TL;DR
In data analysis vs data science, choose analysis to explain what happened and science to predict what happens next. Data analysis turns existing data into reports, dashboards and findings, often within days rather than months. Data science builds models that keep running, so it needs cleaner data, more engineering and ongoing monitoring. Most organizations should start with analysis, because models depend on trusted data and agreed metrics. Fund data science when a decision repeats at scale and better predictions justify owning a model. For a career, pick analysis if you like business questions and SQL, and science if you enjoy statistics and Python.
Key Takeaways
Data analysis explains what happened and why, while data science builds models that predict, classify or recommend what to do next.
Analysts work mostly in SQL, spreadsheets and BI tools, whereas data scientists add Python or R, deeper statistics and machine learning.
Analysis delivers findings in days or weeks, but a production model takes longer and needs monitoring for as long as it runs.
Build a trusted analysis layer before funding data science, since every model inherits the data quality problems underneath it.
AI assistants such as Copilot in Power BI speed up both roles, yet people still frame the question, check the data and own the result.
Analysis is also a common entry route, so many analysts grow into data science by adding statistics, programming and modeling.
One Churn Question, Two Answers
Picture a subscription software company whose renewals have slipped for two quarters in a row. At the Monday leadership meeting, the head of customer success finally asks one question. Why are customers leaving?
First, a data analyst picks up the question. Within a few days, she builds a cohort dashboard that splits lost accounts by plan, region, signup month and support history. The pattern is clear: most of the losses sit in one mid-tier plan, among customers who never finished onboarding.
A data scientist hears the same question and asks a different one. Which accounts will leave next quarter? Over several weeks, he trains a model on usage, billing and support data. The result is a churn-risk score for every live account, which is then refreshed each week.
Both answers are right. The dashboard explains the past, so leaders can fix the onboarding gap. The model ranks the future, so account managers can call the riskiest customers first.
That split sits at the heart of data analysis vs data science. It also shapes which role a company should hire first and which career path suits you, and this guide covers both choices.
What Is Data Analysis?
Data analysis is the work of examining a dataset to answer a specific business question. The analyst cleans the data, explores it, tests possible explanations and then presents the finding in a form a manager can act on.
Most of these questions concern the past and the present rather than the future. Why did sales fall in the Northeast in March? Which marketing channel brought in the customers who stayed longest?
Each of those questions has an answer hidden in data the company already holds, even if nobody has looked yet. So the analyst’s job is to find that answer, check it against other sources and explain it in plain language.
Typical outputs of data analysis include:
Recurring reports and KPI scorecards for finance, sales and operations
Interactive dashboards in BI tools such as Power BI or Tableau
One-off investigations that explain a spike, a drop or an outlier
Metric definitions that the whole company agrees to use
Cadence also matters. A typical analysis takes anywhere from a few hours to a few weeks, and its job is done once the decision is made. When the same question returns every month, the analyst turns it into a dashboard. A tool like Microsoft Power BI then refreshes the answer instead of someone repeating the work by hand.
What Is Data Science?
Data science uses statistics, programming and machine learning to build models that predict, classify or recommend. Instead of answering a question once, it produces a model that answers the same question again for every new customer, order or transaction.
For example, think of a bank deciding whether to approve a loan. An analyst can report the prior year’s default rate by credit band. A data scientist, by contrast, can build a credit risk model that scores each new application in seconds. It draws on patterns no person could hold in their head.
That difference also changes the work itself. A model needs large, well-labeled training data, careful validation and a plan for what happens after launch. Because customer behavior shifts, a model that was accurate at launch can drift, so someone has to monitor and retrain it.
Data science also handles data that standard reporting tools struggle with, such as free text, images, audio and sensor streams. For instance, the text of support tickets can feed a model that flags frustrated customers before they cancel. Our guide to machine learning for business analytics shows how these models also feed back into everyday BI.
Forecasting is still the most familiar example. When a model estimates next month’s demand or a patient’s readmission risk, that is predictive analytics . It is also one of the core jobs data scientists are hired to do.
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Kanerika builds forecasting, classification and risk models on top of trusted data, then deploys and monitors them so the predictions keep working after launch.
Explore Predictive Analytics Data Analysis vs Data Science at a Glance
The quickest way to see the difference is to line both disciplines up against the same questions. The table below compares them across ten practical dimensions, from the question each one asks to how success gets measured.
Dimension Data analysis Data science
Core question What happened, and why? What will happen, and what should we do? Main methods Descriptive statistics, segmentation, trend and variance analysis Statistical modeling, machine learning, experiments Data types Mostly structured tables from ERP, CRM and finance systems Structured data plus text, images, logs and sensor data Typical tools SQL, Excel, Power BI, Tableau Python or R, notebooks, scikit-learn, ML platforms Main output Reports, dashboards, findings Models, scores, forecasts, recommendations Time horizon Past and present Future, often in near real time Depth of math Applied statistics Probability, linear algebra, statistical learning Where the team sits With a business function or a central BI team In a data science or AI team, close to engineering Infrastructure Warehouse or lakehouse plus a BI layer The same, plus training compute, a model registry and monitoring Success measure Faster, better-informed decisions Model accuracy in use and the value of better predictions
Treat each row as a tendency, since real roles overlap. Plenty of analysts run a regression, and every data scientist spends time on plain descriptive work before modeling anything.
Data Analysis vs Data Analytics vs Data Science
The three terms get mixed up constantly, so it helps to pin them down. Data analytics is the wider practice of using data to support decisions, including the people, tools and processes behind it. Within that practice, data analysis is one activity: examining a specific dataset to answer a specific question.
Data science overlaps with analytics but extends it with programming and machine learning. Still, a simple test settles most debates. If the output is an explanation, the work is analysis. If the output is a model that keeps making predictions, the work is data science.
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Data Analysis and Data Analytics Are Not the Same Thing
A short Kanerika explainer on where data analysis ends and the wider practice of data analytics begins, and why the difference matters when you scope a project.
The Four Types of Analytics and Who Owns Them
Analysts and data scientists often describe their work through four types of analytics. Each type answers a harder question than the one before, and ownership shifts as you climb.
Descriptive analytics asks what happened, so analysts own it through reports and dashboards.Diagnostic analytics asks why it happened, which analysts also answer through drill-downs and root-cause work.Predictive analytics asks what will happen next, so data scientists own it through forecasting and classification models .Prescriptive analytics asks what to do about it, and data scientists also own it through optimization and recommendation models.
Back in the opening churn story, the cohort dashboard was diagnostic work, while the weekly risk score was predictive.
The handoff is not strict, though. A strong analyst can build a simple forecast, while a data scientist often starts a project with descriptive work. Even so, the ladder is a practical way to decide who should own a new request.
Data Analyst vs Data Scientist: Roles and Daily Work
Job titles blur across companies, so the clearest way to compare the roles is to follow each person through an ordinary Tuesday. Both start from a business question, but they spend the day on very different work.
For example, an analyst might spend Tuesday morning fixing a revenue dashboard that finance flagged. The afternoon then goes to explaining why a spring promotion underperformed. A data scientist might spend the same morning testing whether a new input improves a demand forecast. After that, the afternoon goes to a model review with an ML engineer.
What a Data Analyst Does
Analysts usually report into a business function or a central BI team. Because their work sits close to the decision, communication matters as much as technical skill.
Writes SQL to pull and join data before any analysis starts
Defines KPIs with business owners and keeps them consistent
Builds reports and dashboards, then keeps them current
Investigates spikes, drops and outliers, then explains the cause
Presents findings to managers who need a decision rather than a method
What a Data Scientist Does
Data scientists usually sit in a data science, AI or advanced analytics team. Their work runs on longer cycles than analysis, and much of it is experimental.
Frames a business problem as a prediction or optimization task
Prepares features, the inputs a model learns from, out of raw data
Trains several candidate models, then tests and compares them
Validates results on data the model has never seen before
Works with ML engineers to deploy, monitor and retrain the model
The best data scientists spend as much time questioning the problem as tuning the model. After all, a very accurate forecast of the wrong target is still the wrong forecast.
Where Data Engineers and ML Engineers Fit
Neither role works alone. A data engineer builds the ETL or ELT pipelines that land clean data in a warehouse or lakehouse. Both analysts and scientists then depend on that foundation, whichever side of the data lake vs data warehouse choice it takes.
From there, the chain runs in a clear order. The analyst turns the data into reports and metric definitions, while the data scientist uses the same data to train a model. An ML engineer then puts the model into production and keeps it healthy, a discipline our guide to MLOps tools covers in depth.
The last step is the one teams most often skip. A churn score that lives in a notebook helps nobody. So teams use reverse ETL to push scores back into the CRM, where sales and service staff actually work.
Skills Compared: What Each Role Needs
Both roles draw on the same toolbox, but they use different parts of it at different depths. The overlap is large, which is why people move between the two roles so often.
Think of the skills as dials rather than switches. An analyst, for instance, turns SQL, BI and business communication up high. A data scientist turns statistics, programming and machine learning up high, while still needing enough SQL to fetch their own data.
Skill Data analyst Data scientist
SQL Advanced, used daily Solid, used to pull training data Statistics Descriptive and inferential basics Deep: probability, experiment design, statistical learning Python or R Helpful, often for automation Essential, used daily Machine learning Working awareness Core skill: training, tuning, validation BI and visualization Core skill Useful for exploring data and explaining models Data cleaning Core skill Core skill Domain knowledge Deep in one business area Enough to frame the right problem Communication Daily, with business owners Regular, to explain models and their limits
One row, however, deserves a second look. Data cleaning is a core skill for both roles, since neither can trust a result built on messy data . A cleaned customer table feeds the analyst’s churn dashboard and the scientist’s churn model alike.
Tools Each Role Uses
Tools follow the work, so the table groups them by layer rather than by brand. Open-source libraries also do most of the modeling. For example, the scikit-learn project describes itself as simple and efficient tools for predictive data analysis .
Layer Data analyst Data scientist
Query and preparation SQL, Excel, Power Query SQL, Python with pandas, Spark Visualization and BI Power BI, Tableau, Looker Notebook charts, plus BI for sharing results Modeling Spreadsheet formulas, DAX time intelligence , built-in BI forecasting scikit-learn, XGBoost, PyTorch, TensorFlow Workspace BI service and shared reports Jupyter or Databricks notebooks Operations Scheduled refreshes and data alerts Model registry such as MLflow , deployment and monitoring Shared platform Warehouse or lakehouse such as Microsoft Fabric, Databricks or Snowflake The same platform, so both roles see one version of the data
Overall, the shared platform row matters most for a company. When analysts and scientists read from different copies of the data with no shared data lineage , their numbers disagree and trust in both teams drops. A single governed platform for data analytics avoids that argument before it starts.
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Kanerika builds governed analytics on Microsoft Fabric, Databricks and Power BI, from data pipelines and agreed metric definitions to dashboards your teams trust.
Explore Data Analytics Services How the Two Processes Work
Analysis and data science follow different life cycles, which explains why one ships in days while the other takes weeks or months. An analysis project ends when someone makes a decision.
A data science project only begins its useful life when the model goes live. Then it needs care for as long as it runs. Data science teams often describe that loop with CRISP-DM, the Cross-Industry Standard Process for Data Mining .
The Data Analysis Process
Most analysis work follows six steps, whether the request is a one-off question or a new dashboard:
Clarify the question and the decision it supports.
Collect data from the warehouse, files or business apps.
Clean it by fixing types, removing duplicates and handling gaps.
Explore patterns, segments and outliers.
Explain the cause, then test it against other data.
Present the finding with a clear recommendation.
Step one is where most analysis succeeds or fails, because everything else depends on it. A precise question, such as which region drove the March drop, saves days compared with a vague request to “look at sales.”
For the churn question, the analyst’s cohort view is one grouped query:
SELECT plan_tier,
onboarding_completed,
COUNT(*) AS accounts,
AVG(CASE WHEN churned THEN 1.0 ELSE 0 END) AS churn_rate
FROM accounts
GROUP BY plan_tier, onboarding_completed
ORDER BY churn_rate DESC;The Data Science Process
Data science adds steps before the modeling and, more importantly, after it:
Frame the business problem as a prediction or optimization task.
Prepare and label the data.
Turn raw fields into inputs the model can learn from.
Train several candidate models rather than just one.
Validate them on held-out data and against a simple baseline.
Once one model wins, deploy it into a business process.
Monitor accuracy and drift, then retrain when needed.
Step five hides a cheap safeguard. A simple baseline is easy to skip and costly to miss. If a model cannot beat a prior-year average or a basic trend line, it is not ready for production.
In scikit-learn, that check takes a few lines. The library documents its DummyClassifier as a simple baseline to compare against more complex classifiers:
from sklearn.dummy import DummyClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import roc_auc_score
from sklearn.model_selection import train_test_split
# X = usage, billing and support features; y = 1 if the account churned
X_tr, X_te, y_tr, y_te = train_test_split(X, y, test_size=0.25, stratify=y)
for clf in (DummyClassifier(strategy="prior"), LogisticRegression(max_iter=1000)):
clf.fit(X_tr, y_tr)
print(type(clf).__name__, roc_auc_score(y_te, clf.predict_proba(X_te)[:, 1]))The dummy model scores an AUC of 0.5 by design, so a churn model that barely clears it has learned little.
Steps six and seven are where data science differs most from analysis, since analysis has no equivalent. They need engineering support, a named owner and a budget that continue long after the project team moves on.
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The Hidden Work Behind Machine Learning
Kanerika on the less visible side of machine learning, the preparation and production work that the data science steps above describe.
Data Analysis vs Data Science: Which Should Your Organization Fund?
Funding follows the decision you want to improve. Name that decision first, then match it to the discipline built for it.
For example, a retailer that cannot agree on its own margin figure needs retail data analytics before a demand forecasting model. On the other hand, a retailer with trusted margin data and thousands of daily replenishment decisions probably does.
Business question Discipline Why
Why did revenue miss plan last quarter? Data analysis A one-time explanation from existing data Which products and regions drive margin? Data analysis Segmentation and drill-down Are we on track this month? Data analysis A KPI dashboard with targets How much stock will each store need next week? Data science A repeated prediction at scale Which customers are likely to cancel? Data science A risk score for every customer Which transactions look fraudulent? Data science Classification in near real time Did the price change work? Both Science designs the test, analysis reads the result
A clear pattern also runs through the table. Analysis wins when a question is asked once or by a few people. Data science earns its cost when the same decision repeats hundreds or thousands of times, because a small gain in accuracy then adds up. That repeated-decision territory is where predictive analytics pays off.
Six Readiness Questions Before You Hire Data Scientists
Before you post a data scientist job, answer these six questions honestly:
Do teams agree on the definitions of core metrics such as revenue, churn and margin?
Is the data a model would need captured, stored and reachable in one place?
Is there enough history, with the outcome you want to predict recorded?
Does the decision repeat often enough for better predictions to matter?
Is there a business owner who will act on the model’s output?
Can someone deploy, monitor and retrain the model after launch?
If the answer to any of the first three is no, invest in data engineering and analysis before anything else. If the answer to any of the last three is no, the model will likely end up unused. That is the analysis-first rule in practice.
How AI Is Changing Both Roles in 2026
As of October 2026, AI analytics tools and assistants sit inside the software both roles use every day. Copilot in Power BI offers help “from on-the-fly analysis for business users to data analysis expression (DAX) generation for advanced creators,” according to Microsoft. Microsoft’s August 2026 documentation notes that some Copilot capabilities are generally available, while others are in preview.
On the data science side, AutoML tools and code assistants speed up feature work and model selection as well as boilerplate code. In the churn example, AutoML can quickly test many predictive AI model types on the usage and billing data. The scientist still decides what counts as a churned account and whether the winning model beats the baseline. Our guide to AI data analysis also compares the assistant tools in more depth.
What does not change, however, is the judgment. Someone still has to frame the right question, confirm the data is fit for use and own the model’s decisions after launch.
Common Mistakes When Choosing
Four mistakes come up again and again when companies choose between the two disciplines:
Hiring data scientists before the data is ready. They end up doing data engineering and reporting instead, so the models never ship.Building dashboards for a prediction problem. A chart of last month’s churn cannot tell an account manager whom to call tomorrow.Shipping a model with no owner. Without monitoring and data observability , accuracy decays quietly. The NIST AI Risk Management Framework , a voluntary framework released on January 26, 2023, gives teams a structure for governing models like these.Treating the two teams as rivals. Analysts know the business context a model needs, while scientists can automate an analyst’s most repetitive forecasts.
Each of these mistakes has the same root cause: picking a discipline before naming the decision it should improve. Name the decision, who acts on it and how often it repeats, and the right discipline usually becomes obvious.
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Talk to Kanerika’s data team about the decision you want to improve, how ready your data is and whether a dashboard or a predictive model would serve it better.
Schedule a Demo → Which Career Should You Choose?
For someone choosing a career, the decision comes down to the kind of problem you enjoy. If you like business questions, quick answers and explaining results to people, then analysis fits. If you like statistics, code and building systems that run on their own, then data science fits.
Both paths usually start with a degree in a quantitative or business field. The US Bureau of Labor Statistics lists a bachelor’s degree as the typical entry-level education for data scientists. Analyst roles, however, are open to a wider range of backgrounds, and a portfolio of real SQL and dashboard projects helps any candidate stand out.
If data analysis is the better fit for you, the fastest way to build job-ready skills is by practicing the core workflow inspecting, cleaning, transforming, and modeling data using SQL, Python, or R. Consider enrolling in a data analysis class to structure your learning and get hands-on practice with real datasets.
Pay, Outlook and Entry Routes
Pay and outlook favor data science on paper. The BLS reports median pay of $120,230 for data scientists in May 2025. It also projects employment to grow 35 percent from 2025 to 2035, from about 275,600 jobs in 2025.
Analyst pay varies widely by title and industry, since the label covers roles from marketing to finance. So compare offers within your own field rather than against a single national figure.
Factor Data analysis Data science
Typical entry route Degree in business, economics, math or a related field, plus SQL and BI skills Bachelor’s degree in a quantitative field, plus statistics and programming Core early skills SQL, Excel, Power BI or Tableau, communication Python or R, statistics, machine learning, SQL First projects Reports, dashboards, ad hoc investigations Feature work, model experiments, model evaluation Pay benchmark Varies by title and industry BLS median pay of $120,230 (May 2025) Outlook Depends on the field and title BLS projects 35 percent growth, 2025 to 2035
From Analyst to Data Scientist
Analysis is a common first step into data science, and the move works because the foundations overlap. An analyst who already knows the data and the business can then add statistics, Python and machine learning one project at a time.
A sensible path has three steps. To begin with, automate one recurring report in Python. After that, build a simple forecast for a metric you already track. Then learn validation and deployment alongside an experienced data scientist.
An analyst moving into data science already brings one skill that models depend on: explaining results and their limits to non-technical colleagues. Models only create value when people trust and use them. Strong data literacy across the business is what turns a new data scientist’s models into decisions.
How Kanerika Builds Analytics and Data Science Capability
Kanerika works with both disciplines in the order this guide recommends. The data foundation comes first, a trusted BI layer comes next, and models follow only when the question needs them.
Our data analytics team builds the pipelines, metric definitions and dashboards. Our predictive analytics team then adds forecasting, classification and risk models on top of that foundation.
One manufacturing engagement shows how the two disciplines work together in practice. ABX, a flexible packaging manufacturer in Charlotte, North Carolina, with eight US plants, planned production on manual, gut-feel sales forecasts. As a result, it faced surplus stock in some periods and stockouts in others.
Kanerika built a time-series and regression model to predict sales, plus an LLM module that classifies buyers as churn-prone, inconsistent or consistent. The insights are then delivered through a Power BI dashboard. According to the ABX sales forecasting case study , updated in August 2026, the company reports 85% accurate sales forecasts and 100% granular insights. It also saw a 50% increase in identifying customer churn risk.
In other words, the model is the data science, while the dashboard is the data analysis. Neither one would have changed planning on its own.
Case Study
85% Accurate Sales Forecasting with AI for Manufacturing
For ABX, a US flexible packaging manufacturer, Kanerika built a time-series and regression forecasting model and an LLM buyer-classification module with insights in Power BI, reaching 85% accurate sales forecasts.
Read the Case Study → Is Data Analysis or Data Science Right for You?
Data analysis and data science answer different questions, so the right choice depends on the question in front of you. For an organization, start with data analysis. First agree on metrics, then clean the data and build the dashboards people trust.
Then add data science when a decision repeats at scale and better predictions are worth the cost of owning a model. For a career, choose data analysis when you enjoy business problems and fast answers. Choose data science if you enjoy statistics, code and building models, and remember that many people start in analysis and move across later.
Get good at explaining the past with data analysis first, then fund data science once the same decision repeats often enough to pay for a model.
Frequently Asked Questions
What is the difference between data analysis and data science?
Data analysis examines existing data to explain what happened and why, using SQL, spreadsheets and BI tools. Data science uses statistics, programming and machine learning to build models that predict outcomes or recommend actions. Analysis usually produces reports and dashboards, while data science produces models that keep running and need monitoring after they go live.
Which is better, data science or data analysis?
Neither is better in general, because they solve different problems. Data analysis is the better choice when you need to explain performance, track KPIs or answer a one-time question. Data science is the better choice when a decision repeats at scale and accurate predictions add value. Most organizations need strong analysis before data science pays off.
Does data analytics require coding?
Some coding helps, but the amount depends on the role. Most analysts use SQL daily, and many use Python or R to automate repetitive work. Plenty of analysis can still be done in Excel and BI tools with little code. Data science, by contrast, needs regular programming in Python or R to build and test models.
What are the top 3 skills for a data analyst?
The three most useful skills are SQL for pulling and joining data, data visualization in a BI tool such as Power BI or Tableau, and clear communication of findings to business owners. Statistics and data cleaning support all three. Learning them takes steady practice on real datasets, so data analysis counts as a hard skill.
What are the 4 types of data analytics?
The four types are descriptive analytics, which shows what happened, diagnostic analytics, which explains why, predictive analytics, which estimates what will happen, and prescriptive analytics, which recommends what to do. Analysts usually own the first two types. Data scientists usually own the last two, because those rely on statistical and machine learning models.
What is the hardest part of data analysis?
For most analysts, the hardest part is getting clean, consistent data and a precise question. Data often arrives with gaps, duplicates and conflicting definitions across systems, and a vague request wastes days of work. Agreeing on definitions and clarifying the decision behind the question up front makes the rest of the analysis much faster.
Can a company use data analysis without a data science team?
Yes. Many companies run well on strong data analysis alone, using reports, dashboards and investigations to guide decisions. A data science team becomes worth the cost when a decision repeats at scale and better predictions would change outcomes, such as demand planning or churn prevention. Until then, analysis delivers most of the value.
Is data analysis part of data science?
Data analysis is one of the activities inside data science, but it also stands on its own. Data scientists explore and analyze data before they build any model. Many analysts, on the other hand, never build models at all. So analysis is both a separate discipline and a foundation that every data science project depends on.
When should a company hire a data analyst instead of a data scientist?
Hire a data analyst first when teams disagree on metrics, reports are built by hand or leaders cannot see current performance. An analyst fixes those gaps quickly and creates the trusted data a future model will need. Hire a data scientist once the data is reliable and a repeated decision would clearly benefit from better prediction.
Which is more in demand, data scientist or data analyst?
Both roles remain in demand. The US Bureau of Labor Statistics projects data scientist employment to grow 35 percent from 2025 to 2035. Analyst demand is spread across many job titles in finance, marketing, operations and healthcare, so it depends heavily on the industry and the skills employers want. Strong SQL and communication skills help in both.
Can a data analyst become a data scientist?
Yes, and it is one of the most common routes into data science. Analysts already know SQL, the data and the business context. To make the move, they add deeper statistics, Python or R and machine learning, usually by automating a report, then building a simple forecast, then learning how models are validated and deployed.
Can a data scientist work as a data analyst?
Yes. A data scientist already has the SQL, statistics and data cleaning skills that analysis requires. The adjustment is usually about focus rather than skill. Analyst work rewards speed, clear dashboards and plain-language explanations for business owners, so a data scientist moving into the role needs to favor simple answers over complex models.
Can data analysts build machine learning models?
Many can, especially for simpler tasks such as a sales forecast or a customer segmentation. BI tools and AutoML tools make basic models easier to build than before. Production models are a different matter, because they need careful validation, deployment and monitoring. That work usually belongs to data scientists and ML engineers working together.
Do data scientists earn more than data analysts?
Data scientists usually earn more, because the role needs deeper statistics, programming and machine learning skills. The US Bureau of Labor Statistics reports median pay of $120,230 for data scientists in May 2025. Analyst pay varies widely by title and industry, so compare offers within your own field rather than against one national figure.
Will AI replace data analysts?
AI is unlikely to replace data analysts, but it is changing the job. Assistants such as Copilot in Power BI can draft queries, summaries and DAX formulas. Analysts still decide which question matters, check whether the data is fit for use and explain what a result means for the business. Those judgment skills grow in value.
