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About Learning to Code for Data Analyst Job:?
My name is Jimmy and I would like to get all the help I can get because I don't seem to memorize how to learn to code in python , Sql, tableau. I seem to feel like I'm not learning anything, do you have any tips , aside from practicing , that can help me learn effectively and what steps to take to become a Data Analyst. Please respond as soon as you can?
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5 answers
Updated
Nehaba’s Answer
It’s completely normal to feel overwhelmed when starting with Python, SQL, and Tableau, especially when things don’t seem to stick right away. I highly recommend that you do not "try" to "memorise" stuff. Aside from consistent practice, I recommend breaking down your learning into smaller, focused goals—such as mastering specific functions or queries—rather than trying to absorb everything at once. Using real-world datasets to apply what you learn can also make the concepts more tangible and easier to remember. Additionally, combining different learning methods like video tutorials, interactive coding platforms, and joining study groups or forums can enhance your understanding. To become a Data Analyst, focus on building a strong foundation in data manipulation, analysis, and visualization, while also improving your problem-solving skills. Lastly, working on small projects or internships can provide valuable hands-on experience. Also, for a UI based tool like Tableau, try to play around with the tool as much as you can with dummy data and give yourself a task to solve - see what kind of data arrangement you need to perform that task, create that data arrangement and again play around... that will help you a lot! All the best!
Updated
Joseph’s Answer
Excel and Tableau are great for visualizing data, but they struggle with very large datasets or complex tasks. Python is crucial for:
1. Automation: It's easy to do a task once in Excel, but running it every Monday at 8:00 AM for 50 regions needs a script.
2. Data Cleaning: Real-world data is messy. Python's Pandas library helps clean, merge, and reshape data in ways that spreadsheets can't handle.
3. Reproducibility: If someone wants to see how you got your results, they can look at your code.
You don't need to be a software engineer, but you should know these areas:
- SQL: Essential for accessing databases to get data.
- Python: Widely used because it's easy to read and has many tools.
Free online course from Alison.com (SQL for Beginners) : A foundational course that covers database schema, operators, and basic-to-intermediate queries (MIN, MAX, COUNT, AVG, SUM).
Free online course from Alison.com (Introduction to Data Analytics with Python: Covers the absolute basics of Python tailored for data tasks, including how to set up the environment and manipulate variables.
1. Automation: It's easy to do a task once in Excel, but running it every Monday at 8:00 AM for 50 regions needs a script.
2. Data Cleaning: Real-world data is messy. Python's Pandas library helps clean, merge, and reshape data in ways that spreadsheets can't handle.
3. Reproducibility: If someone wants to see how you got your results, they can look at your code.
You don't need to be a software engineer, but you should know these areas:
- SQL: Essential for accessing databases to get data.
- Python: Widely used because it's easy to read and has many tools.
Joseph recommends the following next steps:
Hi Joseph! Do you have any tips on learning these learning these languages for Jimmy?
Sharyn Grose, Admin
Updated
Stacy’s Answer
Hi Jimmy,
It's great that you're interested in becoming a data analyst! While I enjoy the coding side of data, I agree with Lauren that data analysis has many parts, so you don't have to focus only on coding if it's not your strength.
A lot of data analytics involves simple tasks like extracting, transforming, and loading data, and cleaning it up to join or append it for your final data set. This doesn't always require coding. The preparation part can be very rewarding! You might also like the business intelligence side, especially if you enjoy data visualization and understanding the story data tells. Many big companies use software with easy interfaces that handle the complicated coding for you, so you don't need to remember all the coding details.
If you ever need to start with a code sample, that's a good approach. It's easier to learn by using code from others who have done what you want to do. You can find code on sites like Stack Overflow and adapt it to fit your needs.
Best wishes,
Stacy
It's great that you're interested in becoming a data analyst! While I enjoy the coding side of data, I agree with Lauren that data analysis has many parts, so you don't have to focus only on coding if it's not your strength.
A lot of data analytics involves simple tasks like extracting, transforming, and loading data, and cleaning it up to join or append it for your final data set. This doesn't always require coding. The preparation part can be very rewarding! You might also like the business intelligence side, especially if you enjoy data visualization and understanding the story data tells. Many big companies use software with easy interfaces that handle the complicated coding for you, so you don't need to remember all the coding details.
If you ever need to start with a code sample, that's a good approach. It's easier to learn by using code from others who have done what you want to do. You can find code on sites like Stack Overflow and adapt it to fit your needs.
Best wishes,
Stacy
Updated
Gabrielle’s Answer
Hello! Starting out it can be difficult to pick up data analysis codes and technology when you do not have a practical way to apply it. It's not just about practicing but having a relevant connection to the work. If you can practice by finding topics that interest you or can relate to, then that helps the info click better. Along with that, I have been a data analysis for 10 years and I still can't always remember all the code, formulas, functions etc I have used. I save all the codes I write. I include comments in it for future reference. If I found useful videos or forums online then I bookmark them. I put everything I have saved in a kind of "cheat sheet" for data, so whenever faced with a problem that I know I have handled before but can't remember the exact syntax then I go to my notes and see what I did before. The point is that you don't always have to have everything memorized as long as you can assess the problem and formulate a strategy to solve it.
Updated
Lauren’s Answer
Hi Jimmy! First, I want to say that you’re definitely not alone in feeling this way. What you’re describing is very common, especially when you’re early in the learning process. I don’t have a magic solution for memorizing code either. I recently finished a master’s degree in data analytics (which you absolutely do NOT need to become a data analyst), and even after coding in multiple classes, I still find it challenging. Coding comes easily to some people, but for many others (including me!) it doesn’t — and that doesn’t mean you’re not capable or that this isn’t the right field for you.
What I can offer is a more holistic way to think about what being a data analyst actually means. “Data analyst” is a broad umbrella term. At its core, the job is about turning data into insights that help people make better decisions. Sometimes that involves coding, but sometimes it doesn’t.
Data analytics can involve several different types of work. Not every data analyst role touches all of these equally (ex. I do no predictive work).
- Data prep: This involves steps of data cleaning and preparation — making sure the data is accurate, consistent, and actually usable. This is a very underappreciated and very important part of data analytics.
- Descriptive analytics: This is the work of summarizing what happened in the past by identifying trends, patterns, and key metrics. (At the most basic level, some terms you may recognize is average, range, mean, median, mode, etc.)
- Predictive analytics: This type of work focuses on what might happen next, often using statistical models.
- Prescriptive analytics: This type of work helps someone answer the question "If I want X result, what should we do to get that?" One tool that is helpful to learn that you may have access to already (or can get it easily) is Excel Solver. It is an add-in app to Excel.
If learning to code doesn’t come naturally to you, I’d encourage you to think carefully about where your strengths are instead (though of course you can keep practicing coding!) Are you good at explaining patterns in plain language? Do you enjoy creating visuals or dashboards that tell a story? Are you detail-oriented and good at spotting inconsistencies in data? Do you like understanding how a business or process works? Those skills are incredibly valuable in analytics roles. I still have to look things up every time I try to code — even with formal training — and that’s true for many working analysts. (And candidly, I don't have to code at my job as a data analyst. I use Excel 90% of the time.)
Good luck! Happy to answer any other questions you have.
What I can offer is a more holistic way to think about what being a data analyst actually means. “Data analyst” is a broad umbrella term. At its core, the job is about turning data into insights that help people make better decisions. Sometimes that involves coding, but sometimes it doesn’t.
Data analytics can involve several different types of work. Not every data analyst role touches all of these equally (ex. I do no predictive work).
- Data prep: This involves steps of data cleaning and preparation — making sure the data is accurate, consistent, and actually usable. This is a very underappreciated and very important part of data analytics.
- Descriptive analytics: This is the work of summarizing what happened in the past by identifying trends, patterns, and key metrics. (At the most basic level, some terms you may recognize is average, range, mean, median, mode, etc.)
- Predictive analytics: This type of work focuses on what might happen next, often using statistical models.
- Prescriptive analytics: This type of work helps someone answer the question "If I want X result, what should we do to get that?" One tool that is helpful to learn that you may have access to already (or can get it easily) is Excel Solver. It is an add-in app to Excel.
If learning to code doesn’t come naturally to you, I’d encourage you to think carefully about where your strengths are instead (though of course you can keep practicing coding!) Are you good at explaining patterns in plain language? Do you enjoy creating visuals or dashboards that tell a story? Are you detail-oriented and good at spotting inconsistencies in data? Do you like understanding how a business or process works? Those skills are incredibly valuable in analytics roles. I still have to look things up every time I try to code — even with formal training — and that’s true for many working analysts. (And candidly, I don't have to code at my job as a data analyst. I use Excel 90% of the time.)
Good luck! Happy to answer any other questions you have.