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How Important Is Coding for a Data Analyst?
How much of the job is coding in being a data analyst? I am currently a Junior in college and I feel like I am a little behind on my coding skills. I am also wondering what coding languages are dominating the industry and what website I could use to either learn or freshen up on them.
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4 answers
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Sneha’s Answer
Hey Kevin! Coding is definitely important for a data analyst, but don’t worry if you feel behind—there’s always time to improve! While tools like Excel and Tableau are widely used, knowing SQL, Python, and R will give you a huge advantage in data manipulation and analysis. You can practice on Kaggle, DataCamp, or Coursera, which offer great beginner-friendly courses. Keep learning, build small projects, and keep your resume organized. Good luck!
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Ahmed’s Answer
1. Coding Basics are Non-Negotiable
- Learn Python/SQL: Focus on fundamentals (data structures, loops, queries).
- Why? To debug AI outputs, validate code, and collaborate with teams.
- Resources: Use free platforms like Kaggle or DataCamp for bite-sized lessons.
2. AI is Your Assistant, Not Your Replacement
- Master Prompt Engineering:
- Example: "Generate Python code to clean [X] dataset, with step-by-step comments."
- Tools to Leverage: ChatGPT, GitHub Copilot, Google BigQuery ML.
3. Prioritize Problem-Solving & Domain Expertise
- Focus on translating data into insights, not just writing code.
- Build projects (e.g., analyze real-world datasets) to showcase your analytical thinking.
Final Note: AI speeds up tasks, but you still need coding basics to control outcomes. Start small, stay consistent.
- Learn Python/SQL: Focus on fundamentals (data structures, loops, queries).
- Why? To debug AI outputs, validate code, and collaborate with teams.
- Resources: Use free platforms like Kaggle or DataCamp for bite-sized lessons.
2. AI is Your Assistant, Not Your Replacement
- Master Prompt Engineering:
- Example: "Generate Python code to clean [X] dataset, with step-by-step comments."
- Tools to Leverage: ChatGPT, GitHub Copilot, Google BigQuery ML.
3. Prioritize Problem-Solving & Domain Expertise
- Focus on translating data into insights, not just writing code.
- Build projects (e.g., analyze real-world datasets) to showcase your analytical thinking.
Final Note: AI speeds up tasks, but you still need coding basics to control outcomes. Start small, stay consistent.
Updated
Minghao’s Answer
Coding is important for data analysts, especially in SQL and Python. Around 30–70% of the job involves coding tasks like cleaning data, analysis, and automation. Don’t worry if you feel behind—just start learning consistently. Focus on SQL first, then Python. Use sites like LeetCode, freeCodeCamp, DataCamp, or Kaggle to practice. Real projects matter more than perfect code.
Updated
Carlos’s Answer
Hi Kevin,
Coding is definitely important for data analysis, good thing is you can focus on specific languages like Python and SQL that are mainly used in the industry. With that said, there are also many low-code tools that are used for data analysis that can do a lot of the data transformation and preparation work like Excel (enhanced now with Copilot AI tool), Tableau or Power BI, and also AI LLMs can do data analysis like ChatGPT or Google's Gemini, however is important still to know how to code to identify "hallucinations" or when the AI is giving the wrong answer, you can then fix what's wrong in the analysis.
Equally important to data analysis is to know about Probability distributions, Statistics and if you're into Machine Learning, learn the "intuition" or purpose of each technique to know when to apply it.
If you want to improve your data anaysis and coding skills, there are many resources online like Coursera, Udemy, edX or Kaggle. In Coursera you can find good courses that you can take for free (only selecting that you want to "audit" the course with no certificate) and you can see all the lectures. Also Udemy runs very often offers with good courses for 10 USD each. As you can see, there are many resources that you can use if you want to improve your skills on data analysis.
Coding is definitely important for data analysis, good thing is you can focus on specific languages like Python and SQL that are mainly used in the industry. With that said, there are also many low-code tools that are used for data analysis that can do a lot of the data transformation and preparation work like Excel (enhanced now with Copilot AI tool), Tableau or Power BI, and also AI LLMs can do data analysis like ChatGPT or Google's Gemini, however is important still to know how to code to identify "hallucinations" or when the AI is giving the wrong answer, you can then fix what's wrong in the analysis.
Equally important to data analysis is to know about Probability distributions, Statistics and if you're into Machine Learning, learn the "intuition" or purpose of each technique to know when to apply it.
If you want to improve your data anaysis and coding skills, there are many resources online like Coursera, Udemy, edX or Kaggle. In Coursera you can find good courses that you can take for free (only selecting that you want to "audit" the course with no certificate) and you can see all the lectures. Also Udemy runs very often offers with good courses for 10 USD each. As you can see, there are many resources that you can use if you want to improve your skills on data analysis.