As a new data analyst, one of the first things you’ll learn is that information is power.
This refers to the art of data analytics itself, of course—it’s all about crunching numbers. But the saying also applies to your career journey. Doing solid research is the best way to make the right choices about the multiple options ahead of you. But with so many resources offering tips for coding, skills development, and other data-related themes, where should you start?
One excellent source of information is Reddit. In a world where so much content is carefully polished, Reddit’s data community offers raw insights from those who’ve been there before you. There’s also a lot of chatter on Reddit! That’s why, in this article, we’ve pulled together some of the best Reddit data advice for beginners. This curated collection sifts through the noise, so you don’t have to.
We’ll cover:
- General tips: Reddit data advice
- How to learn data analysis skills: Reddit data advice
- Which data programming language to start with? Tips from Reddit
- Data analysis coding resources: Reddit data advice
- How to think like a data analyst: Insights from Reddit
- Final thoughts
Want to explore some of the best Reddit advice to guide you out there? Read on.
1. General tips: Reddit data advice
Let’s start with some high-level tips for breaking into data analytics. On the subreddit r/dataanaalysis, one Redditor asks how to help their wife land a role in data analytics.
Questions on this theme are common. Here are a few of the best responses, broadly applicable to any data analytics jobseeker (edited slightly for readability!):
Get an internship
In response to the original poster, one Redditor recommends an internship:
“Many tech companies, including mine, offer internships. Some recruit based on seasonal terms, i.e. spring or fall terms. I don’t know if they care about how long you’ve been out of school, but I’ve seen people in their 40s and 50s in our internship programs so some companies don’t mind about age if that’s another concern.”
While internships aren’t for everyone and don’t always pay well, they can be great for getting spotted by employers. If you’re at college, they sometimes also fit around your studies.
Learn more: How to land a data analytics internship
Apply for as many jobs as possible
The original poster expresses concern that many roles require 1-2 years of experience—a problem inexperienced data analysts often face. This also attracted some helpful responses:
“Apply for roles anyway. Some employers consider a master’s degree equal to 1-2 years of work. Also, a lot of remote applicants either lack basic qualifications (e.g. an undergraduate college degree) or live in another country, which usually isn’t an option. So the competition might not be as stiff as it appears.”
Another remarked:
“Applying for jobs is a numbers game: Apply to as many as possible. It took me 6 months and 240 applications.”
While it’s better not to apply for jobs you’re completely unqualified for, use common sense: it never hurts to try if you’re only missing one or two of the requirements. The worst thing that might happen is that you’ll get rejected.
Grow your network
Finally, yet another Reddit data pro recommended some good networking tips:
“You mentioned that your wife is utilizing her network, but is she also working on growing her network? Reach out to fellow alumni, join Slack communities, and attend local industry events.”
Try to expand your existing network, whether online or offline. You never know where the next opportunity might arise!
2. How to learn data analysis skills: Reddit data advice
Next up, here are some specific tips from the r/analytics subreddit offering excellent myth-busting advice for learning data analysis skills. Here are our top picks from this Reddit.
Don’t overemphasize technical skills
A key point in this thread is that the importance of specific data analytics technical skills, while important, is often over-amplified:
“Most recommendations focus too much on specific technical skills, E.g. “learn PowerBI, SQL, R, etc.” But the best technical skills don’t matter if you can’t translate your data analysis into business decisions.
You did your analysis in Google Sheets but saved the company hundreds of thousands in costs? Well, congrats! Nobody in upper management will ask what cool technologies and coding libraries you used.”
Focus on learning agnostic skills, not specific tools
While there are many tools available, and some will be worth learning later on. So, when setting out, the Reddit data advice is to keep it broad:
“Chances are, in every job, you will have at least some tools for website analytics, dashboards (or whatever) that are not mainstream. Rather than learning the ins and outs of a specific tool […] learn agnostic skills, which you can transfer to different tools.
For example, instead of knowing every detailed feature in Tableau, learn what makes a good dashboard, how it should be structured, and how it provides value to the audience.”
We heartily agree—focus first on learning data analytics theory! The rest will come naturally.
Python is important…but maybe not as important as you think
A third misconception is around the complexity of skills people think they must learn. Initially, learning the basics is enough:
“Even seasoned analysts often won’t use Python that much. Rather, SQL and spreadsheets are often the bread and butter of many analysts and organizations. Of course, Python has many unique advantages and a rightful place in any technology professional’s toolkit for certain specialized use cases. But more often than not it’s used by individuals and organizations to appear ‘advanced’.”
While we still think Python is a great skill to learn, the point remains: focus first on the skills you can’t do without, such as SQL and spreadsheets, and build from there. The Reddit thread goes on to outline a learning framework and other tips for coding that you might find useful.
3. Which data programming language to start with? Tips for coding from Reddit
Okay, so you’ve got the basics down. Now it’s time for some specific reddit data tips for coding. But which programming language to focus on? The subreddit r/datascience has several threads listing coding languages you might want to learn. Here’s what we’ve determined.
SQL, Python, and R are the top programming languages
Many reddit data respondents confirmed what we at CareerFoundry often advise:
“Python/R/both, and SQL, are a solid start.”
“In order of personal preference:
- Python
- SQL
- R (Popular in academia)”
“You need the holy trinity SQL/R/Python”
Search similar threads across Reddit, and this is a solid sentiment. We’d go even further and recommend learning Python as your primary language since it’s a bit easier to grasp the syntax as a beginner.
You should also learn learn SQL (which stands for Structured Query Language and is used to manage and manipulate relational databases). Completely new to coding? Then add R or other more complex languages to your skillset later on, if you wish.
Learn more: What is the easiest programming language to learn?
Shell scripting is an underrated skill
Another skill that comes up a lot—one that isn’t always talked about elsewhere online—is shell scripting. This is clear in the following Reddit exploring useful languages for data scientists and data engineers:
“[Learn] shell scripting, if you count that as a language. I’ve had to understand it a lot when debugging.”
“I second shell scripting, or at least some level of familiarity with shell for cloud computing.”
If you’re not in the know, shell scripting involves creating a set of tasks using commands and scripts that your computer’s operating system (like Windows or Linux) will follow automatically. A shell script is essentially a program in a scripting language that the shell interprets and executes.
You can use these to make your computer handle files, manage programs, or take care of other system-related jobs so you don’t have to do them manually. Common shell scripts include Bash, Zsh (also called Zshell), and the Windows Command Prompt.
4. Data analysis coding resources: Reddit data advice
So you’ve done your research, you’re definitely interested in data analytics and you’ve even picked out a programming language to learn. But where can you find tips for coding, and source datasets and other resources? Here are a few options, as recommended by Reddit.
Seek out online data science resources
The subreddit r/statistics lists many free resources. We’ve reviewed the following and can give them a thumbs up!
“I’ve handpicked more than 60 free online resources to learn data science DataPen.io where you can find resources for data analysis, statistics, machine learning, programming, cheat sheets, and more.”
Find (free and paid) online courses
Meanwhile, a thread on the subreddit r/dataanalysis suggests some additional approaches for new data analysts, including:
“The Google Data Analytics Certification course through Coursera is excellent and inexpensive. [Meanwhile, use] YouTube for Excel, Tableau, & SQL.”
Dig out some tips for coding cheatsheets
Finally, on r/learnprogramming, one helpful Redditor has posted a list of great links and tips for coding:
“This list includes resources I collected and kept as a newbie [programmer/data analyst]. I found these resources to be extremely useful during my initial steps as a programmer and wanted to share them with you. This list includes Python, Java, JavaScript, C++, and related topics.”
So: look for free or cheap courses and resources on sites like GitHub and elsewhere. And if you’re not looking to spend money, YouTube is an excellent free resource (as long as you’re happy to sift through some of the less useful options!) We’ve also gathered our own list of the best free data courses out there.
5. How to think like a data analyst: Insights from Reddit
Finally, you can learn all the tools, hard skills, and programming languages you like. But without the right mindset, you won’t get far. Which begs a fundamental question: How can you learn to think like a data analyst?
On r/dataanalysis, one Redditor asks about how to think more creatively as a data analyst. The following (excerpted) response was upvoted:
“Ask questions about the data you’re looking at. Ask: ‘What does this tell me.’ The questions don’t necessarily have to be interesting but start somewhere. Look at how the data varies and ask the same question with the data split by a measure […]
Come up with a theory, and see if you can prove or disprove it with your data […] As an analyst […] the questions you ask should focus on business outcomes. Think about what a business or service provider would care about—finance (profit/turnover/sales), quality of a product or service, etc. Can you use your data to find any information about these measures?”
Taking this idea a step further, the top-voted response to another, similar-themed thread suggests:
“Start with understanding the KPIs for whatever business or process you are learning. Take a framework approach to solving problems. Don’t just poke around.”
This particular response is accompanied by a dozen useful links to frameworks you can use to start helping you “think” like a data analyst.
6. Final thoughts
As a novice data analyst, Reddit can be a valuable resource. It’s packed with insights, tips for coding, and general advice from experienced individuals who have been on the journey already. The sheer volume of information on the website can be overwhelming to navigate, though. That’s why this article has distilled some of the best Reddit data wisdom for beginners. And even covering these few tips, we’ve barely scratched the surface!
It’s important to carry out your own research and explore Reddit firsthand, too. Remember: being well-informed is the surest way to make smarter career choices, helping you steer clear of well-intended but ill-informed dead ends. And don’t forget: while technical proficiency is vital in data analytics, the broader message we took from Reddit is this: Cultivating analytical thinking and problem-solving skills should be at the top of your priority list!
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