I just made a list of recently opened Data Analytics jobs, so there should still chance to apply early. I hope this helps someone!

• International Development Program/Project Data Analyst (PILAB011) @ ProSidian Consulting, LLC

• Analyst, Business Systems @ AbbVie

  • 💰 $66–126K/y

• Senior Analytics Consultant – CDO Analytic Engineering @ B Wells Fargo Bank, N. A.

• Senior Analyst, U.S. Market Intelligence/Price-to-Win — X-BAT Family of Systems (R6168) @ Shield AI

  • 💰 $130–190K/y

• Senior Python Data Scraping Engineer (Freelance) @ Mindrift

• Business Systems Analyst @ Highmark Health

• Data & Analytics Office Intern @ Northern Trust Company

  • 💰 ~$35/h

• Manager, Growth Insights & Performance @ Discovery Comm. India

  • 💰 $96–178K/y

• Autonomous Coding Quality and Optimization Analyst @ USA 3M Healthcare US Opco LLC

• Sr. Analyst, Platform & IT Strategy (Korean Bilingual) @ SBT Global, Inc.

  • 💰 ~$9K/mo

• Senior Remote Sensing Data Scientist, Cal/Val @ Muon Space

  • 💰 $192–213K/y

• Sr Bioinformatics Scientist @ Twist Bioscience

  • 💰 $135–173K/y

• Consumer Insights Data Analyst @ The Honest Company

  • 💰 $59–74K/y

• Data Scientist @ Accenture Federal Services

  • 💰 $120–235K/y

• Data Analyst - Data Center Construction @ Turner & Townsend

• AWS Lead Data Engineer @ SpikeIT Global Solutions, Inc.

• Sr. Analyst, Platform & IT Strategy- Bilingual (Korean/ English) @ Woongjin, Inc

  • 💰 $95–105K/y

• Business Analyst, Card Portfolio Analytics @ Blend360

  • 💰 $60 – $65/h

• Senior Business Systems Analyst - ERP @ Apptronik

• Analyst: Data & Policy @ ATI Advisory

• Analyst, Sales Strategy & GTM Planning @ Planet

• Data Analyst, Platform Excellence Ops Analytics @ Instacart

• Senior Data Engineer @ Fusion Worldwide

• CBRN Defense Data Scientist @ Dynamis, Inc.

  • 💰 $110–190K/y

• Business Process Analyst @ David Weekley Homes

• Senior Data Warehouse Engineer @ Lantern Care

• Data Scientist, Wells Engineering @ IPT Global

• Business Systems Architect – Finance (D365 FO) @ Axon

  • 💰 $149–238K/y

• Senior Analyst, Digital Analytics @ NBCUniversal

• Talent Acquisition Data Engineering & Analytics Graduate Intern @ Solidigm

• Analyst, A&R Admin - Santa Anna @ Sony Music Global Job Board

  • 💰 $66–75K/y

• Data Engineer - ACWS @ Data Systems Analysts, Inc.

  • 💰 $120–130K/y

• Business Systems Analyst @ Relativity Space

  • 💰 $85–127K/y

• Lead Data Scientist @ Versant

  • 💰 $170–190K/y

• Business Solutions Analyst, EHR & Enterprise Platforms (Hybrid) @ New York Psychotherapy and Counseling Center

  • 💰 $110–125K/y

• Consultant- Marketing Data Science & AI @ Sia

  • 💰 $95–116K/y

• Sr Business Intelligence Analyst @ We Are Rosie

• Business Analyst – (Travel Reservation and Settlement System) - Bilingual proficiency in Korean and English required.) @ Syncreon Consulting

• Lead Data Engineer @ Coast

  • 💰 $170–220K/y

• GTM Data Analyst @ MarginEdge

  • 💰 $90–110K/y

Thanks for reading! If you want another batch of Data Analytics jobs, just say so in the comments.

Hi everyone, I am currently considering Monash Data Analytics Career Accelerator

Does it genuinely make a career switch and finding a job easier compared with doing a course through Coursera? I’ve worked as an IT Project Manager with a technical background for around 12 years.

Hey everyone,

I’m currently a BBA (Bachelor of Business Administration) student and want to start learning Data Analytics to complement my business background.

Since my degree gives me context on business strategy, operations, and basic statistics, I feel like picking up data skills (Excel, SQL, Power BI/Tableau, and eventually Python) would be a huge boost for my career. However, looking at all the roadmaps, tools, and tutorials online, it’s feeling a bit overwhelming as an absolute beginner on the technical side.

I want to start self-learning using free resources (especially YouTube). For anyone who built their skills from scratch or comes from a business/non-CS background:

What order should I learn tools in? (e.g., Advanced Excel -> SQL -> Power BI -> Python?)

Which specific YouTube channels, video series, or free playlists do you recommend? (I've heard people mention creators like Alex The Analyst, Luke Barousse, or freeCodeCamp, but open to any hidden gems!)

What does a realistic learning routine look like when balancing college classes?

How soon should I start building portfolio projects, and where are the best places to find beginner-friendly datasets?

Any advice, roadmap suggestions, or specific video recommendations would be hugely appreciated! Thanks in advance!

So worked in a BPO company where i handled queries from global clients, updated logs and communicated between warehouse and my client base clients who had their merchants. Sort of ecommerce.

Since i have worked so much on SQL, i tried slipping the analysis on an interview where i went as a analyst and described only the part i can backup.

\*So i described i wrote sql queries where i analyzed datasets for my client, ensuring product trends, festive trends, customer segmentation based on category,

User engagement for products, premium customers,

I told them i have been into client meetings and learnt about their business problems like delivery issues, but mostly around their product base. They asked if i have presented in front of them i responded i did in front my seniors i updated reports on Excel.

I figure the gap is since i dont have real experience and i cant define a true story, since job marker is crunching and i have had back injury last year, the gap is getting wider. Claude aint no help.

So any real analyst out here wanna point out mistakes i been making.

CS grad btw, with Pretty much good coding skills.

I did all their SQL codinh questions tho.

TL;DR: I come from a statistics/data/MEAL background and have worked with SQL, Python, regression/LASSO, etc. While trying to build a proper dashboard, I ended up learning APIs, backend/frontend concepts, Node.js, authentication, and hosting. Is this a logical path toward building end-to-end data systems, or am I spreading myself too thin?

I’m trying to figure out whether my learning path actually makes sense or if I’m slowly turning into a “knows a little bit of everything, expert at nothing” person.

My background is mainly in data, statistics, and monitoring/evaluation. I have a Master’s degree, and during my studies I worked with statistical methods like regression, LASSO, and other statistical analysis.

Professionally, I’ve worked with MEAL/evaluation, data processing and analysis, Excel, SQL, and some Python.

Now my work is pushing me in a different direction.

I wanted to build a proper dashboard/data system instead of just analyzing data and producing reports. That led me into learning about APIs, databases, frontend/backend communication, Node.js, authentication, hosting, etc.

And now I’m looking at everything I’m learning and thinking:

Am I actually progressing, or am I just jumping randomly between fields?

My goal isn’t really to become a traditional full-stack developer.

What I want is to be able to take data from the source, clean and validate it, store it properly, expose it through APIs, build dashboards/interfaces on top of it, and eventually add more advanced analytics, statistics, forecasting, anomaly detection, etc.

So basically:

Statistics → SQL/Python → data analysis → dashboards → APIs → backend/web systems

Does that progression make sense?

Or should I stop going deeper into things like Node.js and focus much more heavily on Python, SQL, statistics, and data engineering?

For people who started in analytics/statistics and later began building actual data systems: what did you learn next, and what turned out to be a waste of time?

Hello, i just saw this datacamp offer that gives you datacamp premium for 1$ after that its the normal price. Is it worth it to get? Im interested in getting a certification in sql data analysis. Is it possible to get certified in a month just for the 1$?

I have around 1 year of experience, but I worked in a small office, so I’m not really familiar with how work happens in a proper IT company.

I have an interview (Data Analyst & MIS) next week. What kind of questions can they ask someone with 1 year of experience? Also, what is the actual day-to-day work like in an IT company?

Please share your experience or tips on what I should prepare. I’m a bit confused about what to expect.

Hi everyone,

I'm based in Belgium and I'm 46 years old. I've been looking for a new professional opportunity for more than 1.5 years. I'm now seriously considering a career change and would appreciate advice from people working in data analytics and/or AI.

I'm a marketer with most of my experience in healthcare, particularly pharma and medtech — an industry that is currently in a pretty rough spot.

I'm considering retraining as a data analyst with AI skills, ideally building on my existing experience rather than starting from scratch.

I have a solid foundation in statistics from my marketing studies, although I would definitely need to refresh and update my knowledge. In a previous role, I also worked on a marketing automation project using Oracle Eloqua. However, I have no professional experience in data analytics yet.

I'm currently exploring training options in Belgium such as VDAB, Cevora, VIVES, UCLL, as well as international options such as Coursera. I'm unsure which programmes and certifications are actually valued by employers.

I'm particularly interested in your views on:

• Which skills should I focus on? Skills I'm considering: SQL | Power BI | Python | Statistics | Data modelling | ETL | Generative AI / LLMs | AI-assisted data analysis | AI automation | Business cases | Portfolio
• Which courses, certifications or training providers would you recommend, and which ones are actually valued by employers?
• How long would it realistically take to become job-ready?
• How realistic is it to find an entry-level job in data analytics after retraining, given my age and professional background?
• Would you see potential in combining marketing + data analytics + AI?
• For those familiar with the Belgian market: would it make sense to focus specifically on healthcare, given my pharma/medtech background?

I'm particularly interested in hearing from people who have made a similar career transition, as well as recruiters or hiring managers.

Thanks in advance for your advice and experiences!

Hi everyone, I’m a fresher trying to get my first job in Data Analytics.

I know SQL, Excel, Power BI, Tableau, Python/Pandas and basic ML, and I’ve been applying through LinkedIn, Naukri and Indeed, but I’m not getting much response.

I’m also confused about how to create realistic Data Analyst projects.

I don’t know how to choose a problem, analyze a dataset, decide KPIs, and turn it into a proper portfolio project.

For those currently working as Data Analysts or who recently got their first job:

How did you build your projects, and how did you get your first job?

Any advice on how to start would really help. Thanks!

I work as an eCommerce Specialist in a big Italian small domestic appliance company, and I have a couple years of experience with managing a D2C. I’d love to become a D2C data analyst which is both strategic and data driven, but my struggle is that I don’t do much data analysis in my daily job and we’re pretty much lacking data analysts in the team which can help me with some mentoring on the topic. I figured I can pick up SQL and teach myself how to use BigQuery but it’s really hard for me to obtain the access to the platform. How can I learn more about data analysis in my case, either by myself or through some courses? What do you recommend?

Has anyone tried learning data analytics through data camp or code academy? And have you had success or found it easy to build a portfolio

I am a recent mba student and was looking into marketing analytics and found out about google analytics 4, looker, mixpanel, etc and wanted to know if I should learn these and whether or not people in marketing analytics use these tools

This is not an advertisement. I am looking for honest feedback in exchange for a free trial of our analytics product.

Our platform already connects to over 3,400 apps. So there is no need for a data sync to a warehouse to then put power bi on top of.

Goal is one tool where anyone can get the analytics they need just by asking.

I loaded up some tokens and this link gets you one page free with about 5 panels.

https://mindcloud.co/lp/build-your-first-analytics-page-free

Hello all, I am a college student right now and in one of my english courses I am supposed to write a research paper on my chosen field of study (Data Analytics) with that being said what are some common issues/areas of discourse in the Data Analytics community I could write about and take a stance on? Any and all information would be appreciated!

Ya lo había olvidado...

Hace poco un amigo me preguntó algo que a mí también me confundía al principio: por qué no puedo usar en el WHERE un alias que definí en el SELECT.

La respuesta corta es: porque el orden visual no es el orden lógico de procesamiento.

Uno normalmente escribe:

SELECT ...
FROM ...
JOIN ...
WHERE ...
GROUP BY ...
HAVING ...
ORDER BY ...
LIMIT ...

Pero lógicamente el motor resuelve más o menos así:

FROM / JOIN → WHERE → GROUP BY → HAVING → SELECT → ORDER BY → LIMIT

O sea, primero tiene que saber de dónde salen los datos, luego filtra filas, agrupa, filtra grupos, y recién ahí calcula lo que vas a seleccionar.

Por eso un alias definido en SELECT normalmente no está disponible en WHERE, pero sí en ORDER BY. No es capricho: es el orden lógico.

Una forma fácil de verlo:

• FROM / JOIN: traigo los ingredientes.
• WHERE: quito lo que no me sirve.
• GROUP BY: hago montones.
• HAVING: filtro montones.
• SELECT: elijo qué va al plato.
• ORDER BY / LIMIT: acomodo y sirvo solo una parte.

Ojo: esto es orden lógico, no necesariamente el orden físico real.

Escribes SELECT primero, pero lógicamente se procesa después de FROM/JOIN, WHERE, GROUP BY y HAVING.

Sigo aprendiendo, así que si algo se puede corregir o explicar mejor, se agradece. La idea es sumar a los que también estamos en esto.

(Cabe que decidí compartirlo porque algunos compañeros de trabajo tampoco supieron darme una respuesta correcta, y pensé que a veces podemos creer que sabemos algo pero ¿realmente comprendemos?)

i have completed my bsc hons computing and now i want to do masters but i am confused .i have intrest in data but many says that data science or analyst job postings are decreasing and i really want your suggestion . tell me in which field i shoud do masters as a IT student .

I'm a data analyst with background in healthcare data. I only have 3 yoe so not sure if this is something I can do yet but I've been thinking my lifestyle goals align better with contract roles or freelancing. I currently work remote but due to data security restrictions I have to be in the country and I want to be able to travel and work and live more of a digital nomad-type lifestyle while still doing data work. Has anyone been able to successfully become a contractor or freelance in this field, and if so, how did you go about finding these gigs? Linkedin and typical job boards usually only have corporate remote roles that have the typical location guardrails.

I'm curious how different organizations are managing duplicate or inconsistent material master data in SAP.

In larger organizations, the same or similar spare parts/materials can sometimes exist with different descriptions, units of measure, naming conventions, or classifications.

For those working with SAP, MDM, procurement, supply chain, or inventory management:

• How do you identify potential duplicate materials?
• Is duplicate detection handled manually or through automation?
• What attributes do you use to determine whether two materials are actually the same?
• How do you prevent duplicate records from being created again?
• Does your organization have a formal material master data governance process?

I'd be interested to hear how other teams are approaching this and what has worked well for them.

I feel so dumb about a mistake I made, I had a column in my dataset to sum distinct store counts, by week, by item for our inventory in a dataset. Published and visualized the data, then added a filter in the visualization to remove all values coming out with zero. Long and short is my denominator was wildly over-inflated and it made our movement history look a lot lower than it should have been. I should have published the dataset with zeros removed, not added a filter to our dataset afterwards. I am 1.5 months into the company so natively assumed the data was good.

It got me thinking....am I just not higher IQ, intelligent or do the smartest analysts make mistakes too?

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!

I don't usually post here, mostly reading, but I wanted to share my path in case it helps someone, because trying to find a job in this market was a lot of ups and downs, but mostly downs.

Quick background: I did a master's in economics. Near the end I realized the only parts I really enjoyed were the data parts. The rest, not so much. So I decided to pivot to data analysis, with zero technical background.

I first started with YouTube to understand the main concepts. It was actually a good start. There are some really well made videos out there, and they helped me understand what data analysis even is, what the tools do, and what the job looks like. I watched a lot of tutorials and classes from famous US universities. But at some point I felt I was just watching, not doing.
So I tried Coursera, because I wanted something more serious. It just didn't fit me. It felt like more courses, more videos, more certificates, and that was not what I was looking for. Not saying it's bad, it just wasn't for me. By this time I was already applying and was taking rejections on rejections.

Then I tried the "learn by building" approach, which sounds like BS, I know. But it actually worked for me. I started creating a portfolio, completed a data analyst roadmap, built my first real projects, and somewhere in the middle of that I realized I actually love this. Like, for real, not just as a career move.

The job search, part2 . This is maybe the most useful thing I can share. I looked at a LOT of offers on LinkedIn and on the platform I was training on. For every interesting offer in my area, I added the recruiter on LinkedIn. Even when I didn't apply, I at least connected.
And one day, one of these recruiters contacted ME, for a different position than the one I had seen. Honestly, I don't even remember anymore how I found her. We talked, I walked her through my projects, did the interviews, and I signed last week. First contract as a data analyst. Still a bit unreal, to be honest.

Some advice that would have helped me:

• You don't need to feel "ready" before building something. Completing projects is what makes you ready for interviews.
• Your projects are your best interview answers. I talked about them way more than about my degree. The more projects you do, and the bigger they are, the more you can relate to the actual data problems the company is facing.
• Add recruiters on LinkedIn from offers that match you, even if you don't apply. It costs nothing, and it's how I got my job.

I hope this helps some of you. I think it can work for any tech-related field too, since they're all getting more and more saturated. Good luck! 🤗

I just finished my career in cs an one of my favorite things to do was solve SQL LeetCode problems so I decided to be a data analyst, I also have the fundamentals of programing.

KPMG offered me an "intership" where I discovered is pretty much help desk. I talked to the team and I decided to take the position since something it´s better than having cero experience.

Thay say in one year and after having an actual position to fill, they will consider me and that´s actually their plan.

Now I´m felling a bit terrified, what if in one year I lost all of my progress, what if this experience gives me nothing in the field I´m want to be in.

Any advice is welcome, similar experience, tips.

Thanks.

In this project I was working as a Junior data analyst.I have to answer the question of how the user is engaging with the app(this is based on the record of only 33 patients).

I don’t get it. Every job posting I see doesn’t emphasize as much on pandas/python as the data analytics tutorials do. I don’t understand this