How I Would Become a Data Analyst In 2025 (if I had to start over again)

Summary

In this video, the speaker outlines a streamlined approach to becoming a data analyst in 2025, emphasizing speed and efficiency. Following the SPN method, the steps include understanding various data roles, learning key tools like Excel, Tableau, and SQL, building relevant projects, and creating a strong portfolio. Networking, optimizing LinkedIn and resumes, and applying for jobs are also highlighted. With an emphasis on doing the least amount of work while maximizing results, the speaker shares practical advice for landing your first data job. The process is designed to be fast, simple, and community-driven for optimal success.

Takeaways

• 😀 Understand the variety of data roles available: There are numerous roles in the data world that are similar to a data analyst, such as business intelligence analyst, financial analyst, and healthcare analyst.
• 😀 🌱 Focus on the most in-demand skills: Excel, Tableau, and SQL are the key skills to prioritize when starting out as a data analyst. These are the 'low-hanging fruit' that are easiest to learn and most useful.
• 😀 🐍 Python is not essential initially: Python is only required in 30% of data analyst roles, and it takes longer to master. It’s best to save it for after landing your first job.
• 😀 📊 Build projects to demonstrate skills: Projects are essential for showcasing your abilities. By creating a portfolio of projects, you provide tangible evidence that you can perform as a data analyst.
• 😀 🧠 Focus on realistic learning: Build projects with real-world data, as it provides a more authentic learning experience compared to controlled exercises in online courses.
• 😀 📈 Use platforms like Kaggle for data sets: Kaggle is a great resource for finding high-quality, free data sets that can be used to build your projects.
• 😀 🌍 Diversify project types: Work on projects from various industries (e.g., business, healthcare, logistics) to create a diverse portfolio that appeals to different companies.
• 😀 🌐 Create a portfolio for your projects: Use platforms like LinkedIn, GitHub Pages, or Carrd to showcase your work. A portfolio increases your chances of being noticed by recruiters.
• 😀 📅 Optimize your LinkedIn and resume early: A well-optimized LinkedIn profile and resume are key to getting noticed by recruiters and getting past applicant tracking systems (ATS).
• 😀 📝 Start applying for jobs early: Don't wait until you feel fully prepared. Apply for jobs as soon as you're confident in your skills. Use a variety of job platforms, not just LinkedIn.
• 😀 🤝 Networking is crucial: Networking, both online (LinkedIn) and through personal connections, can greatly increase your chances of landing an interview and a job.
• 😀 🔄 Prepare for interviews: Be ready for both technical (e.g., Excel, SQL, Tableau) and behavioral (e.g., using the STAR method) interviews to confidently navigate the hiring process.

Q & A

What are the main roles in the data analytics field that are similar to a data analyst?

Some of the roles similar to a data analyst include business intelligence analyst, business intelligence engineer, technical data analyst, business analyst, healthcare analyst, risk analyst, and price analyst. While these roles have slight differences, they share similar responsibilities related to analyzing and interpreting data.

Why should you focus on learning Excel, Tableau, and SQL when starting as a data analyst?

Excel, Tableau, and SQL are considered the 'low-hanging fruit' in data analytics because they are easy to learn, widely used, and highly effective in most data analyst roles. Mastering these tools is sufficient to land your first data job without overwhelming yourself with more complex tools like Python.

Why is Python not necessary for landing a data analyst job initially?

Although Python is powerful and popular, it is only required in about 30% of data analyst roles. Learning Python takes significant time and effort, especially for those without a programming background. Therefore, it’s recommended to focus on tools like Excel, Tableau, and SQL for the first job, saving Python for later career growth.

How can building projects help when trying to become a data analyst?

Building projects provides tangible evidence of your skills, showing potential employers that you can perform the tasks required for a data analyst role. These projects, such as analyzing datasets and publishing your results, help break the 'cycle of doom' where a lack of experience prevents you from landing a job.

What are some good places to find data sets for projects?

Kaggle is one of the best places to find a variety of data sets for projects. Other platforms may also offer data, but Kaggle is highly recommended due to its vast and diverse collection, making it a great resource for building realistic data analysis projects.

What is the importance of a portfolio when applying for data analyst jobs?

A portfolio is essential for showcasing your projects to potential employers. It serves as a public display of your work and skills, helping hiring managers and recruiters assess your capabilities. Platforms like LinkedIn, GitHub Pages, and Carrd are great for creating a professional online portfolio.

Why is networking important in the job search process for data analysts?

Networking is crucial because it can often lead to job opportunities through connections. Many people land jobs not through applications, but because they know someone who works at the company. Networking can be done through LinkedIn, talking to friends and family, and building relationships in the data analytics field.

What should be included in an optimized LinkedIn profile for job seekers in data analytics?

An optimized LinkedIn profile should include a clear, professional summary, relevant skills like Excel, Tableau, and SQL, and projects you've worked on. Additionally, make sure your profile is set to 'open to work' so recruiters and hiring managers can easily find you and contact you for relevant roles.

How can job seekers optimize their resumes for Applicant Tracking Systems (ATS)?

To optimize a resume for ATS, avoid using columns or tables and focus on using keywords from job descriptions. ATS scans resumes for specific keywords, so aligning your resume with the language of the job listing increases your chances of passing the automated screening process.

What are the two main types of interviews for data analyst roles, and how do they differ?

The two main types of interviews are technical and behavioral. In technical interviews, candidates are asked to solve problems or answer questions related to data analysis tools, like SQL or Excel. Behavioral interviews assess how candidates would fit into the company culture and their past experiences, often using the STAR method (Situation, Task, Action, Result) to guide responses.