How I’d Become a Data Analyst in 2025 | AI, skills, roadmap

Summary

In this video, the speaker provides a clear roadmap for beginners entering the data analysis field. They emphasize focusing on mastering three core skills—Excel, SQL, and BI tools (Tableau/PowerBI)—and stress the importance of going deep into these skills rather than spreading oneself too thin. The speaker also explains how AI can aid in the process, specifically through prompt engineering, and offers advice on how to effectively approach learning. Additionally, they caution against diving into Python or R too soon and advocate for learning through hands-on projects and obtaining relevant certifications to enhance credibility.

Takeaways

• 😀 Focus on mastering a few core skills instead of trying to learn everything at once. This is the concept of T-shaped learning.
• 😀 AI won't take your job as an analyst, but it can help accelerate your growth if used appropriately. View AI as a personal assistant rather than just a tool for answers.
• 😀 Learn prompt engineering to effectively work with AI. The better you are at providing context and specificity, the more useful AI becomes.
• 😀 Excel is the most widely used technical tool in data analysis. It's versatile and crucial for ad hoc analysis, and understanding advanced features like Power Query and DAX can make you a stronger user.
• 😀 SQL is essential for any data analyst. It is the language for interacting with databases and is fundamental for querying, cleaning, and analyzing data.
• 😀 Master the big six SQL functions (select, from, where, group by, order by, having) and practice writing SQL queries regularly to prepare for live coding scenarios in interviews.
• 😀 Business Intelligence (BI) tools like PowerBI and Tableau are key for creating interactive dashboards and visualizing data. Learn data modeling, chart selection, and the balance between aesthetics and functionality.
• 😀 Don't focus on programming languages like Python or R when starting. These tools are useful but are not necessary at the beginner level. Master Excel, SQL, and BI tools first.
• 😀 Avoid 'course purgatory.' Focus on building projects to demonstrate your skills and apply them in real-world scenarios instead of just consuming information.
• 😀 Consistently adding new projects to your portfolio and sharing them online (e.g., on LinkedIn) increases your visibility and improves your chances of getting hired.
• 😀 Certifications can provide credibility, but don’t overdo it. A few targeted certifications like Microsoft PL300 for PowerBI or Tableau Desktop Specialist can make you stand out.
• 😀 The learning process is about quality and consistency. Focus on building projects and practicing skills regularly, and don't rush into learning too many tools at once.

Q & A

What is the main challenge people face when starting a career in data analysis?

The main challenge is feeling overwhelmed by the many skills and certifications to learn, with uncertainty about where to start and what actually matters in the beginning.

What is T-shaped learning, and why is it emphasized in this video?

T-shaped learning involves going deep into a few core skills rather than being superficial with many. It is emphasized because it allows for a solid foundation and more effective growth in the field.

How should AI be used in data analysis according to the script?

AI should be viewed as a tool to assist and accelerate growth. It won't replace analysts but can make tasks like writing code easier. Understanding prompt engineering and engaging in conversation with AI is important to leverage it effectively.

What is prompt engineering, and why is it important for data analysts?

Prompt engineering is the skill of crafting specific, context-rich prompts when interacting with AI. It’s important because it helps the AI provide more relevant and useful responses, which can aid in problem-solving tasks like writing SQL queries.

What are the three core skills a data analyst should master first?

The three core skills are Excel, SQL, and BI tools (like PowerBI or Tableau). These form the foundational technical abilities needed for data analysis.

Why is Excel considered an essential skill for data analysts?

Excel is widely used across industries and offers versatility. It can handle a variety of tasks from simple ad hoc analysis to more advanced functions with tools like Power Query, Power Pivot, and DAX.

What are some important SQL concepts to learn for data analysis?

Some important SQL concepts to learn include the basic six (SELECT, FROM, WHERE, GROUP BY, ORDER BY, HAVING), joins, case statements, and later, more advanced concepts like window functions, CTEs, and subqueries.

Why is SQL practice essential for data analysts?

Practice is crucial because SQL is often tested in interviews and is a critical tool for querying, cleaning, and analyzing data. Being comfortable with SQL in live coding environments is necessary to perform well in interviews.

What is the role of BI tools in data analysis?

BI tools, such as PowerBI and Tableau, are used to create interactive dashboards and visualize data. These tools help analysts communicate insights and tell a story with the data. Learning to use these tools effectively is vital for data analysts.

Why shouldn't data analysts focus on Python or R right away?

Python and R are valuable languages but are not as essential for beginner-level data analyst roles. Focusing on Excel, SQL, and BI tools first allows for a deeper understanding of core skills, which is more effective than spreading oneself too thin across multiple tools.

What is the recommended approach to learning data analysis skills?

The recommended approach is to focus on building projects rather than just completing courses. Projects help reinforce skills and provide tangible evidence of your abilities for job applications. Continuously adding new projects to your portfolio and showcasing them online, like on LinkedIn, can help increase your visibility and job prospects.

What role do certifications play in a data analyst's career development?

Certifications can add credibility to a data analyst's skill set, but it's important not to overwhelm a resume with too many. Earning a few key certifications, like the Microsoft PL300 for PowerBI or the Tableau Desktop Specialist, can enhance your profile without overloading it.

How can posting projects online help with job search efforts?

Posting projects on platforms like LinkedIn can increase exposure and attract recruiters. It also serves as a conversation starter during interviews, showcasing practical experience and technical skills.