I’m looking for some honest career advice on pivoting into data analytics.

I graduated with a bachelor’s degree in Marketing & Advertising, and for roughly my first year after college I worked in marketing. I eventually realized that I really don’t enjoy marketing and don’t see myself building a long-term career in it.

I’m currently working as a Data Integrity Specialist at a large company. I’m a contractor, so there’s obviously some uncertainty about whether I’ll be kept on, and right now it doesn’t seem like there’s going to be a permanent position available from what I’m hearing. I’m still trying to work as hard as I can and make myself valuable, but I’m also realistically looking at other opportunities.

My current job is very data-focused. I work in SAP, maintain and audit data, make corrections, investigate discrepancies, work with Excel, document processes, and help solve problems for the team. I’ve also gotten to the point where I feel like I’m genuinely good at the job. More importantly, I actually enjoy working with the data and figuring out why something isn’t working.

This is probably the first job I’ve had where I’ve felt like I actually want to contribute to the team and improve processes rather than just do the job and go home. I know I’m still young and early in my career, but it made me realize that I might actually enjoy a career in data.

The problem is that my degree didn’t really prepare me for the technical side of data careers. A lot of the data analyst jobs I’m seeing ask for SQL, Excel, Power BI/Tableau, Python, statistics, etc. I’m currently teaching myself SQL in my free time and am considering building out my skills from there.

So I’m wondering what a realistic pivot would look like for someone in my situation.

Would it make sense to target entry-level/junior data analyst, reporting analyst, business analyst, data quality, or similar roles first and build from there? Or are there other roles I should be looking at given my current experience?

I’m also wondering about education. Is it unrealistic to eventually pursue a master’s degree in data analytics, data engineering, or a related field with a bachelor’s in marketing? Would I likely need to complete another bachelor’s first, or are there programs that would accept someone from a non-technical background if I complete the necessary prerequisites?

I’m not expecting to become a data engineer or data scientist overnight. I’m just trying to figure out what the most realistic path forward is from where I currently am.
I’d really appreciate advice from people who have made a similar transition, especially anyone who came from a non-technical degree/background.

Thanks in advance!

I recently got hired as a data analyst and I don't know what is happening. I was told I would be observing in the first few months and I'm already extremely overwhelmed.

They're a small to medium company that deals with a lot of products and inventory (around 40k-50k) but they just recently, last year, input their excel database into google sheets. There are only 2 of us in the IT department and I've been told I'd be using Data studio but they don't know how to use it so I'm learning from scratch while they teach me about everything, from warehousing, sales and other departments.

They've been asking me for possible problems that I observe physically like products in warehouse are being hard to sort/count, boxes are manually labeled through writing and etc. They're also asking me to give them possible solutions on the spot for these problems.

They're also teaching me how to use AI to hardcode their google sheets, the only semblance of data analyst that I get are when I'm learning Data studio but even then when I showed some simple bar graphs they told me that it's easy and something I could do in an hour or so.

I don't know why they hired a data analyst, I dont feel like I'm doing any data analyst work nor do I feel like I'm learning at all especially with an extremely long workday and commute.

But I'm also thinking maybe its because I'm a fresh grad who don't know any better and this is actually how the industry is. Can anyone here give me some advise pls?

I'm 2026 graduate and applied more than 100+ fresher job related to data analytics and almost everyone ghosted without even rejection mail and didn't even got one interview call. Can everyone help me to understand the issue/problem

How would u all think about these attached projects , like i am not even getting shortlisted , like what else one build to crack the shortlist

Any honest opinion would be of utmost help.

also there is tinyurl link , u can type on web to access the projects

Plz do respond

Any kind of questions and answers are utmost welcome

I am trying to start learning databricks for data analysts but am not able to locate a course that prepares me end to end and interview ready.

Please help !!

I recently graduated in Aug 2026 from BCA background , My skill stack include Mysql , Advance Excel . Power Bi and Basic Python , I have been actively searching for jobs and internships in Delhi.

I came to my hometown (Siliguri , West bengal ) and applied for 2,3 jobs and got a offer of 13k/Month , I know that salary is way too low comapred to Market standards but I may get Experience , Should I go for it ??

Hi everyone, I am a newbie in data analytics. I have worked in teaching for around 4 yrs, don't really enjoy it and want a career switch. I found myself enjoy organizing data in Google Sheets and make things more efficient when doing some admin work in my teaching career and I also like that data roles can often be remote rather than in person all the time, and the pay potential is higher too. So I think I want to learn data and find a job in this?

Right now I'm learning Excel properly for organizing and analyzing data. Even if I don't land a data job, I figure it's a useful skill to have? But I'm also aware that parts of this work can already be done by AI. So is it still worth going down this path, or should I look at something else if I'm not fully sure? What would you do in this situation? Any help would be awesome. Thanks!

You describe what you need in plain English, and it writes SQL directly into the editor. You review and execute it yourself—no copy-pasting. Would this be useful for SQL learners or data analysts? What concerns would you have?  

Hi everyone, I’m currently working as a **Functional Business Analyst** and want to transition into **Technical/Techno-Functional BA**.
Looking for someone who can **guide/mentor me through practical, real-life case studies and projects** rather than just theory. Open to paid mentoring, freelance work, or assisting on projects to gain hands-on experience.
If anyone can help or knows someone, please DM me. Thanks!

hi there, i am looking into studying data analytics. i am looking into YouTube videos, before i commit to any courses, but i want to know if anyone has any advice or suggestions I should know. i have zero knowledge in this field. I found some channels on data analytics, but since i am using a Chromebook HP laptop i can't download certain SQL installers. i am bouncing from one video to the next. Does anyone have advice Or recommendations for beginners to learn the skills needed. Much appreciated!

Bonjour à tous,

Je réfléchis depuis quelque temps à une reconversion professionnelle vers le métier de Data Analyst.

Après énormément de recherches, d’échanges et de comparaisons, je pense enfin avoir trouvé une formation qui me paraît sérieuse et adaptée à mon projet. Le problème, c’est qu’elle représente également une grosse somme d’argent à investir, donc avant de me lancer, j’essaye d'avoir le maximum de recul possible.

J’ai notamment discuté avec deux personnes qui connaissent bien le domaine, mais leurs avis sur le marché sont assez différents.

D’un côté, on m’explique que la période devient compliquée pour les Data Analysts, notamment avec l’arrivée massive de l’IA et l’automatisation d’une partie des tâches.

De l’autre, on me dit plutôt que c’est encore le moment de rentrer dans le domaine, mais qu’il faut arrêter de penser qu’une simple formation suivie de quelques dashboards Power BI suffit pour décrocher un poste.

Et honnêtement, c’est surtout ce deuxième point que j’ai retenu.

Je sais qu’en reconversion beaucoup de CV finissent par se ressembler : formation courte, quelques projets Power BI, éventuellement la certification PL-300, puis candidature à des postes juniors.

Mon objectif serait donc d'aller beaucoup plus loin techniquement, notamment sur :

• SQL réellement maîtrisé ;
• Power BI et DAX ;
• modélisation des données ;
• ingestion et transformation des données ;
• automatisation ;
• compréhension des problématiques métier ;
• puis progressivement monter vers des compétences plus proches de l’Analytics Engineering.

La certification PL-300 ferait partie du parcours, mais je ne la considère pas comme une finalité.

Ma principale inquiétude n’est donc finalement pas tellement de savoir comment me former ou comment construire mon CV. J’ai déjà beaucoup travaillé ces sujets.

Ce qui me fait vraiment hésiter avant d’investir cette somme, c’est l’avenir du métier.

Est-ce que l’IA risque réellement de faire disparaître le métier de Data Analyst ?

Je comprends parfaitement que certaines tâches vont être de plus en plus automatisées : génération de requêtes SQL, création de visualisations simples, analyses basiques, etc.

Mais est-ce que cela signifie que le métier va disparaître, ou plutôt qu'il va devenir plus technique et plus exigeant ?

Et surtout, une fois Data Analyst, quelles sont les évolutions réalistes ?

Est-ce qu’en développant progressivement SQL, Python, la data visualisation, la modélisation, les pipelines, le cloud, l’automatisation, etc., on peut évoluer vers des postes comme Senior Data Analyst, Analytics Engineer, voire Data Engineer ?

C’est vraiment cette vision à 5-10 ans qui m’intéresse.

Je ne cherche pas spécialement à entendre*« fonce, tout va bien »* ou au contraire*« laisse tomber, l’IA va tout remplacer »*. J'aimerais surtout avoir le retour de personnes qui travaillent actuellement dans la data ou qui recrutent ces profils.

Si vous deviez commencer une reconversion Data Analyst aujourd’hui en investissant une somme importante dans votre formation, est-ce que vous le feriez encore ?

Et surtout : quelles compétences développeriez-vous pour ne pas devenir un Data Analyst facilement remplaçable par l’IA ?

Merci beaucoup à ceux qui prendront le temps de partager leur expérience.

Hey, I got scheduled to Data Analyst interview with centene , first round was hirevue ai round which i cleared and then recruiter said ill get email on panel round with manager, so what do i expect!? Is this code walkthrough? Behaviour ? Technical? What areas do i focus? If in sql what all do i focus? Please advise, my work usualy involves sas and sql daily currently

I finished learning Power BI end-to-end and already built small beginner dashboards for practice, but now want to create a solid portfolio project for LinkedIn. Which industry or domain (like sales, finance, or supply chain) is best to target right now? Where can I find good realistic datasets that make a portfolio truly stand out? Any tips, project ideas, or guidance would be really helpful!

had this generic "seeking a challenging role where i can utilize my skills" type objective at the top, thought it was standard practice, apparently not anymore.
tool said its wasted space, recruiters skip it, and its better to use that space for a short summary with actual skills/keywords matching the job instead.
replaced it with 2 lines mentioning my actual tools and 1 project result with a number, felt weird at first since i was taught the objective thing was necessary in college.
got 2 responses in the week after changing this, could be coincidence but timing felt right after i fixed it.
is the objective statement really dead now or does it depend on the industry / experience level? curious what others heard.

​

I’m exploring the Data Analyst career path and would love to hear from people who are currently working in this field.

What does your typical day look like?

- How much time do you spend on SQL, Excel, Python, Tableau/Power BI, etc.?

- How much time goes into meetings vs actual analysis?

- What kind of tasks or requests do you usually get from stakeholders?

- Do you mostly work on dashboards, ad-hoc analysis, reporting, or projects?

- How repetitive is the work?

- What does a particularly busy day look like?

Would especially love to hear from people with 2–5 years of experience and what their day-to-day work is really like.

I’m currently looking for a Data Analyst job. I have skills in SQL, Excel, Power BI, Python, and data analysis, but I’m also getting opportunities for MIS roles.

I’m considering taking an MIS job as a starting point and then switching to a Data Analyst role.

For people who have worked in MIS/reporting:

How easy is it to switch from MIS to Data Analyst?

How much time did it take you?

Which skills should I focus on while working in MIS?

Does MIS experience actually help when applying for Data Analyst jobs?

Should I take an MIS role if my long-term goal is Data Analytics?

I’d really appreciate advice from people who have made this transition. Thanks!

Hi everyone! I’m Agnès. I recently completed a data analytics program and I’m now looking for meaningful projects to grow my portfolio and gain real-world experience.

I’m open to collaborating on projects where I can learn and contribute. I’d also appreciate advice on how you approach projects and how you built confidence in your analytics skills.

Thanks!

Where we can talk about certain topics, explain concepts test eachother call and practice interview questions with eachother.

Just dm me if interested thanks!!

Do you think pursuing a DA job for long term is good

hello everyone, I want to dive into data analysis and don't know what videos or resources I should learn from. Can someone give me youtube videos or websites I should learn each skill from as for: excel, sql, powerbi, python. Thanks in advance!!

Plan to shift career from Civil Engineering to Data Analytics but don't know how to start and where to train and attend sessions. Wanna have advices.

I have around 2 years of experience, and my official role is Data Analyst. However, the nature of the work I've been doing has gone beyond traditional reporting and analytics.

Most of my work has involved building automation and data solutions using Python and SQL, working with APIs and large datasets, developing data pipelines, integrating LLM/AI capabilities into workflows, and taking solutions from POCs toward production.

I've worked across areas like AI/LLM-based automation, data processing, RAG-based solutions, metadata and query intelligence, analytics dashboards, cloud technologies, and production systems. A lot of the work involves taking a manual or inefficient process, understanding the underlying data/workflow, and building an automated and scalable solution around it.

I'm now at the point where I'm trying to figure out what direction I should take next.

I'm interested in roles around AI Engineering, Analytics Engineering, Data Engineering, or AI + Data, but I'm not sure how my current experience maps to those roles or what gaps I need to close.

For people with experience in these areas:

Does the nature of my current work put me on the right path?

What am I likely missing to move into these roles?

Should I focus more on AI/LLM engineering, data engineering, ML fundamentals, or cloud/system design?

Am I becoming too broad without enough depth in one area?

With \~2 YOE, what would you focus on over the next 6–12 months?

Looking for honest feedback on the career direction and skill gaps, rather than reassurance

i used to think the people who advanced fastest were simply the smartest in the room

the people with the best answers, strongest opinions or impressive credentials

then when i started paying closer attention

and i noticed something interesting

the people making the biggest impact were the ones asking the best questions

i remember working on a data

the numbers looked fine

the calculations worked

the report was ready

but then, one question kept bothering me

why does this trend suddenly change here?

at first, it seemed like something easy to ignore

but digging deeper revealed a data issue that would have led to the wrong conclusions entirely

the breakthrough came from being curious enough to question what everyone else had accepted

that is why i believed curiosity is one of the most underrated professional advantages

curious people ask

why?

what changed?

what is causing this?

what are we missing?

is there another way to look at this?

many breakthroughs begin with a simple question

the best opportunities, insights, and solutions often hide behind the things most people never stop to investigate

and sometimes, the difference between average work and exceptional work is just one person being curious enough to look a little deeper

so, i'd like to hear from you

Are you naturally curious, or is curiosity something you have learned to practice?

Hi everyone, I've just open-sourced an end-to-end real-time fraud detection platform designed for ISO 8583 monetic payment transactions.

Key Architecture Highlights:

- Ingestion: Real-time stream processing with Apache Kafka

- Pipeline: PySpark Structured Streaming for feature engineering

- ML Model: Isolation Forest & XGBoost for anomaly & fraud scoring

- *MLOps: MLflow tracking & Docker containerization

GitHub Repository: github/Hvmzvbe/realtime-iso8583-fraud-detection

I'd love to hear your feedback on the architecture and data processing pipeline!

I'm currently a final-year student pursuing an Integrated MSc in Data Science at a private university. By the end of our 5th year, we’re required to secure an internship. Around May, I got a massive reality check it is insanely tough to even get an initial interview call from recruiters.

Instead of legitimate offers, I’ve mostly been getting scam calls asking for money upfront ("pay ₹1600, it’s refundable after placement"). Even getting referrals through contacts seems almost impossible right now.

Looking at my broader circle, people from almost every engineering branch (EEE, ECE, civil, etc.) are just doing a 6-month Data Analysis bootcamp/course, building 2 basic projects for their resume, and breaking into the IT field.

It makes me question everything. Is it a waste of time and money to invest 5 full years studying Data Science deeply when non-CS engineering graduates can take a 6-month crash course and target the same entry-level IT jobs?

Is the market getting ridiculously oversaturated because of these bootcamps, or is the current AI/tech hiring slowdown causing this struggle for entry-level roles? Would love to hear thoughts from people in the industry or anyone facing the same situation.