Skills for a Successful Career in Business Analytics: Data Visualization, Machine Learning and Statistics

Organizations across sectors increasingly use data to strategize and improve performance. This phenomenon has led to the rise of a relatively new discipline known as business analytics. Business analytics is defined as “the art and science of discovering insight– by using sophisticated mathematical, statistical, machine learning, and network science methods along with a variety of data and expert knowledge.”¹

The demand for business analysts has soared as more businesses embrace this approach. According to the U.S. Bureau of Labor Statistics, the number of jobs for management analysts-an umbrella term that includes business analysts-will increase by 10% between 2022 and 2032.² This guide covers essential business analytics skills that can help you jumpstart or advance your career in this rapidly growing field.

Essential Business Analytics Skills and Tools

Business analysts work in various industries, from agriculture to transportation. Developing foundational skills and mastering popular tools can prepare you for a career in many sectors.

Data Visualization Tools

Business analysts often interpret vast amounts of data, such as financial transactions and stock market prices. Data visualization enables them to share their insights with others in accessible graphic formats.³

Common data visualizations include:³

• Scatter Plots: Use dots to show the distribution of data points on vertical and horizontal axes
• Bar Graphs: Represent quantitative data as bars of varying sizes
• Tables: Display data in rows and columns

Software like Microsoft Power BI, Tableau and QlikView enable business analysts to input data and generate interactive data visualizations.⁴

Data Storytelling

Data visualizations translate information into easily digestible graphics, but they often don’t provide enough context to fully understand the significance of this information. That’s where data storytelling comes in. This technique involves analyzing data and using narrative and visual strategies to tell engaging stories about that data.⁵

Here are a few data science storytelling strategies:⁵

• Define your key takeaways
• Consider your audience
• Organize information into a coherent and sequential story
• Use visual elements like color to reinforce your narrative

Machine Learning Algorithms

Business analysts often need to collect and evaluate enormous and complex datasets. Machine learning algorithms increase the accuracy and efficiency of these processes. This innovative technology uses artificial intelligence-powered models to analyze data and make predictions.⁶

There are three categories of machine learning algorithms:⁶

• Supervised Learning: Algorithms are trained using data with known outputs
• Unsupervised Learning: The algorithms learn to detect clusters—or patterns—in data without human intervention
• Reinforcement Learning: This hybrid approach teaches the algorithms to learn through trial and error

Statistical Modeling

No one can see the future, but statistical modeling allows business analysts to make educated guesses about potential outcomes. This approach uses mathematical formulas and software like Stata to analyze historical data and predict future trends.⁷ Organizations can use this information to make strategic decisions, such as choosing investments and designing products.

A data analyst can use two types of statistical methods to draw conclusions:⁷

• Descriptive Statistics: These approaches summarize data using measurements like mean and mode
• Inferential Statistics: These techniques use t-tests and other methods to extrapolate from data

Database Management and Cleaning

Business analysts spend approximately 80% of their time collecting, cleaning and storing information in databases. Only 20% of their work involves data analysis.⁸

Aspiring professionals can prepare for these responsibilities by developing these skills:⁹

• Data Collection: Using data mining software and other tools to collect relevant information, such as customer purchasing habits and transactions
• Data Cleaning: Formatting data, finding missing values and eliminating anomalies
• Database Management: Inputting data into systems, such as NoSQL databases and HTML files

Programming Languages for Data Analysis

Many business analytics positions don’t require programming language knowledge. However, learning to code can allow you to build data pipelines and evaluate data more efficiently.¹⁰

Here are a few common programming languages often used by data analysis specialists:¹¹

• Python: Write scripts for quantitative calculations and machine learning models
• R: Process data, create visualizations and perform statistical analysis
• Structured Query Language (SQL): Build, manage and query databases
• Java: Create machine learning algorithms

Predictive Analytics

Predictive analytics uses historical data, machine learning models, and other tools to forecast future outcomes. Business analysts use software and algorithms to build predictive models that identify patterns in data and infer future trends. This approach allows organizations to make informed decisions and evaluate risk.¹²

There are many business intelligence applications for predictive analytics, including:¹²

• Fraud detection
• Customer retention
• Lead generation
• Machine maintenance planning

Ethical Decision-Making

Business analysts frequently handle confidential data, such as financial records and customer information. Several professional organizations have established ethical guidelines for analysts.

For instance, the International Institute of Business Analysis’ Code of Ethical Conduct and Professional Standards states that members have a responsibility to:¹³

• Practice transparency
• Respect intellectual property
• Represent the profession honestly
• Comply with data privacy regulations
• Maintain data confidentiality
• Only provide services that fall within their scope of practice
• Avoid conflicts of interest

Problem Solving and Critical Thinking Skills

Thinking critically and solving complex problems are important skills for a business analyst or data analyst. Professionals need to understand how to analyze data to identify patterns, trends and opportunities. They also need to use data-driven insights to develop creative solutions to clients’ problems.¹⁴

Continuous Learning and Adaptability for Business Analysts

Business analytics continues to evolve quickly as new technologies emerge. Analysts can keep up with the latest industry trends by joining professional associations, such as the International Institute of Business Analytics (IIBA), the Project Management Institute (PMI), or the Association for Computing Machinery (ACM).¹⁵ These organizations often host conferences, publish academic research journals and coordinate professional development workshops. Professionals can also subscribe to industry newsletters and follow leaders on social media to learn about new tools and techniques.

Unleash the Power of Data Analytics With an Online Degree

Prepare for a high-powered career as a leading analyst with an Online Master of Science in Business Analytics (MSBA) from William & Mary. Our comprehensive program prioritizes hands-on learning, so you’ll gain practical business analyst experience as you work on real-world case studies. You’ll also develop marketable data analytics skills, such as data warehousing, data visualization and cloud computing. Build the dynamic mindset and soft skills, like interpersonal skills and communication skills, that are necessary to lead in today’s complex and rapidly changing business environment.

William & Mary’s MSBA program provides numerous resources to help you advance on your professional journey as a successful business analyst. You’ll receive guidance from prestigious faculty with years of experience in cybersecurity, healthcare, manufacturing and other growing fields. Our Graduate Career Management Center and alumni services also support your future success.

Talk with an admissions outreach advisor today to learn more.