a deployed model gets you hired faster than a diploma alone.
This article was researched and written by the Careery team — that helps land higher-paying jobs faster than ever! Learn more about Careery →
How long does it take to become a data scientist?
With a structured plan: 6-12 months if you study full-time, 12-18 months part-time. The core stack (Python + SQL + statistics) takes 3-4 months. Adding machine learning and building portfolio projects takes another 3-6 months. The biggest bottleneck isn't learning algorithms — it's building end-to-end projects that demonstrate you can frame a business problem, select the right model, and communicate results to non-technical stakeholders.
Can you become a data scientist without a degree?
Yes, but it's harder than for data analysts. According to the Bureau of Labor Statistics, most data scientist positions require at least a bachelor's degree, and many prefer a master's or PhD. However, employers increasingly accept candidates with bootcamp training, strong portfolios, and demonstrated modeling skills. A candidate with three deployed ML projects, Kaggle competition results, and strong statistical intuition can compete with degree holders — especially at startups and mid-size companies.
What is the difference between a data scientist and a data analyst?
Data analysts answer known questions using existing data — pulling reports, building dashboards, and identifying trends. Data scientists build predictive models to answer unknown questions — using machine learning, statistical modeling, and experimentation. Think of it this way: data analysts describe what happened, data scientists predict what will happen.
How much do data scientists make in 2026?
Entry-level data scientists earn $75,000-$100,000. Mid-level (3-5 years): $100,000-$140,000. Senior (5+ years): $140,000-$200,000+. Staff and principal data scientists at top tech companies earn $200,000-$350,000+ in total compensation. The Bureau of Labor Statistics reports a median salary of $108,020 for data scientists, with the field growing 36% from 2023 to 2033.
What skills do data scientists need?
Python (pandas, NumPy, scikit-learn — used daily), SQL (every data scientist queries databases), statistics and probability (hypothesis testing, Bayesian reasoning, distributions), machine learning (regression, classification, clustering, ensemble methods), and communication (translating model outputs into business recommendations). Deep learning (PyTorch or TensorFlow) is increasingly expected for mid-level and senior roles.
Every company wants to "use AI." Most have no idea where to start. Data scientists are the people who bridge the gap between raw data and intelligent decisions — and the demand for them is growing faster than almost any other technical role. The Bureau of Labor Statistics projects 36% growth for data scientists from 2023 to 2033, making it one of the fastest-growing occupations in the economy.
Here's the learning order: Python first, then SQL, then statistics and probability, then machine learning, then deep learning fundamentals. That's not arbitrary — it's the sequence that builds each skill on top of the last and gets you employable fastest. Everything else — degrees, certifications, specializations — layers on top of these five pillars.
Companies are sitting on mountains of data they don't know how to use. Data scientists are the ones who turn that data into predictions, experiments, and competitive advantages. The role goes far beyond dashboards and reports — it's about building systems that make decisions smarter.
- Data Scientist
A data scientist designs experiments, builds predictive models, and applies statistical and machine learning techniques to solve business problems. Using Python, SQL, and frameworks like scikit-learn and PyTorch, data scientists transform raw data into actionable predictions and recommendations. Unlike data analysts who describe what happened, data scientists predict what will happen and prescribe what to do about it.
The Real Day-to-Day
Here's what data scientists actually do — not the job posting fantasy, but real work:
Morning
- Write a Python script to clean and feature-engineer 6 months of customer transaction data for a churn prediction model
- Run SQL queries against the data warehouse to validate that training data matches production data distributions
- Review experiment results from an A/B test on a new recommendation algorithm — calculate statistical significance and effect size
- Debug a gradient boosting model that's overfitting on the training set by tuning regularization parameters
Afternoon
- Present model results to the product team: "This churn model identifies at-risk customers 45 days before cancellation with 82% precision — here's the cost-benefit analysis of intervening"
- Pair with a machine learning engineer to prepare a model for production deployment — discuss latency requirements and monitoring
- Explore a new dataset using Jupyter notebooks — plot distributions, check for missing values, test correlations between features
- Write documentation for the modeling pipeline so the team can reproduce and iterate on the work
Emily Robinson and Jacqueline Nolis, authors of Build a Career in Data Science (Manning, 2020), describe the role clearly: "A data scientist's job isn't to build the most complex model — it's to solve the right problem with the simplest model that works." That distinction separates productive data scientists from perpetual tinkerers.
Data science in 2026 is applied problem-solving with data. The value is not in the algorithm — it's in framing the right question, choosing the right approach, and communicating the answer to people who make decisions.
But "data scientist" means different things at different companies. Understanding how the role differs from adjacent careers — and choosing the right path — starts with a clear map.
This is the most common confusion in the data world. All four roles touch data and code, but the daily work, required skills, and career trajectories are fundamentally different.
| Factor | Data Scientist | Data Engineer | ML Engineer | AI Engineer |
|---|---|---|---|---|
| Primary focus | Build models, design experiments, extract insights from data | Build and maintain data pipelines and infrastructure | Deploy and optimize ML models in production | Build LLM-powered applications and AI agents |
| Core tools | Python, R, SQL, Jupyter, scikit-learn, pandas, NumPy | Python, SQL, Spark, Airflow, dbt, cloud platforms (AWS/GCP) | Python, Docker, Kubernetes, MLflow, TensorFlow Serving | Python, LangChain, vector databases, OpenAI/Anthropic APIs |
| Key outputs | Predictive models, experiment results, statistical analyses, recommendations | Data pipelines, ETL jobs, data warehouses, data lakes | Production ML systems, model APIs, monitoring dashboards | AI-powered features, chatbots, RAG pipelines, AI agents |
| Math required | Heavy (linear algebra, calculus, probability, statistics) | Minimal (data structures, algorithms) | Moderate (optimization, linear algebra) | Low-moderate (embeddings, prompt engineering concepts) |
| Closest analogy | Scientist (runs experiments to predict the future) | Plumber (builds the pipes data flows through) | Factory engineer (turns prototypes into production lines) | Product builder (assembles AI components into user-facing features) |
The key distinction: Data scientists focus on problem formulation and modeling — figuring out what to predict and how to predict it. ML engineers focus on deploying models to production — making sure predictions happen reliably at scale. Data engineers focus on data infrastructure — making sure the data scientists and ML engineers have clean, reliable data to work with. AI engineers focus on building applications powered by pre-trained models — integrating LLMs and generative AI into products.
In practice, boundaries blur. At a startup, one person might do all four roles. At a large tech company, each role is a distinct team with its own career ladder. The smaller the company, the more you need to be a generalist.
Not sure which role fits your skills? Our Data Scientist Career Path guide maps the progression from entry-level to staff, and our Is Data Science a Good Career? guide covers market outlook, salary ceilings, and long-term viability.
Data scientists build models and design experiments. Data engineers build the infrastructure that feeds those models. ML engineers deploy models to production. AI engineers build LLM-powered applications. At smaller companies, these roles overlap — at larger ones, they're distinct career tracks with different skill requirements.
Knowing the differences helps you choose the right path. The next question is how to get there — and there are three very different routes.
Three paths lead to a data science career. The "best" one depends on your background, budget, and timeline.
| Factor | Master's / PhD Degree | Bootcamp (3-6 months) | Self-Taught |
|---|---|---|---|
| Time | 1-2 years (master's), 4-6 years (PhD) | 3-6 months full-time | 6-18 months (your pace) |
| Cost | $30,000-$120,000+ | $10,000-$20,000 | $0-$1,000 (free courses + optional certs) |
| Best for | Career changers wanting the strongest credential, research-oriented roles | Career changers with quantitative backgrounds who need structure | Self-motivated learners with existing programming or math skills |
| Career services | Alumni network, campus recruiting, research advisor connections | Job placement support, employer partnerships, career coaching | None (you network and apply independently) |
| Employer perception | Very strong — meets most job requirements | Growing acceptance, especially with strong portfolio | Depends entirely on portfolio quality and Kaggle profile |
| Credential | MS/PhD in data science, statistics, CS, or related field | Bootcamp certificate | Online certificates (Google, IBM), Kaggle rankings |
| Best outcome | Research scientist, senior DS roles, FAANG-level positions | Mid-size company DS roles, analytics-heavy DS positions | Startup DS roles, analyst-to-DS transitions |
The education reality for data science: Data science has a higher credential bar than data analytics. According to the Bureau of Labor Statistics, a bachelor's degree is the typical entry-level education for data scientists, and many employers prefer a master's degree. However, "preferred" doesn't mean "required" — a candidate with strong Python skills, statistical intuition, three deployed ML projects, and competition results can absolutely get hired without a graduate degree. The portfolio is still the ultimate proof of competence.
Certifications won't replace a degree or portfolio, but they can strengthen your application. Our Best Data Science Certifications guide ranks programs by employer recognition and ROI.
A master's degree opens the most doors in data science — especially at large companies and research-oriented roles. Bootcamps offer the fastest structured path for career changers with quantitative backgrounds. Self-teaching works best for people with existing programming or math skills. All three paths require a strong portfolio to be competitive.
The path gets you knowledge. But knowledge without applied skills is just theory. Here's what to actually learn — and in what order.
Learn these in order. Each tier builds on the previous one. Tier 1 gets you through the door. Tier 2 makes you competitive. Tier 3 makes you hard to replace.
Tier 1: Non-Negotiable (Learn First)
Python (pandas + NumPy) — Python is the lingua franca of data science. Not "basic Python scripting" — you need pandas for data manipulation (groupby, merge, pivot, time series), NumPy for numerical computing, and comfortable fluency with Jupyter notebooks. If you can load a messy CSV, clean it, engineer features, and produce a summary report in pandas — you're ready for the next step.
SQL — Every data scientist queries databases. You need JOINs across multiple tables, window functions, CTEs, and subqueries. SQL is how you extract the raw material for your models. Many data science interviews include a SQL assessment — and failing it ends the process regardless of your modeling skills.
Statistics and Probability — This is what separates data scientists from people who know how to call sklearn.fit(). Distributions, hypothesis testing, Bayesian reasoning, confidence intervals, p-values, correlation vs. causation. You don't need a PhD-level understanding — you need enough intuition to know when a result is real and when it's noise.
Tier 2: Core (Learn Next)
Machine Learning (scikit-learn) — Supervised learning (linear regression, logistic regression, random forests, gradient boosting), unsupervised learning (k-means, PCA, DBSCAN), model evaluation (cross-validation, ROC curves, precision-recall tradeoffs), and feature engineering. scikit-learn is the standard library — learn it deeply before moving to deep learning. Aurélien Géron's Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow (O'Reilly, 2022) is the most practical reference.
Data Visualization — matplotlib, seaborn, and Plotly for exploratory analysis and communicating model results. Visualization isn't optional — it's how you explain your models to stakeholders who don't read code. A confusion matrix means nothing to a VP. A chart showing "we can catch 82% of churning customers 45 days early" means everything.
Tier 3: What Makes You Stand Out
Deep Learning (PyTorch or TensorFlow) — Neural networks, CNNs, RNNs/transformers, transfer learning. Not every data science role requires deep learning, but the ones that pay the most do. PyTorch has become the industry standard for research and is increasingly used in production.
Experimentation and Causal Inference — A/B testing design, uplift modeling, difference-in-differences, instrumental variables. Companies like Netflix, Airbnb, and Spotify hire data scientists specifically for experimentation — it's one of the highest-value specializations.
Cloud Platforms (AWS/GCP/Azure) — SageMaker, Vertex AI, or Azure ML for training and deploying models at scale. Cloud fluency signals you can work beyond your laptop.
Python and SQL are the foundation — learn them first. Statistics separates data scientists from code copiers. Machine learning is the core craft. Deep learning and experimentation are the specializations that command the highest salaries. Master Tier 1 and 2, and you qualify for most entry-level and mid-level data science roles.
Skills on paper don't get you hired. Applied skills — demonstrated in a portfolio — do. Here's how to build one that actually works.
A data science portfolio is not a collection of Kaggle notebook copies. It's proof that you can take a messy, ambiguous business problem and deliver a working solution. Hiring managers have seen a thousand Titanic survival models — show them something that proves independent thinking.
Three projects is the sweet spot. Each should demonstrate a different skill from the Tier 1-2 stack, and each should answer a real question — not just "explore" a dataset.
The End-to-End ML Project
What to build: A complete machine learning pipeline — from raw data to deployed prediction. Pick a real problem: predict customer churn, forecast demand, classify support tickets. Use a public dataset from Kaggle, UCI, or a public API. Include data cleaning, feature engineering, model selection, hyperparameter tuning, and evaluation.
Tools: Python (pandas, scikit-learn), Jupyter, GitHub
What it proves: You can frame a business problem as an ML task, build a model that works, and evaluate it rigorously — not just call .fit() and report accuracy.
The Statistical Analysis / Experimentation Project
What to build: A rigorous statistical analysis or simulated A/B test. Analyze a real dataset with hypothesis testing, confidence intervals, and causal reasoning. Show that you understand when a result is statistically significant, when it's practically significant, and when you need more data.
Tools: Python (scipy, statsmodels), Jupyter, GitHub
What it proves: You have statistical intuition — not just ML cookbook skills. This is the project that differentiates data scientists from people who completed a machine learning MOOC.
The Deep Learning or NLP Project
What to build: A project using neural networks — image classification with transfer learning, text classification with transformers, or a recommendation system. Deploy it as a simple web app or API using Streamlit or FastAPI so anyone can interact with it.
Tools: PyTorch or TensorFlow, Hugging Face, Streamlit/FastAPI, GitHub
What it proves: You can work with modern deep learning tools and deploy a model that people can actually use — not just a notebook that runs on your laptop.
Three portfolio projects — one end-to-end ML pipeline, one statistical analysis, one deep learning project — prove you can do the job. Each should solve a real business problem, not just demonstrate a technique. A deployed model is worth ten Jupyter notebooks.
A strong portfolio gets you interviews. But understanding what hiring managers actually look for — and what their job postings really mean — is what gets you the offer.
Most data science job postings are aspirational wish lists. Understanding the gap between what they say and what they actually need is an unfair advantage.
| What the Job Posting Says | What They Actually Mean |
|---|---|
| "5+ years of experience in data science" | You can independently scope, build, and evaluate ML models — not just follow tutorials |
| "PhD preferred" | They want strong statistical and mathematical foundations — a master's degree or equivalent self-taught depth works |
| "Experience with TensorFlow/PyTorch" | You've built and trained models beyond scikit-learn — not just completed a deep learning course |
| "Strong communication skills" | You can explain a model's business impact to a non-technical VP without using the word 'gradient' |
| "Experience with big data technologies" | You've worked with datasets that don't fit in memory — Spark, distributed computing, cloud pipelines |
| "Full-stack data science" | You can do everything from data extraction to model deployment — common at startups, rare at large companies |
Where the Jobs Are
Data science roles exist across every industry, but the experience varies significantly:
- Tech companies (FAANG and startups) — Cutting-edge tools, experiment-heavy culture, highest pay. Focus on recommendation systems, search ranking, personalization. Competitive hiring with take-home challenges and whiteboard coding.
- Finance and fintech — Risk modeling, fraud detection, algorithmic trading. Strong quantitative bar. Excellent pay with bonus-heavy compensation.
- Healthcare and biotech — Clinical trial analysis, drug discovery, patient outcome prediction. Meaningful work, specialized domain knowledge required. Growing demand.
- E-commerce and retail — Demand forecasting, pricing optimization, customer segmentation. High volume of data, fast iteration cycles. Strong entry point for first DS roles.
- Consulting (McKinsey QuantumBlack, BCG Gamma, Deloitte) — Client-facing, high-pressure, exposure to many industries. Excellent learning accelerator but limited depth in any one domain.
- Applying only to 'Data Scientist' titles — many equivalent roles are called 'Applied Scientist,' 'Research Scientist,' 'ML Scientist,' or 'Quantitative Analyst'
- Listing algorithms without context on your resume — 'Random Forest' means nothing; 'Built a random forest churn model that identified 82% of at-risk accounts, saving $2.1M in annual revenue' means everything
- Overinvesting in deep learning before mastering fundamentals — most real-world DS problems are solved with gradient boosting and logistic regression, not neural networks
- Skipping the SQL assessment prep — many candidates with strong ML skills fail because they can't write a window function under time pressure
- Waiting until you feel 'ready' — apply when you can build an end-to-end ML project independently, even if you haven't memorized every algorithm
Ready to apply? Our Data Scientist Resume Guide has templates and bullet point formulas for quantifying ML impact, and our Data Scientist Cover Letter Guide shows how to translate technical skills into business language.
Data science job postings are wish lists, not requirements lists. Apply when you can independently build end-to-end ML projects, target equivalent titles beyond "Data Scientist," and always include a portfolio link. The first role is the hardest to get — after that, experience compounds.
Landing the first role is the steepest part of the climb. Once you're in, the career trajectory is well-defined — and the ceiling is higher than almost any other technical role.
Data science has a defined career ladder, though titles vary by company. The progression is less about years and more about what you can independently own and the scope of problems you solve.
| Level | Typical Years | Focus | Salary Range (US) | What Gets You to the Next Level |
|---|---|---|---|---|
| Junior Data Scientist | 0-2 | Execute assigned modeling tasks, clean data, build initial models under guidance | $75,000-$100,000 | Deliver end-to-end projects independently, develop domain expertise |
| Data Scientist | 2-5 | Own modeling projects, design experiments, collaborate with product and engineering | $100,000-$140,000 | Identify high-impact problems proactively, influence product decisions with data |
| Senior Data Scientist | 5-8 | Lead complex projects, mentor juniors, define modeling strategy for a product area | $140,000-$200,000 | Drive cross-functional initiatives, publish internal research, build reusable tools |
| Staff / Principal Data Scientist | 8+ | Set technical direction, lead research initiatives, partner with executives on strategy | $200,000-$350,000+ | Organizational impact, thought leadership, defining the company's data science culture |
Specialization Paths
As you gain experience, specialization increases your market value — and your salary ceiling:
- Product Data Scientist — A/B testing, causal inference, user behavior modeling. The most common specialization at tech companies. Directly tied to product decisions and revenue impact.
- Research Scientist — Pushing the state of the art in ML/AI. Requires deep mathematical foundations, often a PhD. Found at research labs (DeepMind, FAIR, Google Brain) and R&D teams.
- ML Platform / Infrastructure — Building internal tools, feature stores, and model serving platforms. Hybrid DS/MLE role. High demand at companies scaling their data science practice.
- Domain Specialist — NLP, computer vision, time series forecasting, recommendation systems. Deep expertise in one technical area. Commands premium salaries when the domain is in demand.
See our Data Scientist Career Path guide for detailed progression maps, and our Data Scientist Job Outlook for market demand forecasts and the impact of AI on the profession.
Data science career progression moves from executing assigned modeling tasks to owning strategic decisions. The jump from junior to mid-level hinges on independence. The jump from mid to senior hinges on business impact and technical leadership. Specialization — especially in experimentation, NLP, or ML infrastructure — accelerates both salary and career growth.
- 01Data scientists build predictive models and design experiments to turn raw data into strategic business decisions
- 02Learn skills in this order: Python (pandas + NumPy) → SQL → statistics and probability → machine learning (scikit-learn) → deep learning (PyTorch/TensorFlow)
- 03Three education paths work: master's degree (strongest credential), bootcamp (fastest structured path), self-taught (cheapest) — all require a portfolio
- 04Build three portfolio projects: one end-to-end ML pipeline, one statistical analysis, one deep learning project — each solving a real business problem
- 05Data scientists differ from data engineers (infrastructure), ML engineers (deployment), and AI engineers (LLM apps) in focus, tools, and daily work
- 06Job postings are wish lists — apply when you can build end-to-end ML projects independently, and target equivalent titles (Applied Scientist, ML Scientist, Quantitative Analyst)
- 07Career progression moves from executing models to owning strategy — salaries range from $75K entry-level to $350K+ at staff level
Is data science a good career in 2026?
Yes. The Bureau of Labor Statistics projects 36% growth for data scientists from 2023 to 2033 — much faster than average for all occupations. The median salary is $108,020, with senior roles exceeding $200,000. Data science also offers strong career optionality — the skills transfer to ML engineering, AI engineering, product management, and technical leadership. The role is evolving as AI tools automate routine tasks, but the core value of problem formulation, experiment design, and business communication remains in high demand.
Can I become a data scientist with no experience?
Yes, but it requires more preparation than entry-level analytics roles. Data science has a higher technical bar — employers expect Python fluency, statistical reasoning, and ML modeling skills. The path in: build three strong portfolio projects (end-to-end ML, statistical analysis, deep learning), participate in Kaggle competitions, contribute to open-source projects, and earn a certification from Google, IBM, or a recognized bootcamp. A quantitative background (math, physics, engineering, economics) makes the transition significantly easier.
Do I need a master's degree to become a data scientist?
Not strictly, but it helps significantly. Many job postings list a master's degree as preferred — especially at large companies and research-oriented roles. However, bootcamp graduates and self-taught candidates with strong portfolios are hired regularly, particularly at startups and mid-size companies. A master's degree is most valuable for: (1) career changers from non-technical fields, (2) candidates targeting research scientist roles, and (3) international candidates who need visa sponsorship, as many employers require an advanced degree for sponsorship eligibility.
What programming languages do data scientists need?
Python is non-negotiable — it's used by over 90% of data scientists daily (Kaggle Survey, 2024). SQL is required for data extraction and is tested in most DS interviews. R is still used in academia, healthcare, and government, but Python has largely replaced it in industry. Beyond languages, the key frameworks are: pandas and NumPy for data manipulation, scikit-learn for machine learning, PyTorch or TensorFlow for deep learning, and matplotlib/seaborn for visualization.
What is the difference between data science and machine learning?
Data science is the broader discipline — it includes data collection, cleaning, exploratory analysis, statistical modeling, machine learning, experimentation, and communication. Machine learning is a subset of data science focused specifically on building algorithms that learn from data and make predictions. A data scientist uses machine learning as one tool among many. An ML engineer specializes in building and deploying ML models at scale. Think of it this way: data science is the question, machine learning is one method of answering it.
Will AI replace data scientists?
AI will change data science, not eliminate it. Tools like ChatGPT, GitHub Copilot, and automated ML platforms are already handling routine tasks — writing boilerplate code, running standard analyses, and generating visualizations. But the core value of a data scientist — identifying the right problem to solve, designing experiments, understanding causal relationships, and communicating nuanced findings to stakeholders — requires judgment that AI cannot replicate. Data scientists who leverage AI tools as accelerators will become dramatically more productive, not obsolete.
How is data science different from data analytics?
Data analytics focuses on describing and interpreting existing data — building dashboards, writing reports, identifying trends. Data science focuses on predicting future outcomes and prescribing actions — building ML models, running experiments, and developing algorithms. Data analysts answer 'what happened?' Data scientists answer 'what will happen?' and 'what should we do about it?' Data science requires stronger math (linear algebra, calculus, probability) and programming skills, and typically commands higher salaries.
What projects should I build for a data science portfolio?
Build three projects that demonstrate different skills: (1) an end-to-end machine learning project — take raw data, clean it, engineer features, train and evaluate multiple models, and present business recommendations; (2) a statistical analysis or simulated A/B test — demonstrate hypothesis testing, confidence intervals, and causal reasoning; (3) a deep learning or NLP project deployed as a web app — show you can work with modern tools and ship something people can interact with. Each project should solve a stated business problem, not just explore a dataset.
Prepared by Careery Team
Researching Job Market & Building AI Tools for careerists · since December 2020
- 01Occupational Outlook Handbook: Data Scientists — Bureau of Labor Statistics, U.S. Department of Labor (2024)
- 02Build a Career in Data Science — Emily Robinson and Jacqueline Nolis (2020)
- 03Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow (3rd Edition) — Aurélien Géron (2022)
- 04State of Data Science and Machine Learning Survey — Kaggle (2024)