Difference #1: Software Proficiency

Unlike most amateurs, professionals are skilled in using software (such as Python and R) that can handle multiple data formats seamlessly. While tools like MS Paint are limited to images, analytics software can manage a variety of data types—images, tables, audio, text, and much more.

Here's how that same image appears when opened with Python:

And here's how it looks in R:

Difference #2: Managing Large Datasets Efficiently

Another key difference is that professionals can effortlessly work with vast amounts of data. Even though I have over two decades of experience, I often prefer to view a single image in a browser or MS Paint rather than in R or Python. However, if you're tasked with analyzing a million images, using MS Paint at a rate of one second per image would take you over a month of full-time work. In contrast, a professional can accomplish this in mere minutes using the right tools and coding techniques.

Wondering how to get started with these tools? Look up installation guides (both R and Python are free) and experiment. It's similar to MS Paint, but with enhanced capabilities. Simply search online for tasks you wish to perform and follow the guidance.

The first result of a search can provide you with all you need.

For those new to R, your next inquiry might be “How do I install a package in R?” After that, you're on your way. Just copy the provided code, substituting “my image” with your file's name and path. If you're unsure about any terms, look them up. Mastery comes from exploring, and seeking answers is how developers grow.

Engaging in this process will lead to the unexpected realization that you've developed professional software skills over time.

One reason I enjoy programming is that it resembles a mix of magic and LEGO. To learn the necessary 'spells' for your tasks, consult the internet, which itself is a form of data analysis.

In essence, you don’t need formal training. Challenge yourself to explore diverse data formats in R or Python, and continuously seek solutions online as you encounter obstacles. After you access the data (here's how to find data for analysis), formulate a question that interests you and use the tools to find an answer. Begin with small tasks and gradually tackle larger challenges. There's nothing holding you back—embrace the journey!

Difference #3: Critical Perspective on Data

In my view, mastering the tools is the easier aspect; the challenging part lies in adopting an analytical mindset. This includes a critical view of data itself. Professionals develop a certain skepticism towards data—it's not magical but rather a representation of human-recorded information.

Only novices view “data” as something sacred. Professionals have faced enough disappointments to recognize that data is merely a collection of numbers recorded digitally.

The real value of data is its capacity for memory, not its intrinsic quality. Adding statistics to a narrative might impress amateurs, but seasoned analysts are not easily swayed. They remain immune to what I term data science bias, which is the tendency to trust information simply because it appears scientific. A compelling graph accompanying a flawed report won't deceive them.

Experts realize that while data can support opinions, it doesn't make them infallible. W. Edwards Deming famously noted that “without data, you're just another person with an opinion.” However, it's equally true that “with data, you're still just another person with an opinion.” True analysts grasp this concept deeply.

To cultivate this critical mindset, stop venerating data. Hopefully, you've already learned to be discerning with images. For instance, you would not accept an Instagram post as an unbiased depiction of reality unless you took the photo yourself.

Don't treat data as special!

The same common-sense principles you apply to the visual and auditory stimuli around you also pertain to structured data (numbers in a table or spreadsheet). Assuming data equates to truth is akin to blindly accepting everything an author writes without understanding their background. By maintaining a healthy skepticism, you’re well on your way to effective analytics.

If you’ve managed to navigate adulthood without recognizing that not everything online is true, I suggest reconsidering whether analytics is the right career for you.

In addition to practical experience with professional tools, a skilled analyst comprehends the more serious dimensions of the profession, which will be addressed in the next article in this series. Here's a preview of the upcoming topics:

Difference #4: Understanding Career Dynamics Difference #5: Avoiding Data Charlatanism Difference #6: Resisting Confirmation Bias Difference #7: Setting Realistic Expectations for Data Difference #8: Knowing How to Add Value Difference #9: Rethinking Time Management Difference #10: A Nuanced Perspective on Excellence.

If you have other distinctions that don't fit these categories, feel free to share them in the comments!

# Previous Article in This Series Thinking of Becoming a Data Analyst? You’re One Already! --------------------------------------------------------------------- Yes, you. ======================= towardsdatascience.com

# Next Article in This Series Shifting Your Mindset from Amateur to Professional Analyst ----------------------------------------------------------------------- The Journey to Becoming a “Real” Data Analyst =========================================================== towardsdatascience.com

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