r/dataanalytics

I've been in analytics for about 8 years (currently a senior DA, more on the strategy/storytelling side than pure engineering) at a company that's gone all-in on AI over the past year, claude code, cursor, skills, agents, etc.

What I'm noticing: a lot of the "traditional" analyst work, pulling data, building dashboards, even a chunk of the insight-generation is getting absorbed by AI tools and self-service agents and skills. Stakeholders can increasingly just ask a tool for the numbers instead of filing a ticket with an analyst.

That's great for efficiency, but it's got me thinking hard about where this leaves the career path. The old ladder was something like: Analyst → Senior Analyst → Staff or Principal Analysist → Analytics Manager → maybe Director. If a good chunk of the "analyst" part of that job is being automated, what does progression look like now?

For those of you with a similar background (analytics-heavy, not classically trained SWE/ML engineers) who've been building with AI/LLMs at work recently where did you end up moving next, or where do you plan to go next? A few things I'd love to hear about:

• What job titles/roles have you (or people you know) transitioned into? (AI PM, ML/AI engineer, "AI analytics lead," analytics engineering, something else entirely?)
• What skills did you actually have to pick up to make that jump, and which ones turned out to be overrated?
• Did you move up within the same company, or did it take a lateral/external move to change your title and scope?
• Is "analyst" as a career track just going to get squeezed out, or does it evolve into something like an "AI insights strategist" type role?

Genuinely trying to figure out how to future-proof my own path here, so any real examples (not just "learn Python") would be hugely helpful. Thanks.

I'm an engineering graduate. After graduation, I spent the last year doing data analytics certifications and building projects. I learned SQL, Excel, Python, Power BI, and statistics, and I've been seriously trying to break into the field.

It's now been a year, and despite applying consistently, I'm getting almost no responses.

Entry-level data analyst jobs seem insanely competitive, many postings get 100+ applicants within an hour, and it feels like the market is heavily saturated for freshers.

At this point, I'm confused and honestly worried.

Should I keep trying in data analytics and improve further, or is it smarter to shift into another career path that has better opportunities and less saturation?

If switching is the better option:

What fields would you recommend for someone with an engineering background and analytics skills?

Should I do another course/certification to move into a better field?

Is data engineering, business analysis, cloud, or something else a better choice right now?

I don't want to waste more time going in the wrong direction. I'd really appreciate honest advice from people working in tech or analytics.

Hey everyone,

I have an entry level customer service finance job I’ve been in where I’m training to get my series 7. I have an interview for a sales and marketing analyst position, but I’m skeptical.

I have a marketing background but I don’t feel confident in the marketing analytics space. Basically, I feel like I have more opportunity pivoting into finance. The role I have now pays 50k a year but if I get an offer with this company I’ll be at 65k actually working in analytics.

I guess I’m asking what the best path is. I feel discouraged as I see analysis is being automated and teams are getting laid off. Especially since marketing is the first area they opt in to layoff first.

My old math professor is off for vacation otherwise I’d ask her expertise but I just need to hear recommendations from others.

Hey , I am thinking of creating a Whatapp group for Learning and Practicing Data Analytics together and finally get placed!

There will be accountability challenges , referrals and obviously knowledge sharing .....

Let me tell me if anyone wants it so...

You are absolutely welcome....

Hello Everyone!!, I just completed a BIG project I have been working for a month and i want your opinion about it.

It's a SpaceX Launch Predictor & Cost Optimizer (A full end-to-end ML system that predicts the probability of a SpaceX Falcon 9 booster landing successfully, enriches launch data with real weather conditions, and exposes the results through an interactive Streamlit web application with a business ROI calculator.)

It Includes Data Pipeline, Advanced Machine Learning Algorithms (with Hyperparameter tuning), Explainability AI (SHAP), MLOps (AWS S3, Docker) and Business Value (ROI Calculator = Financial Results).

FUN FACT: For this project i used my own Evaluation Metric library (standardizes supervised and unsupervised model diagnostics into a single, consistent API), that is also Verified and Published in PYPI Community.

Project Info: https://github.com/Alkiviadisss/SpaceX

Hi everyone,

I'm currently doing the Codebasics Data Analytics Bootcamp and I'm almost done with the Excel module (on the last exercise).

The problem isn't that I don't want to become a Data Analyst—I'm still committed to it—but I'm finding it difficult to stay engaged while studying alone.

In the past, I mostly studied in groups, so there was always discussion, accountability, and motivation. Now I'm learning solo, and after doing many similar Excel exercises, I feel myself getting bored and losing focus.

Has anyone else experienced this?

A few questions:

* How do you stay motivated and consistent when studying alone?

* Do you have any techniques that helped you avoid burnout or boredom?

* What are your favorite websites to practice Excel, Power BI, SQL, or Python for Data Analytics?

* Where do you find Data Analytics hackathons, case competitions, or data challenges to participate in?

I'd really appreciate any advice or resources from people who have been through this.

Thanks!

So i learned count (*), distinct, substring, concat, extract, where (=<>) in, between,like and i have been asking Gemini to give me challenges and i won't say i get all of them right but I'm getting better,now my problem is group by i feel like I'm stuck and can't understand it.

P.S. I am using ChatGPT.

Hi everyone,

I'm feeling a bit lost and could really use some advice from people who've been through this or are already working in the field.

I'm 30 and currently working remotely for a US-based company as an Operations Analyst. My day-to-day work is mostly helping US clients with their job search—updating resumes, applying for jobs, maintaining Excel sheets, and handling different operational tasks. I enjoy the work, but I don't see myself doing this forever.

For quite some time, I've wanted to move into Data Analytics or Business Analytics. The problem is that every time I start researching, I end up more confused than before. Everyone recommends something different.

Some say do the Google Data Analytics Certificate, others say it's outdated. Some recommend Microsoft, IBM, Coursera, Udemy, Maven Analytics, DataCamp, YouTube... and honestly, I don't know what's actually worth my time.

A little about where I am right now:

\- I'm comfortable using Excel, but I know I still have a lot to learn.

\- I'm basically a beginner in SQL, Python, Power BI and Tableau.

\- I don't have a technical or Computer Science background.

\- I work full-time, so realistically I can study around 1-2 hours a day.

\- My goal is to become job-ready within the next 8-12 months.

I'm not looking for the easiest path. I'm looking for the path that actually works.

If you were starting from my position today, what would you do?

\- Which courses would you actually spend money on (if any)?

\- Which skills would you learn first?

\- Should I focus on Data Analytics or Business Analytics?

\- How much do certificates really matter compared to projects?

\- If you had to create a roadmap for someone like me, what would it look like?

I'd really appreciate honest advice, especially from people who made a similar transition or who hire for these roles.

Thanks in advance!

I asked AI for a plan to learn data analytics and this is what it gave me ,do you guys who are alr in the field think it’s useful and if there are things that can be added in it?

1. Excel (1–2 weeks)
Best free option:
ExcelIsFun (YouTube) – Probably the best free Excel channel.
Microsoft Learn – Official tutorials from Microsoft.
Practice: Download datasets from Kaggle and recreate charts and Pivot Tables.

2. SQL (2 weeks)
Learn from:
SQLBolt (interactive and beginner-friendly)
freeCodeCamp SQL Course (YouTube)
Practice on:
HackerRank (SQL)
LeetCode (Database problems)
Aim for 50–100 SQL questions over the two weeks.

3. Python (2 weeks)
Learn from:
freeCodeCamp – Python for Everybody (YouTube)
OR Programming with Mosh – Python for Beginners (YouTube)
Then immediately learn:
pandas
NumPy
Matplotlib
The freeCodeCamp pandas course is excellent.

4. Statistics (throughout)
Best resource:
Khan Academy
Focus only on:
Mean
Median
Standard deviation
Probability
Correlation
Regression
Hypothesis testing

5. Power BI (1 week)
Best free options:
Microsoft Learn (official)
Guy in a Cube (YouTube)
Learn:
Importing data
Dashboards
Visualizations
Basic DAX

6. Git & GitHub (2–3 days)
Learn from:
freeCodeCamp Git & GitHub Course
GitHub Skills (interactive lessons)
Just enough to upload and document your projects.

7. Projects (Last 2 weeks)
Get datasets from:
Kaggle
Data.gov
Our World in Data
Build projects like:
Netflix data analysis
Sales dashboard
Spotify analysis
Cricket statistics
Pakistan inflation analysis

Hi everyone,

I'm looking for honest feedback on my resume as I apply for entry-level data analyst positions.

A little about me:

• I recently completed my M.S. in Data Analytics.
• Before that, I worked as a high school math teacher, so I have experience explaining complex concepts, working with data, and solving problems.
• I'm transitioning into analytics and building a portfolio while continuing to strengthen my SQL, R, Python, Excel, Tableau, and Power BI skills.

Right now I'm focused on creating projects that demonstrate practical business analysis rather than just completing academic projects.

I'd really appreciate any feedback on:

• Whether my resume effectively tells my story as a career changer.
• Skills or projects that employers would expect to see.
• Anything that makes my resume look weaker than it needs to.
• Formatting or wording improvements.
• Any red flags that would keep you from interviewing me.

I know I still have a lot to learn, so I'm looking for constructive criticism. Thank you for taking the time to help!

https://preview.redd.it/qouviryxdxah1.png?width=1328&format=png&auto=webp&s=991478fbbd560a6734c42a7caebeb81510911751

I have sql, Excel, power bi and projects and I am starting to apply for entry level jobs. I have a doubt what should be my next step should I go for python pandas or any cloud skill like aws or Azure? Or should I start Google advanced data analytics course?

Please guide me on this any positive feedback is welcome.

I was just curious what is your starting salary for an entry-level data analyst? I’ve read it’s around 50 to 60 K but I’ve also seen some people say as low as 40 K so I’m just curious what the general idea is

pursue bsda so js wanted yalls opinion on what i should minor in alongside my da major ,maths or physics?

Hello everyone,

I graduated my bachelor degree in business administration and highly interested in finance field in my university period. It was until I internship one of the mining company’s accounting/finance sector. The task was routine eventually I bored bcs of this and I did not see any long term improvement. I like to analyze big datas either in Exel, sql or can be phyton library(but not too much since I do not like heavy coding) and make inside are the company doing well or some specific analysis.

I am thinking data analyst or business analytics can be best suit for me. When i watch youtube video i see data analytics all about Excel, power query or powerbi and simple sql topic and think about is it all about. ??However, i do not know business analytics field what is that in comparison to data analytic??. Is it worth to move one the job right now or i should completely think new things at all. Thanks you for listening to end.

hi! So I have this dataset where the objective is to predict a student's health risk, but I'm a lil confused about how to handle the null values. These are the % of null values for the columns:

             id                          0.000000
health_condition            0.000000
sleep_duration             11.012943
heart_rate                  1.135073
bmi                         2.013946
calorie_expenditure         7.658878
step_count                  2.016554
exercise_duration           1.000017
water_intake                6.300211
diet_type                   1.000017
stress_level               12.000064
sleep_quality               8.452690
physical_activity_level     5.306715
smoking_alcohol             4.141791
gender                      3.097141
dtype: float64id          

What would you recommend I do for these values? If I were to drop the columns <5%, I would be losing nearly 100,000 values (out of 700,000) which I don't think is all that good. I thought of using K-means to fill the null BMI values but I don't know.

I would appreciate any advice! Thanks :)

hey guys so im pursuing data analytics rn and im on my semester break rn for like 2 months and i need ppl to suggest me free courses to do to add to my cv .ut would be rlly helpful thankss ^^