How I'd Learn Data Analytics in 2024 | 3 Month Plan

Summary

The video script emphasizes that obtaining a Google Data Analytics certificate alone does not guarantee a job in the competitive field of data analytics. The speaker, Ran, outlines steps for aspiring data analysts to succeed in 2024, including acquiring a learner's mindset, mastering statistics, and learning tools like SQL, Excel, and data visualization. Networking and showcasing projects on platforms like GitHub and Tableau Public are highlighted as crucial for demonstrating expertise and securing a job in the industry.

Takeaways

• 📚 Getting a Google Data Analytics certificate alone does not guarantee a job in data analytics due to the competitive nature of the field.
• 💡 The importance of having a learner's mindset in the rapidly changing data industry, where tools and technologies evolve frequently.
• 🎓 Emphasizes the value of signaling to employers through experience, degrees, or certifications like the Google Data Analyst or IBM certificate.
• 🧮 Stresses the critical role of statistics as a foundational technical skill in data analytics, over languages like SQL or Python.
• 📊 Recommends mastering the fundamentals of statistics, including probability, hypothesis testing, and regression, for a strong analytical base.
• 📈 Advises learning Excel or Google Sheets for smaller scale data analysis and familiarity with pivot tables, lookups, and basic visualizations.
• 🗝️ Highlights SQL as the core skill for data analysis, often a requirement in job interviews, and recommends resources like w3schools.com and Data Camp.
• 📊 Discusses the significance of data visualization as a science and art, recommending learning the principles of storytelling with data before specific tools.
• 🐍📊 Suggests learning either Python or R for data analysis, with Python being more versatile and R being more statistics-focused.
• 🔍 Encourages showcasing skills through projects in specific domains of interest, using platforms like GitHub and personal websites to display work.
• 🤝 Underlines the importance of networking for job referrals and understanding company cultures and project types in the data analytics field.

Q & A

What is the main message the speaker is trying to convey about getting a Google Data Analytics certificate?

The main message is that obtaining a Google Data Analytics certificate alone will not guarantee a job in data analytics. The field is competitive, and one must be willing to continuously learn and adapt to new tools and technologies.

What does the speaker suggest is the most important technical skill for a data analyst?

The speaker emphasizes that statistics is arguably the most important technical skill for a data analyst, as it forms the foundation for data analytics work.

Which two certifications does the speaker recommend for those looking to signal their interest in data analytics to employers?

The speaker recommends the Google Data Analyst certificate and the IBM certificate due to their brand recognition and the confidence that major tech companies have in them.

Why is it important to have a learner's mindset in the field of data analytics according to the speaker?

Having a learner's mindset is crucial because the industry is rapidly changing, with tools becoming outdated quickly. Being open to learning new tools and technologies is essential to staying ahead.

What is the first step the speaker suggests to land a job as a data analyst?

The first step is to signal to employers your interest in the field, which could be through gaining experience, obtaining a relevant degree, or getting a certification.

What does the speaker recommend focusing on after learning statistics?

After learning statistics, the speaker recommends learning Excel or Google Sheets, as they are commonly used for smaller scale data analysis.

Why is SQL considered the 'bread and butter' of data analysis according to the speaker?

SQL is considered the 'bread and butter' because it is a required skill for over 90% of data analysis jobs and is often tested in technical interviews.

What does the speaker suggest about learning data visualization tools like Tableau or PowerBI?

The speaker suggests learning the science behind storytelling with data rather than just the tool itself, as data visualization is both a science and an art.

Why is it beneficial to showcase projects when applying for data analyst positions?

Showcasing projects is beneficial because it demonstrates practical application of skills and can set applicants apart from others who only list tools on their resumes.

How does the speaker recommend networking in the field of data analytics?

The speaker recommends using LinkedIn to connect with alumni or people at desired companies, joining communities like Discord for data analysis, and engaging in organic conversations to build relationships.