The Worst Advice You Could Get as a Data Analyst

Announcement (40% OFF)

Just added the 11 new videos about working with databases to Master Python Fundamentals—The Ultimate Python Course for Beginners.

You will learn how to interact with a database using Python, and you will create an inventory app that interacts with a database.

When you sign up for this course, you will get two books.

• 50 Days of Python: A Challenge a Day, and
• Master Python Fundamentals

Now, you will not just learn Python but will also have enough challenges to tackle as you learn. This Python course is growing with more videos to be added.

Check out this video on viewing data in a database. Click here to watch.

Introduction

“No enemy is worse than bad advice.” ― Sophocles

You do not become a data analyst in isolation; you become one by feeding off information and knowledge from other people. The good news is that there is a lot of information out there (e.g., online forums, social media). The bad news is that, while most of it is helpful, there is a lot of advice that aspiring data analysts must avoid. Aspiring data analysts must be cautious of bad advice that can derail their career growth. In this article, I will look at five pieces of advice that may be counterproductive to your career growth and that you must avoid if you are aspiring to become a data analyst.

1. "Learning Python to Become a Data Analyst is a Waste of Time"

This has to be one of the most ridiculous things that I have heard. What makes it even more ridiculous is that it comes from people who claim to have skin in the game. While it is true that some roles, or those focused on specific domains (e.g., finance), might prioritize other skills like SQL or business acumen and not Python, online job boards like Indeed or Glassdoor show percentages in the range of 60–70% of data analyst jobs listing Python as a requirement or preferred skill. Reports from tech recruiters or training providers suggest even higher numbers, reaching 80% or more. So do not let anyone discourage you from learning Python. In my own experience, adding Python to my resume significantly increased recruiter outreach. Python is the key to future-proofing your career. As the field of data analysis evolves, skills in Python open doors to advanced analytics, machine learning, and data science roles. Neglecting Python can limit career growth opportunities. My advice is that you should learn both SQL and Python.

2. "Just Focus on Visualization Tools"

This is another piece of advice that is absolutely misleading. I would, in fact, label it dangerous. The advice is that you must prioritize learning visualization tools like Tableau or Power BI over SQL and Python. What the advice fails to tell you is that data analysts must interact with databases. So, how are you going to extract data from a database if you do not know how to query databases? SQL is a critical skill for accessing and manipulating data stored in relational databases. Understanding SQL provides a strong foundation for data analysis. Visualization tools often require SQL knowledge to retrieve the data they visualize effectively. To be effective, data analysts need a balance of data extraction (SQL), manipulation (Python), and visualization skills.

Build the Confidence to Tackle Data Analysis Projects

To build a successful data analysis project, one must have skills in data cleaning and preprocessing, visualization, modeling, EDA, and so forth. The main purpose of this book is to ensure that you develop data analysis skills with Python by tackling challenges. By the end, you should be confident enough to take on any data analysis project with Python. Start your 50-day challenge now.

3. "Networking Isn't Important; Just Focus on Your Skills"

My former boss once told me, "Don't spend all your time working on your Excel; get your numbers 80% right and spend the rest of your time interacting with people on your floor." Yes, skills are crucial, but neglecting networking can hinder your career progress. The truth of the matter is that many job openings are filled through referrals and professional networks. Building connections can help you access the hidden job market. So while it is necessary that you improve your skills, ensure that you find time to grow your network. For example, you can grow your network by increasing the number of people that you interact with at work or by attending industry events and conferences. You can also leverage social media to expand your network.

4. "Coding is the Only Skill That Matters"

Strong coding skills are no doubt an important skill for data analysis success. Analysts with strong technical skills have an advantage over those with weak technical skills. However, to argue that coding skills are the only skills that you need to succeed in data analysis is very misleading. A good data analyst must have a well-rounded skill set. The ability to communicate complex findings, solve problems creatively, and understand the business context in which data resides is just as important as your flawless coding skills. As a matter of fact, the whole data analysis process is a storytelling process. In today's world, there is also a strong emphasis on soft skills. These are skills that relate to how well you can work with and interact with others. To be a well-rounded data analyst, work on your coding skills, but do not neglect other important skills such as communication skills.

5. "Fancy Visualizations Are King"

Some people will tell you that you have to make your visualizations as fancy as possible because that is what skilled data analysts do. Don't fall prey to the misconception that "fancy equals skilled." Imagine a chart overloaded with 3D effects and clashing colors. It might grab attention, but will it effectively communicate insights? I have seen some interesting debates online where the focus is on nothing but the aesthetics of the visuals. It is like dazzling data visualizations are the key to successful analysis. What these people fail to tell you is that fancy does not always equate to clarity. They also fail to tell you that the end users of the visualization are more interested in clarity. A well-designed visualization should communicate insights effectively, not just impress with visual effects. The key is to prioritize clarity over aesthetics.

Final Thoughts

By carefully considering the merits of the information you receive, you can develop a well-rounded skill set that positions you for success. So even if you are just starting your career, completely dismissing Python can be shortsighted. Focusing only on visualization tools is not realistic for any data analyst. Improve your coding skills without neglecting other important areas, such as soft skills.

Remember to also dedicate some time to growing your network; the data analysis community is a valuable source of support and collaboration. Thanks for reading.

Newsletter Sponsorship

You can reach a highly engaged audience of over 340,000 tech-savvy subscribers and grow your brand with a newsletter sponsorship. Contact me at [email protected] today to learn more about the sponsorship opportunities.