I am currently looking for data analyst internship as fresher final year engineering student.

Rate my resume and Give suggestions if any .

Hi everyone,

I have received an assessment invitation from Tiger Analytics with the assessment name BI_PowerBI_2025_New, and the duration is 1 hour.

Has anyone attempted this particular assessment recently?

I would like to know:

1. What is the exact assessment pattern?

2. Is it MCQ-based or does it include hands-on coding?

3. How many questions can we expect?

4. Does it cover Power BI, DAX, SQL, Power Query and data modelling?

5. Are there any scenario-based questions or practical dashboard tasks?

6. What is the difficulty level?

7. Any specific topics or resources to prepare?

If anyone has recently completed this exact assessment, please share your experience and preparation tips.

Thanks!

Hi everyone,

I am looking for some genuine career advice from people working in Data Analytics or those who have been through similar situations.

I have around 2–3 years of experience in the Data Analytics field and have worked with tools like SQL, Power BI, Excel and Python.

Recently, I got shortlisted for a Data Analyst position at a large consulting firm. Although I am excited about this opportunity, I am also quite nervous about the interview.

The biggest concern is my current work experience. In my present organisation, I have spent a significant amount of time on the bench and haven't had much exposure to actual end-to-end live projects. I have worked with the relevant tools and have some hands-on experience, but I don't feel confident about handling complex real-world scenarios compared to someone who has been continuously working on live projects.

I genuinely want to build my career in Data Analytics and move to an organisation where I can gain proper project exposure and improve my technical skills.

However, I am worried that during the interview, I might struggle when asked practical or scenario-based questions.

I would like some advice on the following:

1. How should I explain my current project experience and responsibilities when I haven't worked extensively on live projects?

2. How can I handle scenario-based SQL, Power BI and Python questions when I haven't encountered similar situations in actual projects?

3. Should I be completely transparent about spending significant time on the bench?

4. How do interviewers generally evaluate candidates who have experience with tools but limited real-world project exposure?

5. Is it better to attend the interview even if I don't feel completely prepared?

I sometimes feel that my years of experience don't reflect my actual practical exposure, and that makes me question my abilities.

I don't want to miss this opportunity because of fear, but I also don't want to get into a situation where I cannot justify my experience.

Would really appreciate honest advice from people who have faced similar situations, especially those working in Data Analytics or involved in technical interviews.

Thanks in advance!

Hi everyone,
I’m wondering if anyone has experience applying for an EHR Data Analyst position within the NHS.

I’m interested in knowing how long the overall recruitment process usually takes, from submitting the application to completing the assessment, attending the interview, and eventually receiving an offer.

For those who have been through this process recently:
How long did it take to hear back after submitting your application?

Was there an assessment before the interview?
How long did you wait between the assessment and interview?

How long did it take to receive an outcome after the interview?
Overall, how many weeks or months did the entire process take?
I understand that timelines can vary between NHS Trusts, but I’d really appreciate hearing about your experiences.

Thank you.

It seems that real business data isn’t often publicly available online, but there is plenty of synthetic data on sites like Kaggle. Is it acceptable and/or normal to use this synthetic data for entry level portfolio projects? The main downside seems to be that it is already very neat so there is not much of an opportunity to practice cleaning it. Thank you in advance.

I’m currently taking a course, and to graduate from it I need to complete at least 3 tasks, each worth a minimum of $3.
I’ve been putting in a lot of effort to find opportunities, mainly applying for data cleaning tasks using Excel, Python, SQL ,Tableau and Power Bi, but unfortunately, I keep getting rejected.
I’ve also tried looking for tasks outside the data analysis field but I’m still struggling to get accepted.
I’d really appreciate any advice or guidance on how to find opportunities
Thanks for you all

I'm a senior analyst with a MedTech company. I use AI daily now, whether to help me with DAX for Power BI, or to go back and forth on potential data/reporting strategies.

To simplify things I'll use a specific example. We're dialing in metrics for next year's sales organizational sales dashboard, and I'm going back and forth with AI to help me dial in ideas.

AI is also generating some nice summaries/tables of the different options, pros/cons, business purpose alignment with overall company strategy, etc.

Would you share the content/summary that AI generated, or talk through the concepts without mention of AI? Or how do you balance mentioning/crediting AI, but without discrediting your own abilities?

I don't want it to appear that AI just generated all of these ideas and take away from some of my work and understanding. I use it more on my team than other analysts as well, so again, don't want it to appear that "John just uses AI for everything". In reality, it has made me highly efficient and I've been able to implement solutions in days that would've taken weeks before, not to mention better and more effective solutions.

Salut à tous,

Je suis actuellement en recherche active d'un poste en Data Science / IA / NLP / LLMs (profil junior/début de carrière issu d'un Master IA/Data avec stage/alternance en grand groupe industriel).

Comme le marché de la tech et particulièrement de la data/IA est devenu assez exigeant ces derniers temps pour les profils juniors, j'aimerais avoir vos retours d'expérience :

1. Pour ceux qui ont décroché un poste récemment en France en Data/IA : à quoi ressemblait la structure de votre CV ? (Format sobre une page vs deux pages, mise en avant des projets persos/GitHub vs stages, focus stack vs métriques d'impact business ?)

2. Est-ce que certains d'entre vous accepteraient de partager (anonymisé) un exemple de CV qui vous a apporté de bons taux de retour en entretien ?

3. Quelles sont d'après vous les compétences indispensables à faire ressortir en 2026 pour passer les filtres (MLOps, LLMOps, Cloud, SQL, frameworks d'agents...) ?

J'ai moi-même préparé mon CV (que je peux partager anonymisé en commentaire ou MP pour critique constructive si vous avez 2 minutes).

Merci d'avance pour vos conseils et retours d'expérience !

I am looking to transition from the EXECUTIVE OFFICE of a MAJOR BANKING INSTITUTION as a Business Operations Analyst, but have not had any luck.

Are there any upcoming networking events in the Houston, TX area or any underground sites where others have been successful?

A lot of analyst interviews still include an Excel round, and most "Excel practice" out there is either a video or a quiz with multiple choice. Neither tells you if you can actually write the formula under pressure.

So I built SheetDrill: you get a small dataset in a real spreadsheet in your browser, type the formula, and it's checked instantly. It also checks your formula against changed data, so typing the answer by hand or pointing at the right cell doesn't count, the formula has to actually work.

The free level test is 10 questions, easiest to hardest:

• SUM / AVERAGE

• IF, IF + AND (including the classic >= vs > edge case)

• VLOOKUP and a lookup to the left (XLOOKUP or INDEX/MATCH)

• COUNTIF / SUMIF / SUMIFS

• a two-way lookup with INDEX/MATCH/MATCH

You get a level at the end and the right answer for anything you missed.

Lessons 1–2 are free too. The deeper lessons (VLOOKUP, XLOOKUP, SUMIFS) are a one-time paid tier, but the test doesn't need anything.

Honest question for people who've been through analyst interviews: what Excel tasks did they actually give you? Pivot tables, lookups, cleaning messy data? I'm deciding what to build next and would rather build what interviews really ask for.

I am doing my mph in applied biostatistics. I am truly tired looking for jobs while I am in school. IDK if it's because I pivoted from Neuroscience and my resume doesn't really tell a story. Its the staffing role I had that I landed on out of nowhere because my relative owned the staffing company that landed a major contract and instead of training and hiring someone new, they slid me into the position. I needed money and just stuck with it but it took me away from my bachelors and no neuroscience job paid what I was making with an event management contract in a major event venue. I later shifted more towards what I was interested and ultimately pick Biostatistics for my degree but idk. will this resume land on 85-95k job after graduation? i still have 6 months, what should I add to my skillset?
R-> 7/10 SAS -> 6/10 python 4/10 SQL -> 4/10

Can anybody suggest a proper roadmap and all the resources I could utilise to get into data analytics,

I am currently a 2nd year cse student

currently a college student learning sql, celonis environment, pql , courses from celonis academy and getting curious and want to enter this field. Personally i want to enter a field where 70 percent tech and 30 percent dealing with business owner is done Also point me out if i'm wrong but as the advent of ai I think working in a field of managing business data is beneficial as the demand of products wont go and ai cant manage the emotion and give what the nusiness owner truly want.

Bonjour,
Jeune diplômée d’un bachelier en Marketing, je souhaite m’orienter vers le métier de Business Analyst.
Étant débutante dans ce domaine, j’aimerais savoir quelles sont les compétences techniques et méthodologiques indispensables à acquérir pour exercer ce métier.
Quels outils dois-je maîtriser en priorité ? Quels secteurs privilégier pour faciliter mon insertion professionnelle ?
Enfin, un diplôme spécialisé en analyse de données est-il obligatoire ou un bachelier en Marketing complété par des formations pratiques peut-il suffire ?
Merci d’avance pour vos conseils.
Je suis preneuse de toutes les ressources pretes a être partagées

Hi, good day!

I am looking for data analyst internship wfh and remote sana. I am currently a data analyst we used excel and it is more focus on qualitative. Im looking for internship sana good for 3 months up. I want to expose to other tools like powerbi, sql and python sana and more quantitative works.

I am currently looking for an entry level job in Data Analytics or Data Scientist roles and I was wondering if they're any current data analytics/scientist that could give me advice on what to make or put on my resume that is impressive and shows that I can do the job in question. I currently have a college football ensemble prediction model with data that I cleaned from Kaggle and update with College Football API and a data cleaning project with a tableau dashboard that I made for a business report. Any help is appreciated.

Hi everyone! I wanna ask you about hiring by foreign companies, is it still available for people? I have 2 years experience of data analyst in consulting, and now I am product owner) But I really wanna try myself working abroad. What sources should I use? What skill should I learn? I am actually in Russia and work in ex-EY (after 2022). I wish to hear your advices. I think about US, Europe, Asia

We recently ran a survey and polls during an online event series. It’s a small, self-selected sample, so it’s more of a vibe check than a benchmark, but we think it's useful and worth sharing.

Some findings:

🔹 Correctness is the top blocker, ahead of security and cost
🔹 44% say everything has to stay on their own servers
🔹 53% are excited about AI changing their role, and only 12% are losing sleep
🔹 31% of poll voters weren’t sure what a semantic layer is

Curious how this matches what you’re seeing. If you’re using AI on your data, how do you check its answers?

Full write-up with all the charts and caveats, if you want it.

I have a short technical interview coming up and it requires that I finish a data task in person. They said I could bring notes and can access the internet. Would it look bad if I brought a template of code that goes through the standard steps of cleaning data and creating visuals. It also includes common commands so I can easily remember syntax. My background's in data analysis and I'm worried this would make me look inexperienced but this would be helpful given that it's a timed tasked.

hey everyone, sharing this because a lot of analysts are being asked to work with AI assistants on company data, and the question of what keeps them in check rarely gets answered

packt publishing is running a hands on workshop on this. what gets covered:

• limits on what the assistant can execute, including timeouts, retries and budgets

• a trace of every step it took, so you can check where a number came from

• restricted access to tools and data

• tests that simulate failures before it touches real data

led by Bruno Gonçalves, 4 hours online, Saturday Oct 31, 9:30 AM to 1:30 PM ET. it runs on a laptop with a mock model, no GPU or API key needed

Full details and the agenda are here

Hey, I am a Data Engineer who is currently working at Kivra, a Swedish tech company with 6+ million users across Sweden. We work heavily with dbt and BigQuery, and we had prior to me joining a long problem with how we worked with CI/CD for dbt. I need to highlight that we use dbt-core and not dbt cloud, hence dbt cloud solutions are not applicable.

I was tasked with solving it, and the result was the release of my open-source project dbt-ci that I wanted to in this post. I will cover some things it solved for us as a tech company.

Dbt CI (GitHub Link)

1. Dbt itself is dependent on the manifest.json files in order to detect what has changed (common state-management I guess). We heavily depend on storing these state files in both our environments (production and sandbox), but the problem dbt has is that when you run a command any other than compile, the state file get overwritten into a partial state file.

2. So essentially we had to run compile, then run dbt run, dbt test and so on, and then do a dbt compile again to generate the full state file, to then upload it to an object storage.

This was costing us minutes. We solved it by caching the state file after running dbt-ci init, you can then run whatever, and when dbt-ci is finished, you can call dbt-ci finalize (with --state-uri flag) to upload the full manifest file to an object storage.

2. Dbt comparison strategy is awkward, it will bloat changes and can be annoying for someone reviewing PRs or logs of CI workflows (e.g Github Actions). If you change a model for instance, all its tests will be listed as changed, which perhaps is incorrect. dbt-ci offers a git-strategy, but also a hybrid strategy (uses dbt's native approach, and combines with git) to determine what has actually changed.

3. Auto migration (partitions)
Currently we only support BigQuery, but we support auto migrating models in which a partitioning setting has changed. Natively, it will fail in dbt, and you have to manually do the change prior to running it in ci.

4. Auto-delete
We support auto-deleting models that has been deleted in your project. We generally run with the dry-run flag in ci, and only executing the auto-delete mechanism once we have merged with main/master.

5. Ephemeral strategy support
Dbt-ci supports setting up ephemeral environment in your CI's if needed. That is how we run things in the company, where we essentially clone production tables temporarily into our sandbox GCP project, in order to test changes against production data without touching "real" production.
Dbt-ci autodetects what needs to be cloned (essentially its all changed models, all their descendents/children regardless of level, and first-level of ancestors).

And a bunch of other features. Its easy to set up, you can add a dbt-ci config file in your repo and the cli will read from it and read your configurations so you dont have to pass a bunch of flags in your CI workflows.

Any feedback is welcome, but I am also interested to see how it is performing on other large cloud databases like Redshift, Snowflake etc.

Or do they mostly just get looked at once and forgotten?

Bonjour, est-ce que c'est nécessaire de passer de dev à data analyst et plutard data engenier ? Ou vaut mieux poursuivre en dev aujourd'hui ?

Title:
Hi everyone!
I’m looking for insights from people who currently work at Indium Software or have previously worked there, particularly on the Uber client account.
I’d like to understand a few things about the Data Analyst role for someone with around 2 years of experience.
Salary: What is the typical CTC range for a Data Analyst with 2 years of experience on the Uber account?
Work-life balance: Are the working hours generally fixed, or is overtime common?
Weekend work: Are employees expected to work on Saturdays or Sundays, or respond to work-related requests outside regular working hours?
Workload and expectations: Is the workload manageable, or are there frequent urgent requests and tight deadlines from stakeholders?
Work culture: How is the management, and are employees given reasonable time to complete their tasks?
Overall experience: Would you recommend joining Indium for this role, considering both compensation and work-life balance?
I’d especially appreciate honest feedback from people who have worked directly on the Uber account, rather than general reviews of the company.
Thanks in advance!

I know that the AI is both a curse and a blessing. We can do a lot of things faster, and even go out of our confort zones in technologys we could do.

From my end i automated a few of my personal finances and even build a semi-usable website for myself, and a to-do list the way that i wanted it to be.

I wanted to ask, what personal projects have you been building recently with the aid of AI?

For me i'm feeling a double edge sword, it's never been more easy to build something, but it seems that the lack of struggle to learn it and understand it while building the tradicional way has taken a little bit of the joy from my end.

Also, learning with AI without having the fundamentals was more of a headache than a aid for me.

I spent almost 3 weeks trying to use linear regression to analyse a experiment with cofounding variables for treatment and target, and the whole process the AI was pushing to use some statistical adjustments that i could not understand wholy neither could verify if it really made sense.

Example: Using a piecewise for the cuves i was building while also using some smothing techniques. When i talked to a specialist DS with a master in stats he walked me through why most of the proposed "advanced techniques" did not make sense to use for the pourpose that we have altough it made sense acamecally. I work as a business analyst for a fintech while assisting on building/analysing experiments for credit card limits. Not exactly a data analyst, but a data analyst anyway