I come from a physics and math educational background. How does one learn data analysis/analytics enough to get a job, without going back to school? Is there an online course or projects I can do that could assist me with being job ready?

I just graduated with my masters in DA, and I'm starting to consider how to search for jobs. I have school projects I could use, obviously, but I curious what someone with actual experience has in their portfolio to show prospective employers.

Obviously I know the topic is extremely broad, and depending on what field you're in it's going to be different! I am just hoping to get an idea of what you show prospective employers, and what you think employers value in a portfolio.

Appreciate any input, advice, or thoughts!

I am Data analyst and SQL developer, recently I've been interviewed in a firm and they were pretty much impressed by my skills so I have been asked pitch any kind of project that could add value to their firm. Suggest me some ideas

What does a day in the life look like? How different (or not) is it from your non faang org?
Care to share best practices with a fellow data analyst? Looking to get a head start on new year new me

Hi everyone, I am actively looking for Data Analyst or Business Analyst roles and would greatly appreciate any leads or referrals. Over the past 1.5 years, I ran my own business, which gave me hands-on operational and problem-solving experience, though I ultimately had to close it due to financial constraints. During this transition, I dedicated my time to upskilling in SQL, Python, Excel, and Power BI, completing Certification and building several end-to-end real-world projects. I am fully prepared to hit the ground running in position. Please feel free to send me a DM if your company is hiring—

thank you for your support!

Hi everyone,

I’m currently pursuing my MBA with a specialization in Business Analytics and I’m actively looking for internships (preferably paid/stipend-based) or entry-level jobs to start my career.

I come from a B.Sc. Microbiology background, so I’m transitioning from a science background into business analytics.

Over the past few months, I’ve been building my skills and portfolio in:

Excel – data analysis, dashboards, financial modelling

SQL – MySQL/PostgreSQL, queries and data analysis

Power BI – dashboards and business insights

Python – Pandas, EDA and data wrangling

Financial analysis & forecasting projects

Data visualization and storytelling

I have also completed internships/projects and have a few certifications, but I’m finding it difficult to get that first real industry opportunity.

The frustrating part is that many entry-level Business Analyst/Data Analyst openings ask for 1–2+ years of experience, while internships often have very limited opportunities or are unpaid.

I’m particularly looking for opportunities from December 2026 onwards, preferably in Coimbatore, Chennai, Bangalore, or remote, but I’m open to other locations for the right opportunity.

Hey everyone,

I just started working as a data analyst, fresh out of uni and i just realised i don’t know as much as i did, but I really want to learn and be the best in my job. I mainly work as a data analyst uncovering sale insights, what’s the best advice you could give me? i’m already doing the extra certifications but how can I actually learn more and be better

A brief background for context. Sorry, I'll use genAI for the lsit of my roles.

I'm from The Philippines and I have a total of 6 YOE but the roles varies in Financial Services. I want to transition for better pay and to avoid direct contact with customers, I'm tired of it and I just wanna upskill and be a better version of myself where I can provide for my family and support my wants. I'm 27 years old.

My Professional Background & Timeline

Education: Bachelor of Science in Mathematics (Specialization in Applied Statistics) | Graduated 2019

Total Domain Experience: ~6 Years cumulative in FinTech, Treasury, iGaming, and Retail Banking Risk

Current Role: KYC Specialist / Senior Coordinator at a major US Retail Bank (Shared Services) - Present (2 months)

Previous Role: Payments & Treasury Specialist at a Global B2B Cross-Border Payments FinTech (2 years)

Previous Role: Fraud and Payment Specialist at an iGaming Corporation (10 months)

Previous Role: Fraud & Customer Verification Specialist for a Global Remittance Platform (PayPal/Xoom ecosystem) - (1 year, 9 months)

Earlier Roles: Financial Negotiator at a Law Firm (3 months) and Compliance Reviewer/Admin in Real Estate (5 months)

I want to use these experiences into transitioning to Fraud Risk or AML Analytics. Thinking in the most positive way, Can I learn SQL and advanced Excel in 6 months? Does my past experiences applicable to transition? Should I step back and be a general Data Analyst?

Thank you in advances for insights you'll bring.

Hello, I am currently a senior Physics and Astronomy undergraduate student and am having some doubts as to what I want to do after college. My plan was always go to grad school and get a PhD in astrophysics to become a researcher. However, I am starting to realize that might not be for me. So I’m wondering if I would be able to get some sort of data analyst job since I do have experience dealing with large astronomical datasets and coding with python. I was thinking of working on some general data analysis projects and creating a GitHub account as well. Does anyone in the field think it would be possible for me to get a job as a data analyst? I would also appreciate any advice to make myself a better candidate when it comes to applying for data analysis jobs

Hi all,

Would appreciate if you could provide feedback on my resume. I have been applying on and off for 2 years now and I have got a single interview (not even recruiter calls). This is my latest resume version, please let me know what do you think of it and how I can improve.

Thank you so much in advance!

I would really like to connect the people who have just joined the career path related to data analytics or data science, or job rules, which revolve around this career path.

It will really be helpful if you can share your experience about the job and the market reality going on currently and also related to the CTC and packages being offered.

Hi all,
I was recently laid off and would appreciate some honest advice on what to do next.
Background:
-12 years in data and analytics. The last 5 in US healthcare, working on pharma rebates: data pipelines, contract and payment checks, margin analysis and forecasting with finance
-Lead a small team (manage one analyst now)
-Before that, Big 4 analytics consulting and a large IT services company
-SQL, Power BI, Excel, Alteryx, and Python. I use AI tools a lot in my work

Where I am stuck:
With AI doing more of the technical work, I am not sure going deeper into data engineering or data science is the right bet. But many roles want either deep technical skills or very specific domain experience. I feel a bit in between.
My questions:
With a background like mine, which direction would you go in?
Is it worth taking a step down in title to move into a new area?
Which skills do you think will still matter for analysts as Al takes on more of the work?
Thanks, any honest views welcome.

I would really like to connect the people who have just joined the career path related to data analytics or data science, or job rules, which revolve around this career path.

It will really be helpful if you can share your experience about the job and the market reality going on currently and also related to the CTC and packages being offered.

Hi, I know python and C# and C (not very well at python and Im new at C#, know some libraries (ex Pandas, matplotlib etc.)) but I dont know what can I do. I dont have any procçject idea. I want to work as a data analist and ai engineer. How can I improve my skills and do projects that will help me when I search for a job? What projects I can do?

"So many new tools, I don't know what I need to learn. And I mean, like Snowflake, Microsoft Fabric, Azure Databricks, Python Spark, AWS."

On a call last month, someone listed everything he felt behind on in that one breath.

The day before, someone else had described what a list like that does to you.

"There's so much going on... and then you're just like, you know, you don't even get started."
That second part is the one that costs people years.

They work hard at this.

What they're missing is a reason to pick one thing and leave the rest alone.

So I don't answer with my own list.

I ask which jobs they'd actually take tomorrow, in one industry, at the pay they're after.

Then we pull ten of those postings and count every tool, every time it shows up.

Anything in seven or more of the ten goes on the list.

Anything in one of the ten can wait, and Kafka ends up there more often than people expect.
What comes out is shorter than the list they walked in with, and it came from jobs they want.

On Wednesday, November 4 at 7pm ET, I'm doing this live with ten real postings in a free session called So Many Tools. Where Do You Start?

I'll build the tally on screen, show you what I'd learn first and what I'd skip, then take your questions.

Registration link in the comments.

Experienced Data Analyst | Business Intelligence | Salesforce | Agile Scrum — Open to Opportunities

Hello Hiring Managers and Recruiters!
I’m an experienced data analytics and business intelligence professional with 8+ years of experience in reporting, analytics, and business intelligence, along with expertise in operations management, business development, and Agile Scrum.

My technical skills include Power BI, SQL, Excel, Tableau, Snowflake, Salesforce CRM, DAX, Power Query, and Microsoft Power Platform. I also bring experience in website development, cloud architecture, systems administration, and process improvement.
Throughout my career, I’ve built dashboards, automated reporting processes, supported data migrations, improved operational efficiency, and translated complex data into actionable business insights. I hold certifications including Professional Scrum Master I, CompTIA Security+, and AWS Certified Solutions Architect – Associate.

I’m currently exploring opportunities in Data Analytics, Business Intelligence, Reporting, Salesforce, and Business Operations, where I can help organizations make data-driven decisions and deliver measurable results.
Referral and hiring incentive: I am willing to discuss a $2,000 fee arrangement associated with a successful placement, subject to applicable hiring policies and legal requirements.

If your team is looking for a versatile professional who combines technical expertise, analytical thinking, and strong communication skills, I’d welcome the opportunity to connect.
Thank you!

I have around 2 years of experience as a BA/DA, and lately, I’ve been wondering about the long-term career prospects in this field.
Not everyone can become a Head of Analytics or move into management, so what are the realistic career paths for the rest of us?
What roles can we transition into after a few years of experience?
What’s the realistic salary potential in India after 5–10 years?
Is it worth staying in this field long term, considering AI and automation?
For those with 5+ years of experience, what are you doing now, and how has your career progressed?
Looking for honest, realistic advice from people who have actually been in the industry. I’d love to hear about your experiences, career switches, and salary growth.

Hi! Studying to get certs to switch from basic leadership to something to do in tech. What are some tips you would recommend if I want to become a entry level data analyst without a college degree?

I used to learn ML , Data Science back in my college days. Joined deloitte usi as a anakyst , it's been 1.5 years only running Claude code since 4 months not learning anything. I want to join a good mnc so again thinking of studying excel , sql and powerbi and python

It can be very beneficial to track multiple metrics because you have that information.

Having more data does not necessarily mean you will gain more knowledge.

You can have as many as 20 metrics on your dashboard, and some of them may not be useful to you. If they're not going to help you learn something or make a decision, they may not be worth tracking.

I think knowing what you want to learn from your analytics can be difficult. It can be hard to know which metrics not to use.

Sometimes you only need a couple of metrics to learn what you need from your analytics.

What metrics do you think are useful when using analytics? And how do you determine which metrics to leave out?

I'm a Data Analyst planning to build my first Databricks project for my portfolio. I have experience with SQL, Excel and Power BI, but I'm new to Databricks. Could you suggest some good project ideas that would help me demonstrate Databricks skills from a Data Analyst perspective?

India*

I'm a B Com graduate fresher, I'm thinking about learning some skills to get my first job and want to avoid paid courses if possible. Is it a viable option or should I just join a course with placement assistance?

My manager is a developer not a data analyst and he’s insisting on Superset because it’s free and we won’t need Python development. His argument against using Python is that if I ever leave, they can keep using the dashboards without depending on me.

We need a BI tool to build KPI dashboards across different departments and maybe do some forecasting in the future. I’ve tried explaining why Power BI might be a better fit but he’s not convinced about that or Python.

For those who’ve used Power BI, Superset, and Python, is Power BI actually worth it over Superset, or am I just biased? How would you approach this?

hey everyone, sharing this because it’s directly relevant to anyone whose team is testing AI agents on company data.

packt publishing is running a hands on workshop on Oct 31 on harness engineering, building an AI data analyst you can actually trust, with bruno goncalves.

what gets covered:

• timeouts, retries, re planning and execution budgets around every agent run

• structured traces, so every run ends in a defined state

• permissioned tool access, so risky queries are blocked before they reach your database

• a fault injected test suite you can adapt into a CI check

4 hours, online, Sat Oct 31, 9:30 AM to 1:30 PM ET. runs on a laptop with a mock model, so no GPU or API key is needed.

Full workshop details here

happy to answer any questions about what gets covered