Alur Belajar Menjadi Data Analyst 2025 | FREE SUMBER BELAJAR

Summary

This video offers an updated and in-depth guide for aspiring data analysts, focusing on the current role, job prospects, and a structured learning path. It outlines six key stages: understanding data analytics, mastering Excel, studying statistics, learning SQL, visualizing data, and finally, Python programming. Emphasizing hands-on practice and portfolio development, the video stresses the importance of free resources, accessible learning tools, and continuous growth. Whether you are starting fresh or looking to expand your skills, this roadmap helps you become a successful data analyst in today’s data-driven world.

Takeaways

• 😀 Data analysts remain key strategic partners for teams like marketing, operations, and business, providing insights, recommendations, and strategies.
• 😀 AI, like ChatGPT, helps speed up the work of data analysts, but the core job remains the same.
• 😀 According to the World Economic Forum, data-driven jobs will dominate and grow rapidly from 2025 to 2030, creating numerous opportunities.
• 😀 To become a data analyst, understanding what data analysis is and what types of analytics exist is crucial before diving into tools.
• 😀 Excel is an essential tool for beginners due to its simplicity, accessibility, and widespread use across industries.
• 😀 Learning basic statistics, including mean, median, correlation analysis, and regression, is a foundation for data analysis.
• 😀 SQL is vital for data analysts, as it is used daily for querying databases and extracting insights from them.
• 😀 Data visualization is key for presenting data clearly, and learning how to create dashboards is essential for data analysts.
• 😀 Python, though not always required, is an important tool for data analysts, particularly for handling large datasets and performing advanced analytics.
• 😀 Building a portfolio by applying learned skills in real-world projects and case studies is critical for showcasing your abilities to employers.

Q & A

What are the key responsibilities of a data analyst in today's job market?

A data analyst's role remains the same, involving analyzing data to provide insights, recommendations, or strategies. These insights help business teams, marketing teams, and operational teams make informed decisions about new features, campaigns, or business strategies.

How does AI impact the work of a data analyst?

AI, such as tools like GPT, speeds up the work of data analysts by automating certain tasks, but it does not change the fundamental role of a data analyst. Analysts still need to interpret data and provide strategic insights.

What are the job prospects for data analysts in the coming years?

According to the World Economic Forum, data-driven jobs will be the fastest-growing jobs from 2025 to 2030, indicating a strong demand for data specialists. Data analysis remains a valuable career choice with long-term job security.

Why is it important to understand what data analytics is before using tools like Excel or Python?

Understanding the core concept of data analytics is essential because it helps you decide if this field is right for you. It provides the foundation for learning more advanced tools and techniques and helps you grasp the broader scope of data analysis tasks.

Why is Excel considered an important tool for beginner data analysts?

Excel is easy to understand, widely used, and helps build a solid foundation in data analysis. It's essential for beginners because it simplifies concepts like data cleaning, pivot tables, and basic data analysis, making the learning process smoother.

What basic statistical concepts should a data analyst understand?

A data analyst should understand basic statistics such as min, mode, median, quartile, percentile, correlation analysis, hypothesis testing, and regression analysis. These concepts form the backbone of data analysis and help in interpreting data.

What is the role of SQL in the daily work of a data analyst?

SQL is a critical tool for data analysts because it helps them query databases, extract data, and perform data manipulation. Mastery of SQL allows analysts to handle complex datasets and conduct efficient data analysis.

How does data visualization fit into the work of a data analyst?

Data visualization is a key aspect of data analysis. A data analyst must not only learn the tools for creating visualizations and dashboards but also understand how to design them for clarity, making the insights easy to interpret and use by stakeholders.

Is learning Python necessary for data analysts, and why?

While not all data analysts use Python, it is highly recommended because it adds value to an analyst’s skill set. Python is powerful for data manipulation, analysis, and visualization, and it supports libraries like Pandas and Matplotlib for advanced data tasks.

What should a data analyst do after learning the tools and techniques in the learning flow?

After learning the tools and techniques, a data analyst should create projects or portfolios based on real-world datasets to demonstrate their skills. Practicing through case studies and documenting the process helps build a professional portfolio that can be used for career advancement.