In an era of rapid technological advancements, the role of data analyst is evolving at an unprecedented pace. Luc BarousseSeasoned data analyst and YouTube content creator, offers a comprehensive guide on how to become a data analyst in 2024. Drawing on his diverse experiences in corporate America and working for top influencers like Mr. Beast, Barousse shares valuable insights into the tools and skills needed in this dynamic field.
Core Skills for Future Data Analysts
Before delving into the latest AI tools, Barousse stresses the importance of mastering the foundational skills that remain essential in the field of data analytics. “SQL, or SQL as many call it, tops the list,” he notes. This programming language is essential for communicating with databases, a fundamental aspect of data analysis. According to Barousse, SQL is mentioned in nearly half of all job postings for data analysts, underscoring its importance.
Excel, the ubiquitous spreadsheet software, is a close second. Despite its intended use for ad hoc analysis, many companies rely heavily on Excel for complex data tasks. “Excel is featured in about a third of all job postings, which is a testament to its continued relevance,” Barousse adds.
When it comes to programming languages, Python and R are the most widely used. Barousse points out Python’s versatility, making it suitable for tasks ranging from advanced analytics to machine learning. “Python is almost as popular as Excel, appearing in nearly a third of job postings,” he points out. R, while more specialized, remains a valuable tool for statistical analysis, although it is less commonly used than Python.
Visualization tools like Tableau and Power BI are also essential. These tools allow data analysts to create interactive dashboards and visualizations, helping non-technical stakeholders understand complex data insights. “I’ve spent weeks creating dashboards that help my colleagues make data-driven decisions,” Barousse says.
AI Revolution: Transforming Data Analysis
AI is transforming the data analytics landscape, lowering barriers to entry and improving efficiency. Barousse reflects on his experience building a data analyst portfolio without writing a single line of code, thanks to advances in AI tools. “The barrier to entry to becoming a data analyst and actually analyzing data is getting lower and lower,” he says.
Integrating AI into SQL workflows is a major step forward. Barousse uses GitHub Copilot, an AI-powered coding assistant, to speed up query writing and improve efficiency. “Copilot can autocomplete queries and answer questions about SQL syntax, but I’m exploring other tools that could offer even more functionality,” he says.
Microsoft Excel has also seen some transformative updates. The introduction of Microsoft 365 Copilot, which leverages OpenAI technology, allows users to ask questions about their data and receive insights directly within Excel. Another major feature is the integration of Python, which enables advanced calculations and analytics within the familiar Excel environment. “These updates make Excel more powerful than ever, bridging the gap between traditional spreadsheets and modern data analysis tools,” Barousse says.
The Importance of Learning Python
For those just starting out on their data science journey, Barousse recommends Python as a go-to programming language. “Python is a versatile language that can handle a wide range of tasks, from scraping data to building web applications,” he says. He also notes that AI coding assistants like GitHub Copilot and Google’s Duet AI can help learners quickly grasp Python by providing real-time feedback and code suggestions.
Visualization Tools: Power BI vs. Tableau
When it comes to visualization tools, Barousse favors Power BI because of its integration with Power Query and DAX capabilities. “Power BI makes it easy to clean and analyze data, while Tableau excels in community support and sharing capabilities,” he says. Both tools have benefited from AI enhancements, with Power BI integrating a basic version of Copilot and Tableau developing its own AI capabilities under Salesforce’s Einstein Analytics.
AI Assistants and Job Security
Data analysts often worry about whether AI will replace their jobs. Barousse answers this question by citing a KPMG study that found that more than half of business leaders expect AI to expand their workforce rather than reduce it. “AI is designed to support, not replace, data analysts. It improves productivity and allows us to focus on more complex, value-added tasks,” he says.
A Harvard study confirms this view. It reveals that consultants who use AI are significantly more productive and produce higher quality results than those who do not use AI. “The data is clear: AI is here to improve our jobs, not eliminate them,” concludes Barousse.
As Barousse navigates the changing landscape of data analytics, he remains optimistic about the future. With AI tools streamlining workflows and enhancing capabilities, the role of data analyst is more dynamic and exciting than ever. For those entering the field, embracing these advancements while mastering foundational skills is essential to thriving in this evolving profession.
