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Don’t Learn Python as a Data Analyst (Learn This Instead)

Introduction

Python has built significant momentum over the past 20 years and has become one of the most used data tools out there. It’s free, open-source, and has over 137,000 libraries that make tasks like data handling, visualization, and machine learning easier with tools like Pandas, Matplotlib, and scikit-learn. Yet, despite its power and capability, I don’t recommend it as the first thing you learn when starting out as a data analyst. Here’s why.

Opportunity Cost

Opportunity cost refers to the potential gains you miss out on when you choose one alternative over another. When starting out in data analysis, there are better things to learn first, things that don’t require as much time as Python does. Python's learning curve can be steep. The first six weeks can be particularly challenging, often making you feel like you're making little to no progress. Here are some reasons why:

  1. Installation Challenges: Python can be tricky to install and run, especially for beginners. Most people download it from python.org, but this doesn’t provide an IDE (Interactive Developer Environment), making it hard to actually start coding. Tools like Deepnote, Hex, or the Anaconda distribution can simplify this process by offering built-in IDEs and libraries.

  2. Programming Language Complexity: Learning Python means learning a new programming language, which, like any language, takes time. Concepts like loops, functions, variables, operators, recursion, list comprehension, and namespaces are not easy to grasp initially.

  3. Versatility Overload: Python can do anything—from querying data to building machine learning models. This versatility is both a blessing and a curse because mastering Python would require you to understand all forms of data analysis at once, which is impossible at the start.

Alternatives to Python

BI tools like PowerBI or Tableau offer simpler alternatives and have a less steep learning curve. These tools are very user-friendly, featuring drag-and-drop functionalities similar to PowerPoint. You can become proficient in these tools in about a month, compared to six months with Python.

Python's Popularity in Job Markets

Python is, at best, the third most popular data tool for data analysts. Job scraping data shows that Python is mentioned in fewer entry-level job postings compared to senior-level roles. My guess is Python isn’t mentioned in about 80% of entry-level data analyst job postings. Focusing on learning Python could significantly slow your job hunt.

Get Paid to Learn Python Later

Landing your first data job is the hardest part. Once you’re in, many companies will actually pay you to learn Python. For example, during my time at ExxonMobil, I was allowed to dedicate 1-2 hours each week to study Python using the company’s LinkedIn Learning account.

So, what’s the better option: slowing down your job hunt to learn a skill that isn’t necessary for the majority of entry-level positions, or getting paid to learn it on the job?

If You Still Want to Learn Python

If you decide to learn Python, here’s a suggested roadmap:

  1. Variables and Print Statements
  2. Mathematical Operations
  3. Functions
  4. Loops
  5. IDEs
  6. Libraries: Start with Pandas, Matplotlib, Seaborn, Plotly, and NumPy.
    • How to read data with Pandas
    • Descriptive analytics with Pandas
    • Filtering data with Pandas
    • Data visualization (Seaborn is a good starting point)

If you want more details on what data skills you should be focusing on as a beginner, you can watch the suggested video or check the link provided in the show notes.

Keywords

  • Python
  • Data Analyst
  • Opportunity Cost
  • BI tools
  • PowerBI
  • Tableau
  • Programming Language
  • Learning Curve
  • Installation Challenges
  • Company Training
  • Pandas
  • Matplotlib
  • Seaborn
  • Plotly
  • NumPy

FAQ

Q: Why shouldn’t I learn Python as my first data analyst tool?
A: The opportunity cost is high—there are simpler tools like PowerBI and Tableau that you can learn faster and use to get a job quicker.

Q: What are the challenges of starting with Python?
A: Python has a steep learning curve due to its installation challenges and the complexity of programming concepts.

Q: Are BI tools easier to learn?
A: Yes, tools like PowerBI and Tableau feature drag-and-drop functionalities and can be mastered in about a month.

Q: Is Python necessary for all data analyst jobs?
A: No, Python is mentioned in fewer entry-level job postings compared to other tools. About 80% of entry-level jobs don’t require Python.

Q: Can I learn Python on the job?
A: Yes, many companies will actually pay you to learn Python once you land a job, making it easier to upskill while getting paid.

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