Data Scientist vs Data Analyst: Key Differences, Salaries & Career Paths in 2026

If you’ve been browsing job portals lately, you’ve probably noticed two titles showing up everywhere: Data Analyst and Data Scientist. They sound similar; they both work with data, and honestly, a lot of data analyst and data science job ads use the terms almost interchangeably. But they’re not the same job, and picking the wrong one to aim for can cost you months of studying the wrong skills.

What is a Data Analyst?

A Data Analyst uses existing data—such as sales figures, website traffic, or customer feedback—to identify trends and help businesses make informed decisions. They clean, analyse, and visualise data using SQL, Excel, Power BI, and Tableau to answer questions such as what happened and why it happened.

What is a Data Scientist?

A Data Scientist builds predictive models and machine learning solutions to forecast outcomes and automate decisions. They work with structured and unstructured data using Python, statistics, and machine learning to answer what is likely to happen next and develop intelligent, data-driven systems.

Data Analyst vs Data Scientist: Key Differences

Feature	Data Analyst	Data Scientist
Primary Goal	Explain what happened and why, using existing data	Predict what will happen next and build systems that act on it
Focus	Historical trends, reporting, and business performance	Forecasting, automation, and building predictive models
Programming	Basic to intermediate SQL; Python/R often optional	Advanced Python (or R) is essential, including libraries like Pandas and Scikit-learn
Statistics	Descriptive statistics — averages, trends, correlations	Inferential statistics, probability theory, and hypothesis testing at a deeper level
Machine Learning	Rarely required; may use pre-built ML outputs	Core skill — building, training, and evaluating ML models
Data Volume	Works mostly with structured, moderate-sized datasets	Works with large-scale structured and unstructured data (text, images, logs)
Decision Making	Supports decisions with reports and dashboards for others to act on	Builds models and systems that can make or automate decisions directly
Technical Complexity	Moderate — tool-driven and business-focused	High—engineering, statistics, and applied AI combined

The honest way to think about it: almost every Data Scientist could do a Data Analyst’s job. Not every Data Analyst is immediately equipped to do a Data Scientist’s job; that jump usually requires dedicated upskilling in programming and statistics. You can upskill through a specialised data science degree or a computer science degree

Skills Required for Data Analysts in 2026

The bar for analysts has moved up. It’s no longer just Excel and PowerPoint. Employers in 2026 expect:

• SQL fluency — writing and optimising queries is non-negotiable
• Data visualisation — Power BI or Tableau, with the ability to design dashboards that actually get used, not just built
• Excel/Google Sheets at an advanced level — pivot tables, formulas, basic automation
• Basic statistics — understanding averages, distributions, correlation vs causation
• Business acumen — the ability to translate numbers into a story a manager can act on
• AI-assisted workflows — using tools like Claude or ChatGPT to speed up analysis, write SQL faster, and summarise findings; this has become a real differentiator in hiring conversations
• Python or R (increasingly expected) — not mandatory everywhere, but a growing number of listings now ask for at least basic scripting ability

Skills Required for Data Scientists in 2026

• Data scientist roles ask for a deeper technical stack:
• Python (essential) — with libraries like Pandas, NumPy, and Scikit-learn
• Machine learning fundamentals — regression, classification, clustering, and model evaluation
• Statistics and probability — at a level well beyond descriptive stats
• SQL and data engineering basics — pulling and shaping large, messy datasets
• Cloud platforms — AWS, Azure, or Google Cloud experience is increasingly expected, not optional
• Communication skills — the ability to explain a model’s output to people who aren’t technical
• Applied AI/LLM knowledge — many 2026 job listings now expect familiarity with generative AI tools and how to apply large language models to real business problems, not just traditional ML

Educational Requirements for Both Careers

• A bachelor’s degree in computer science, data science degree, statistics, mathematics, or a related field is the standard entry point for both roles
• Data Analyst roles are more accessible to career switchers — many analysts move in from business, finance, or marketing backgrounds after completing a data science degree, bootcamp, or professional certification
• Data Scientist roles more often expect a strong quantitative foundation, and a postgraduate qualification (Master’s) is common among mid-to-senior hires, especially at larger companies and MNCs
• Portfolio projects matter more than the certificate alone — employers increasingly want to see real analysis or models you’ve built, not just a transcript
• A structured data science degree gives you both the theory and the practical exposure employers look for, which is why more universities are building programmes specifically around this career path rather than treating data science as a module inside a general IT degree

Data Analyst vs Data Scientist Salaries in 2026

Data Analyst Salaries

1.1 Entry Level (0-2 years): Around MYR 3,500–5,500 per month
Strong SQL and visualisation skills or portfolios built during their studies.
1.2 Mid Level (3-5 years): Roughly MYR 6,500–8,000 per month.
Expertise in business intelligence, finance analytics, or marketing analytics.
1.3 Senior Level (6+ years): MYR 8000–15,000 per month
Strong stakeholder management skills and platform depth
(Source: Glassdoor)

Data Scientist Salaries

2.1 Entry Level (0–2 years): Around MYR 5,000–7,000 per month
Strong foundations in Python, SQL, machine learning, and statistics.
2.2 Mid Level (3–5 years): Around MYR 8,000–14,000 per month
Experience building and deploying machine learning models, with expertise in AI, predictive analytics, or big data.
2.3 Senior Level (5+ years): Around MYR 15,000–25,000 per monthStrong stakeholder management, leadership, and platform expertise, with experience deploying AI solutions at scale.
(Source: Calculator Malaysia)

The overall pattern: the gap between analyst and scientist pay widens with seniority. A Senior Data Scientist can earn 60–80% more than a Senior Data Analyst, mainly because the skill barrier to entry and to advancement is steeper.

Data Analysts vs Data Scientists: Career Paths in Malaysia

A typical data analyst and data science career path looks like this:

• Start as a Data Analyst or Junior Data Scientist — build a foundation in SQL, visualisation, and basic statistics
• Specialise — choose a domain (finance, marketing, e-commerce, healthcare) and go deep
• Upskill into Python, ML, and cloud platforms if you want to move from analyst to scientist — this transition typically takes 12–18 months of focused, structured learning.
• Move into senior or lead roles — Data Analytics Manager, Lead Data Scientist, or Data Architect- where you’re shaping strategy, not just producing reports
• Long-term, both paths can lead to leadership positions like Head of Analytics or Chief Data Officer, roles that increasingly sit at the executive table at Malaysian companies

Top employers hiring for both data analyst and data scientist roles in Malaysia span e-commerce (Shopee, Lazada, Grab), banking (Maybank, CIMB, Hong Leong Bank), and multinational shared service centres (Accenture, DXC, Intel)—giving you a genuinely wide range of industries to enter through.

Launch Your Data Science Career with LSBF Malaysia

A strong foundation is essential in a field that evolves as quickly as data and data science. LSBF Malaysia offers two bachelor’s programmes aligned with industry demand:

• Bachelor of Science with Honours in Computing (Data Science), 3+0, in collaboration with the University of Greenwich – Focuses on statistics, machine learning, and data analytics for students pursuing a dedicated data science career.
• Bachelor of Science with Honours in Computer Science, in collaboration with the University of Suffolk – Provides a broad computing foundation with pathways into data-focused careers.

Both programmes combine technical skills such as Python, statistics, and machine learning with practical business applications, leading to internationally recognised UK degrees delivered in Malaysia.

With data science jobs consistently ranking among the most searched-for careers in Malaysia, and demand only growing across banking, e-commerce, and tech, now is a practical time to invest in the right qualification.

Whether you’re aiming to become a data analyst first and grow into a data scientist role, or you want to go straight for the technical deep end, a structured data science degree gives you the credibility, network, and hands-on project experience that a self-taught path often struggles to match.

Check out LSBF Malaysia’s bachelor’s programme here.

A blog written by Serin Thankam Sam

FAQs Data Analyst & Data Science Degree