Top 10 Skills For Data Scientists

With many businesses in the progress of adopting AI technology and looking to close gaps in essential skills, data science looks set to be one of the most in-demand skills of the future. 

In fact, data scientists, particular those proficient in AI, look set to be in increasing demand, with 50% of employers needing more data scientists than they have at present. 

Looking to develop your data science skills? Our resident data and AI specialist, Hazar Tez, has selected the top 10 must have skills for data scientists, and a recommended course to support your learning.

Hazar Tez is a data and AI specialist, researcher, and trainer with a unique blend of academic depth and industry experience across data science, data engineering, cloud and AI technologies. 

1. Data Science Fundamentals

Understanding data lifecycle, data governance, data science tools and visualization are essential skills for beginners. 

Why data science fundamentals are important

Data science fundamentals give you the critical foundation to understand what data science actually is, how it’s applied, and how to think like a data scientist.

Without this base, it's easy to misuse tools, misinterpret results, or focus on the wrong problems. A strong grasp of the fundamentals helps you ask better questions, choose appropriate methods, and communicate insights with clarity, making everything else in your data science journey more effective.

Our expert says:

"Learning the fundamentals will give you the confidence to approach real-world problems with structure and purpose. It will help you go beyond just coding to actually understanding how to drive business decisions with data." - Hazar Tez

Best courses for fundamental data science skills

• Introduction to Data Science for Data Professionals

2. Data Wrangling and SQL

Over 80% of a data scientist’s time is often spent not modeling, but preparing the data. If you can’t access, clean, and structure raw data, your insights and models will be built on flawed foundations.

Why data wrangling and SQL skills matter

Data wrangling is a core competency in data science because raw data is rarely clean, complete, or analysis-ready. Whether you're working with databases, APIs, or flat files, being able to extract, filter, reshape, and join data using SQL and Python (with libraries like pandas) is critical to generating trustworthy insights.

Solid data handling skills ensure your analysis is built on clean foundations, your models are accurate, and your workflows are efficient, especially when dealing with large, messy datasets common in real-world projects.

Our expert says:

"You will find that mastering data wrangling in Python and SQL gives you the edge in nearly every data science project, whether you're cleaning marketing data, joining sales records, or preparing inputs for a machine learning model. These are the tools that make you fast, reliable, and highly employable in any data-driven role." - HT

Best courses for data wrangling and SQL

• Python for Data Handling
• Querying SQL Databases 

3. Descriptive and Inferential Statistics

Descriptive and inferential statistics are foundational to understanding, interpreting, and validating data in any data science workflow. 

Why descriptive and inferential statistics skills matter

Descriptive statistics help you summarize and visualize key patterns, while inferential statistics enable you to test hypotheses, draw conclusions, and make predictions based on data samples.

Without these skills, it's easy to misread results, overfit models, or deliver misleading insights. A solid grasp of statistical thinking ensures your analysis is rigorous, explainable, and trusted by stakeholders.

Our expert says: 

"You will consistently rely on descriptive and inferential statistics to uncover patterns, validate assumptions, and guide decision-making across projects. Building strength in these areas will set you apart as someone who not only analyzes data but understands the confidence and uncertainty behind every conclusion you share." - HT

Best courses for statistics skills

• Statistics For Data Analysis in Python
• Statistics For Data Analysis in R
• Fundamentals of Data Statistics 

4. Programming and Data Pipelines

Python is the leading language in data science due to its simplicity, flexibility, and powerful ecosystem of libraries like pandas, NumPy, and scikit-learn. 

Why programming skills and data pipelines matter

Knowing how to write clean, efficient Python code and build scalable data pipelines allows you to automate workflows, process large datasets, and move seamlessly from data extraction to analysis and modeling.

These skills are essential for production-level work, ensuring that your insights and models can be delivered reliably and repeatedly in real-world environments.

Our expert says: 

"You will become highly versatile by learning both Python and R for data pipelines, enabling you to work across different teams and industries with confidence.

This dual-language ability, paired with automated workflow design, will help you stand out as a flexible and well-rounded data scientist who can deliver insights from prototype to production." - HT

Best courses for programming and data pipelines skills

• Python 3 Programming
• Data Handling in R
• Programming in R

5. Time Series and Forecasting

Time series forecasting is a critical skill in data science for predicting future values based on historical trends.

Why time series and forecasting skills matter

Time series forecasting is used in a wide range of business applications, like sales forecasting, inventory planning, demand prediction, and financial modeling, where understanding seasonality, trends, and anomalies directly impacts operational and strategic decisions.

Mastering time series methods equips you to deliver insights that drive forward-looking action, making your work essential to decision-makers who rely on accurate forecasting to reduce risk and optimize performance.

Our expert says: 

"You will find time series forecasting to be one of the most valuable tools in real-world analytics, especially when working in domains like retail, finance, or logistics.

By demonstrating your ability to model and anticipate future outcomes using historical data, you will become a trusted contributor to business planning and strategy." - HT

Best courses for time series and forecasting skills

• Time Series and Forecasting with R
• Time Series and Forecasting with Python

6. Machine Learning and Big Data

As data science matures, it's no longer enough to build accurate models. You also need to operationalise them.

Why machine learning and big data skills matter

Machine learning, big data technologies, and MLOps practices enable you to train models on large-scale datasets and deploy them reliably into production environments.

Cloud platforms like AWS, Azure, and Databricks allow you to scale these workflows, automate retraining, monitor performance, and integrate with real-time systems. These skills are essential for turning prototypes into production-ready solutions that deliver consistent business value.

Our expert says: 

"You will stand out in the data science field by mastering cloud-scale machine learning, big data tools, and deployment workflows. These capabilities show that you can move beyond experimentation to build scalable, production-grade solutions, making you a valuable asset to any data-driven organization looking to operationalise AI." - HT

Best courses for machine learning and big data

• Data Science and Machine Learning with Python
• Practical Data Science with Amazon SageMaker
• Machine Learning on Google Cloud
• Machine Learning Engineering on AWS
• Designing and Implementing a Data Science Solution on Azure
• Implementing a Data Science and Machine Learning Solution for AI with Microsoft Fabric

7. Mathematics for Data Science

Mathematics is the backbone of data science, enabling you to understand how algorithms work, interpret model behavior, and make informed decisions about methods and tuning.

Why mathematics skills matter

Key areas like linear algebra, calculus, probability, and statistics underpin everything from regression to deep learning. A solid grasp of math allows you to go beyond using libraries and actually understand what’s happening under the hood, leading to better models, smarter troubleshooting, and clearer explanations to stakeholders.

Our expert says: 

"You will gain a major advantage by developing a strong mathematical foundation for data science, as it will help you understand the logic behind models, optimize algorithm performance, and confidently defend your analytical choices. 

This knowledge will set you apart as someone who doesn't just use tools, but truly understands them." - HT

Best courses for mathematics skills

• Mathematics for Data Science

8. Deep Learning and Generative AI

Generative AI is rapidly transforming data science by enabling new ways to create synthetic data, automate feature engineering, generate code, and build intelligent assistants. 

Why deep learning and generative AI matters

Deep learning and Gen AI expands what’s possible, from improving model training with richer data to accelerating workflows through AI-powered automation.

Understanding generative AI equips you to leverage cutting-edge tools and techniques, stay competitive, and drive innovation in your data projects.

Our expert says: 

"You will unlock powerful new capabilities by mastering generative AI in data science, allowing you to automate routine tasks, enhance data quality, and create innovative solutions. This skill positions you at the forefront of the field, ready to harness AI’s full potential to transform business outcomes." - HT

Best courses for deep learning and generative AI skills

• Generative AI Essentials
• Generative AI Engineering with Databricks

9. Experimental Design

Experimental design is crucial in data science because it allows you to draw valid, causal conclusions from data. 

Why experimental design matters

Whether you're running A/B tests, optimizing processes, or validating model impact, well-designed experiments help control bias, isolate variables, and ensure statistical clarity.

Without proper design, even the most sophisticated models can lead to misleading insights or wasted resources. This skill enables you to test hypotheses confidently and make data-driven decisions that truly hold up under scrutiny.

Our expert says: 

"This is arguably the most important skill for an experienced data scientist. You will elevate your impact as a data scientist by learning experimental design, giving you the ability to structure tests that produce clear, actionable results to impact the growth of your business.

This expertise is especially valuable in roles involving product development, marketing, or operations, where validating the effect of changes can directly influence strategy and revenue." - HT

Best courses for experimental design courses

• Implementing a Machine Learning Solution with Azure Databricks
• Advanced Machine Learning with Databricks
• Smart Analytics, Machine Learning and AI on Google Cloud
• Amazon SageMaker Studio for Data Scientists

10. Business Acumen and Data Storytelling 

In data science field, expertise come with soft skills. 

Why soft skills matter? 

Business acumen, prioritization, and storytelling are essential for transforming technical data science work into real-world business value, while doing your job with impact.

These skills help you understand what problems matter, frame them in the right way, focus on what drives impact, and communicate insights clearly to non-technical stakeholders.

Without them, even the best models or analyzes can go unused. Thinking in terms of outcomes, not just outputs, ensures your work drives decisions, aligns with strategic goals, and creates measurable impact.

Our expert says: 

"You will significantly increase your influence as a data professional by building strong business acumen and communication skills. The ability to prioritize high-impact projects and tell a clear data story will make your insights more actionable and position you as a strategic partner, not just a technical contributor." - HT

Best courses for soft skills

• Fundamentals of Data Storytelling
• Present with Impact

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We have an experienced team of AI and data trainers who support businesses and individuals in upskilling in essential data skills, including analytics, data science, data visualization, AI and data engineering and more.