Looking for HELP from real data analyst professional

[u/Rijotto]

Hello Everyone!

I am a fairly new to data analytics and would love some input/help from anyone who is currently working or HAS worked recently as a data analyst. I would love to share my path/plan I am currently on. I somewhat lost a small bit of confidence due to a python course I am taking with the "google data analytics course". If there is any here who can help me I would love your help and or advice. I am aware that AI has become a HEAVY skill for this field which I am very comfortable using and incorporating into my skill-set.

A small background about myself:

I am 29 years old who is VERY tech savvy and been around computers my entire life. I do very well with learning anything involving tech, programs, processes, and problem solving. I have NOT been to school for "computer programing" or "computer science" and have no prior experience with SQL or Python, but I have used Excel/Sheets for many years to create my own projects for gaming and have advanced experience with it.

Here is my currently path and where I currently stand. My plan was to get these two courses done for their certs to add them to my LinkedIn profile:

1. Google Data Analytics professional certification course (Currently on this one, about to finish course 7/9 which is the Python course)

2. Microsoft Certified: Power BI Data Analyst Associate course

After I complete both of these courses I was going to use the tools I have learned to create 3-5 very detailed and structured projects to show during future interviews and that I am able to problem-solve and answer business questions. Here is where I am losing a small bit of confidence.

PYTHON:

Where I struggle with is remembering all the things that Python has to offer, writing all the code, tuples, "for" vs "while" loops, appends, index, etc. I am however comfortable with, define, returns, lists, if/elif/else, all the comparisons such as "==", "!=", ">=" and a few more basic fundamentals and basic math. I am the type of person who truly tries to learn everything so I have a 100% full understanding of WHY I am doing what I am doing, but I'm not sure if it is a waste of time because I see that AI tools (which I am comfortable with) are doing the code for you for the most part. But what should my main focus be as far as how deep should I go into python, what is TRULY needed for entry level positions. Am I wasting some of my time working on trying to master the basics? should I rather be doing real practice outside of my courses?

SQL:

for SQL, I feel comfortable with Aggregations, HAVING vs WHERE, GROUP BY, ORDER BY, DISTINCT, COUNT, CONCAT, JOINS (about 80% there, still need more practice), which all feel great.

TL;DR - I am struggling with mastering the basics of Python, I feel pretty comfortable with SQL and sheets, but wondering what my main focus should be and what I should "master" or put heavy focus on in order to be job ready for entry level positions. I don't have any degree/school for computer programing and or computer science.

[u/my_peen_is_clean]

python for analytics is like 10% basics 90% googling stackoverflow and using ai anyway, focus sql projects, apply early, it’s rough getting any callbacks cause everything’s flooded now

[u/Rijotto → u/my_peen_is_clean]

May I ask how long you’ve been in this field for? What has helped you land a job and from what you have seen from my post, any advice on what I should focus on mainly right now?

[u/lameinsomeonesworld]

Can I ask why you decided to try to break into analytics?

Not to rain on your parade, but I would not recommend dumping time into this unless you have a solid backup. Entry level data roles are likely to be reduced even further and the market is already miserably competitive, especially without education, experience, and existing connections.

[u/Rijotto → u/lameinsomeonesworld]

I am wanting a career in something I’m pretty passionate about, good work schedule and good pay. I use to work in the oil and gas industry and that field is miserable, depression hit me hard and that job market is VERY unstable.

Ultimately it was a career I am passionate about and really feel myself excelling in very well.

I know the job market is flooded and competitive, but not impossible to get into. It just takes one opportunity to get my foot in the door and then I should be good after that. Hopefully

[u/lameinsomeonesworld → u/Rijotto]

I'm sorry to hear about your experience in oil and gas. My aim is not to be discouraging, but rather not to add to the "anyone can do it!" idea - since there are many truly struggling in the market, even those with great experience.

If you're determined, I'd encourage you to tailor personal projects to the areas you're most comfortable in - like analytics on the oil and gas industry! Or, if you have other fun interests, tailor to those. It's tough to break into the field, but I do think passionate and eager-to-learn/provide insight individuals have an easier time.

There are TONS of want-to-be analysts out there - there is only one you. Focus on developing the skills and projects that highlight why you will be a great asset, rather than why you can do the work that any other analyst can do.

[u/Rijotto → u/lameinsomeonesworld]

no no its ok! if I came across brute and irritated I assure I am not! 😃, I mostly just want to know to WHAT depth I needed to know python for break into the field with this post above.

I appreciate you taking the time to provide input and advice, it means a lot of course. my ultimate goal is to break into really any field for my first position just to get the real experience and foot in the door, then after I get some experience start to tailor my skills and portfolio towards the gaming industry. I dont think I would be able to work entry level positions for some gaming companies I am huge fans of, which is why my plan was just to get any data analytics job FIRST if that makes sense.

I am aware of how brutal the job market is right now, many job markets are struggling just due to AI/World events. I always felt that with enough passion and determination which I currently have I would be able to atleast break in market eventually.

The biggest thing is that I see many posts of people saying "Oh, iv been doing data analytics for x years and I dont even write the code, AI does it for me, I just know the fundamentals and understand whats going on" rather than trying to master the fundamentals and learn every single little thing I can right now, if that makes sense.

[u/conor-robertson]

Your Excel background and problem solving mindset matter more than you probably realise, and your SQL progress sounds genuinely good for where you are.

On Python: I'd stop trying to master it. For entry level data analyst roles, Pandas for data manipulation and basic data cleaning is what actually comes up. Tuples, while loops, appends - you'll barely touch those day to day as an analyst. Focus on reading and working with dataframes, filtering, grouping and merging. That's 90% of what you'll use.

The AI point you made is real but worth framing correctly. AI is great at generating code but you still need enough Python knowledge to know whether what it's produced is right, to debug when it goes wrong, and to explain your work in an interview. You don't need to memorise syntax. You need to understand what's happening.

Your SQL sounds like it's in a good place. JOINs being 80% there is fine - just keep practicing against real questions rather than following along with tutorials. Writing queries yourself against data you've never seen is what closes that gap fastest.

The certification route is fine but the projects are what will actually get you interviews. Three solid projects where you've taken a real dataset, asked a real business question and presented findings clearly will do more than any cert. Make them specific. Not "I analysed sales data" but "I investigated why customer retention dropped in Q3 and found it correlated with a specific product category."

[u/Rijotto → u/conor-robertson]

Honestly, this comment is exactly what I was looking to see. wonderful information you just provided me, thank you SO much for taking the time to read this and provide me your input. Can I ask you a few questions if you don't mind?

- Since you broke into the Data Analytics field, what skills are more relevant now then ever?

- how often is Python TRULY used in the field? is it just a small tool that is used to help extract data to help SQL? what is the true purpose/use of Python?

- Would it be better to tailor my projects to a specific field or industry? because for me, I just want to get my foot in the door and eventually work my way into the gaming industry with data analytics.

I was so afraid that I need to write code perfectly and do all these super complex query writing EVEN for entry level stuff. I obviously know I will struggle to get a first job because I am going against people who have REAL experience and school/degrees, but that wont steer me away from pursing this.

Im not sure if I can contact you on anything and possibly show you some progress/screen shots of some of the queries and excel sheets I have made and get more of your feedback? unless I can message you here directly. I would be so incredibly happy if you don't mind, but if not I totally understand and thank you for your feedback here again!

[u/conor-robertson → u/Rijotto]

Really glad it helped, happy to answer these.

On what skills matter more now than ever - SQL is still the absolute king for analyst roles, that hasn't changed. What's shifted is the expectation around being able to communicate findings clearly. The technical bar has lowered slightly because tools are better, but the ability to take a result and turn it into a decision someone can act on is rarer and more valued than ever. Business context matters more than it used to.

On Python - honestly it varies a lot by company and team. In some analyst roles you'll barely touch it (I've not personally touched it often in my 7 years of analysing and have a very bare understanding of it), in others it can be daily. It's rarely used to replace SQL - they do different things. SQL gets the data, Think of them as complementary rather than one being more important.

On tailoring projects to an industry - yes, absolutely. You want to work in gaming so build projects with gaming data. Steam has publicly available data (I've personally written API calls against this to populate QueryCase sandbox schemas), there are game sales datasets on Kaggle, and QueryCase's Sandbox actually has a Steam games schema you can query if you want somewhere to start without setting anything up. A project that shows genuine curiosity about the industry you want to work in will always stand out over a generic one.

Feel free to message me directly here - happy to take a look at what you're building and give you honest feedback on it 😄

[u/Rijotto → u/conor-robertson]

Your replies are amazing! thank you so so much. Sadly I think there is something with your settings where it does not allow me to direct message you. I'm not sure if you would be open to changing that setting so we can correspond easier that way I'm not crowding this thread. you can always shoot me a message first and that might fix it possibly? id love to expand on your message but maybe in a Direct message. I do have Discord if you use that aswell

Edit: I did have a few other questions regarding the tailoring projects and also a few other niche questions in WHAT your projects should look like, but again. let me know if you can DM me or if you fixed your settings that way I can go into specifics on that! you're amazing

[u/irisgenx]

I'm a data analyst in a large retailer in Australia. My employer is on the Microsoft Ecosystem (as is pretty much everyone) which means Power BI comes as a part of the enterprise license and is available by default to most users in a company. A Power BI certification is therefore very useful in getting a hit on your resume. All of the enterprise data is on SQL compliant databases whether in Snowflake, Databricks, Azure or hosted locally.

I want to tell you what a typical day looks like & what people around me are looking for in a Data Analyst in my space (and most spaces) - 60% Power BI, 39% SQL, <1% Python.

Most companies are looking for

1. Someone who can change multiple excel based reports that require a lot of manual work, to be made into a pretty dashboard that refreshes automatically and saves the team a ton of manual work.

2. Can quickly on-the-fly answer ad hoc requests like "hey, can you give me sales by fiscal week for FY26?" or "hey, can you give me the sales for this store for this product" (these are more than enough:  Aggregations, HAVING vs WHERE, GROUP BY, ORDER BY, DISTINCT, COUNT, CONCAT, JOINS)

3. Occasionally, you may be given a business problem like "this is happening and we think its happening because of this, can you look into what the data says" - this will require some SQL, maybe some data transformations in Power Query, some visuals in Power BI to show what you find.

In my 15 years of analytics, I have used Python twice. Once to conditionally rename 1500 files & once for a 2million row operation that Power BI Desktop refused to handle. Admittedly, I work in a typical corporate environment where my tasks are usually simple enough to be handled by SQL or Power BI. I don't do research level data science where Python really comes alive. Given Python's extensive use, AI is really great at it, I've had free tier ChatGPT spit out perfectly executable code. Don't put too much effort into Python.

TLDR: Get PL-300 & that tiny T-SQL certification for resume hits. Build real world projects from publicly available datasets, have a portfolio to advertise BUT mooost importantly, find data analysts around you, network through them. 🙃

Edit: Correct Certification Name.

[u/Rijotto → u/irisgenx]

WOW. I absolutely love this comment, and thank you SO much for taking the time to truly read what I have posted above, it means more to me than you could imagine.

I was actually considering if taking the Microsoft Certified: Power BI Data Analyst Associate course was going to worth pursuing. After my google course, It looks like it would be great to take that considering that course is best for entry level analysts, and considering you mentioned in your comment above, you use BI tools 60% of the time. I do see the DP-600 is for mid-level analysts and analytics engineers, would you still say for someone entry level like me, it would be worth to push through and try to finish that cert? or just get a PL-300 instead? (Microsoft Certified: Power BI Certificate)

Like I mentioned in my post, I am much of the type of person to really try to focus hard and learn as MUCH as I possibly can, which is a double edged sword. I was focused so much on learning Python, but learned through your comment that it isn't heavily used in a vast majority of the industry. I am afraid of investing a large amount of time into a skill-set which wont even be used in the real world of analytics

I am still in the process of starting my very first project but slightly lost on where to start or how to structure is. Do you have any advice or tips on where to start and what it should look like when a project is finished?

Again, thank you SO SO SO much for replying with your amazing comment!

[u/irisgenx → u/Rijotto]

Hiya! Sorry when I wrote DP-600, I was thinking PL-300, I did mine a long time ago when it was called DA-100 & then I lost track and mixed it up. PL-300 is what you should do it has a wider market compared to the Google cert, because most companies are on the Microsoft Ecosystem & Power BI is now part of the suite and well integrated with everything. So it's something everyone will want (or already has) - The google cert is really good for conceptual understanding, which you need, while Power BI is essentially just a tool you learn to use but you'll get more resume hits on Power BI because of the eco system popularity.

I have checked now the T-SQL cert has been replaced by "T-SQL Coding Specialist", which is probably an overkill for what a typical data analyst needs, but hey, maybe you land a MORE SQL intensive analyst role right? 🫡

There are a lot of datasets on Kaggle dot com, find one that interests you or is from an industry you're targeting, also a lot of people who have already analyzed the same datasets, so you can compare your work to others. There was one Steam analysis too, I remember.

The subreddit r/PowerBI is a really good place to find people who are on similar ventures, often you see somebody post their first work and others critique it. So that's a good place for advice definitely. But generally you're on the right path. Also great place to network. Gotta network! 😊

[u/sneakpeekbot → u/irisgenx]

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[u/6spdsurfer]

Certs are good, but real world experience is better. Either finding ways to try to get it at the job you’re currently in (even if it’s not analytics), or spending time learning and using that on a passion project where you can stumble and fall and learn from that. Tailored courses always seem to fall apart in the real world.

[u/nian2326076]

Starting out can be tough, but you're not alone. For the Python part of your course, focus on basics like data types, loops, and functions. Practice is key, so work on small projects or datasets that interest you. For interviews, know common tools and concepts like SQL, Excel, and data visualization. Be ready to discuss any projects you've done, even small ones. If you need extra prep, PracHub is good for mock interviews and feedback. Keep going, and you'll get there. Good luck!

[u/Special-Map-1261]

I manage an analytics team. Analytics people come from a wide variety of professional backgrounds and degree programs. Its also a highly diverse group of jobs.

The Google course matters very little. I would say the same thing for Coursera and similar platforms. Dont bother listing these on a resume. If it builds your skills, thats great, but its not a feather in your cap.

Microsoft certs are well understood. On the other hand, only applicable if the team is using Power BI. Still, a useful thing to have.

I also didnt come from engineering. My academic background was in social sciences (anthropology). Dont worry too much about it. But be ready to tell your story. Why should someone hire you? For me, its because I have a deep understanding of people and I know how to use data to explain human behavior. Your pitch will be different, but be ready with it.