Top 10 Data Science Skills for Business Analysts

Running a business is more complicated than ever, and leaders need to know their information is accurate and impactful. Analytics offer data-driven solutions that enhance both operational and strategic decision-making. The need for analysts who can offer these solutions is growing, with the Bureau of Labor Statistics expecting the number of management analysts to grow by 9% from 2024 to 2034.¹

Analysts serve as the bridge between technical and non-technical stakeholders. Technical skills, such as statistics and coding, are vital for understanding the data. Soft skills, such as communication and storytelling, help you present your findings. Understanding the basics of machine learning can help analysts automate many of their tasks.

This post lists the top 10 data science skills you need for success as a business analyst. You’ll also learn how a Master of Science in Business Analytics can help you build these skills.

1. Data Analysis and Interpretation

A business analyst turns numbers into insights. To do so, they must possess an understanding of both structured data, such as names or dates, and unstructured data, such as collections of emails or other text.²

Analysts identify patterns and place data in the proper context to turn raw numbers into actionable, real-world insights. Their information increasingly drives business decision-making.³

2. Statistical Knowledge

Statistics help assign meaning to large sets of numbers.⁴ Business analysts use descriptive statistics to summarize a data set, such as reporting the mean, mode or variance of a data set.⁵ They also leverage inferential statistics to make predictions or generalizations about the data.⁶ For instance, you could use inferential statistics to estimate the behavior of a large group of consumers.

To decipher mountains of raw stats, business analysts use multiple data analysis techniques, including probability, hypothesis testing and regression analysis.

3. Python and R Programming Skills

One of the most important data science skills a modern business analyst can have is proficiency in Python or R. Python libraries help analysts manipulate data and build predictive models, while R offers powerful statistics capabilities and libraries that can assist with both manipulation and visualization.⁷

Both languages provide analysts with tools to automate tasks and develop reproducible workflows.⁸ For instance, analysts could build an automated weekly sales report or set triggers that alert stakeholders to potential problems.

4. SQL and Database Management

Knowledge of Structured Query Language (SQL) is required for most business analyst positions.⁹

SQL allows business analysts to update and manage databases. It can also extract specific datasets, transform data into compatible formats and build detailed reports.

Analysts use these capabilities to uncover trends and insights hidden in a company’s raw data.⁸ For instance, you could sort customer data to find and compare different customer profiles or identify the purchases made by a particular customer.

5. Data Visualization

From basic bar charts to detailed dashboards, visualizations help business analysts communicate with stakeholders who aren’t knee-deep in the data weeds. Tools such as Tableau and Power BI, along with Python visualization libraries, assist analysts in creating dashboards and visual reports that tell a story about business trends.

Knowing how to use data visualization tools to their full potential allows you to enhance communication by creating more engaging presentations. It can also encourage a more data-driven culture.¹⁰

6. Business Intelligence Tools

Business intelligence tools pull information from multiple sources to answer analytical questions about business needs. They give shape to raw data, supporting better decision-making in the process. For instance, BI tools can identify successful marketing strategies, predict cash flow needs or predict future problems.¹¹

Businesses also use BI platforms to facilitate collaboration, reporting and data collection across multiple areas.¹¹

7. Machine Learning Basics

Machine learning is one of several AI technologies revolutionizing business. Knowing basic machine learning algorithms such as linear regression, logistic regression and decision trees helps analysts build predictive models.⁸ Business forecasting supported with machine learning concepts can predict future sales, analyze customer churn and optimize supply chains.¹²

Understanding machine learning can also help you collaborate with data scientists and those working in AI.⁸

8. Data Cleaning and Preparation

Bad data yields bad results. That’s why data preparation is vital for ensuring quality and reliability. Preparation involves detecting and correcting missing, inconsistent and inaccurate data, as well as identifying relevant data sets.¹³

Different data science skills can complement each other. For instance, business analysts can use machine learning algorithms along with their expertise in data cleaning to automate some of their preparation.¹³

9. Critical Thinking and Problem-Solving

Critical thinking and problem-solving are obligatory skills for data science success. Formulating business questions, identifying patterns in data and spotting possible connections all require analysts to think objectively and avoid cognitive biases.¹⁴

Analysts also use problem-solving skills to recommend solutions that meet certain constraints. For instance, an analyst might need to find an optimal marketing solution given a certain budget or schedule.

10. Communication and Storytelling With Data

Data science skills alone aren’t enough. Analysts also need to effectively communicate their insights to stakeholders, especially when they are advocating for changes in the business.¹⁵ Dr. Guillermo Rodríguez-Abitia, program director of William & Mary’s Online Master of Science in Business Analytics, stated, “I always tell my students we are storytellers. It doesn’t matter that you’re the best model maker and you create all these models that will give you very good insights in terms of what the data is telling you. If you cannot communicate that in plain English, in business language to the business leaders, then your work is useless.”

Business analysts serve as the link between technical and non-technical stakeholders.¹⁶ Soft skills—communication, storytelling, building relationships—are key in showing how your data findings align with business objectives.

Build Your Business Analysis Skills Today

Business analysis evolves by the day. If you’re not continuously learning, you could get left behind. An Online Master of Science in Business Analytics (MSBA) from William & Mary gives you the data science skills required today, as well as a foundation for the future.

We’ve developed our curriculum in consultation with employers to build both the technical skills and business leadership you need for career growth. Six months after graduation, 78% of our alumni report receiving or expecting to receive a promotion.¹⁷

Our accelerated program is built for ambitious professionals eager to use their data science skills in the real world. You can earn your 32-credit degree in as few as 16 months, with no GMAT or GRE scores required. Learn more about our admissions requirements and take advantage of multiple mentoring opportunities as you network with our tight-knit alumni community.

Schedule a call today with an admissions outreach officer, or contact us with any questions.