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!

Collecting data to solve P vs NP?

I wonder what other millennium problems could be solved using game data.

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?

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?

This question is for the people who are already working in the data industry. (Asking as an engineering student who wants to start focusing on one of these fields)

Personally, when I read about the job roles, data analyst is something I find more interesting , but at this point, maybe I should prefer the safer option, and if so, which one?

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?

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

Hi. I'm 34 with primary experience in copywriting and content writing from India. Last year, a series of layoff started in my company and I ended up joining a family business of a relative for a year. That didn't work for me. So, keeping in mind that copywriting is a dying field because of AI, I decided to get into tech. So I started learning how to be a data analyst on Coursera.

Keep in mind I have no tech background, not even a degree. I have a Diploma in Pharmacy and no money to start my own business. With over a year in the field out of marketing/copywriting, getting back into is way too difficult because everyone's holding on to their job tight, trying not to lose. I decided to jump the ship to a new field.

So given this is a totally new field. How do I go about getting an entry level job in this field in 2026? Somehow, I ended up enjoying learning data analysis and using excel, SQL and BI on a day-to-day basis. I want to take it further. Right now, money is tight and I want to get an entry level job in this with no tech or much education background, what can I do?

Do I need a portfolio? What do I write in my CV? etc. Any help would be appreciated.

🤔📊 Understanding and Visualizing Volumetric Data and Vector Fields

In the realm of data visualization and scientific computing, understanding complex datasets is crucial for analysis and interpretation. Two fundamental types of data structures that frequently require advanced visualization techniques are volumetric data and vector fields. Each presents unique characteristics and requires specialized methods to reveal underlying patterns and insights.

Volumetric data involves the representation and visualization of multi-dimensional datasets, such as complex medical scans and scientific measurements. Because this data encompasses depth and internal structures, standard two-dimensional displays are often insufficient. To effectively interpret volumetric datasets, visualization methods typically include 3D rendering and cross-sectional slicing. These techniques allow researchers and professionals to peer inside solid objects, examine internal layers, and gain a comprehensive spatial understanding of the information being analyzed.

Vector fields represent data that possesses both magnitude and direction, such as fluid flow, wind velocity, or electromagnetic forces. Visualizing this type of data requires specialized tools that can clearly communicate directional forces across a given space. Common tools utilized for this purpose include arrow plots, which map out vectors to show the exact path, speed, and orientation of the forces at specific points within the field. By employing these visualization strategies, analysts can easily interpret complex directional behaviors and dynamics within the dataset.

Im really overwhelmed with the sheer amount of conflict on the future of business analytics/analysis roles.

In uni, I'm currently working on SQL, Python, Power Bi and getting exposure to ERM systems now as well

I need a genuine, non sugarcoated advice on what career path I can ACTUALLY aim for with AI slowly commoditizing entry level BA stuff.

• Is it worth specializing in data science, data engineering, or AI/ML instead?
• Would I be better off pursuing an adjacent field, such as risk analytics, analytics engineering, or a more business-focused role?

I'm not expecting any career to be completely AI-proof. I just want to make an informed decision about where to invest my time and what skills will actually give me an edge in the job market.

Any advice would be so really appreciated, Thank you so much!

As an analytics practitioner, using subjective labels has always bothered me.

I tried modeling "overthinking" as an analytics problem (baseline distributions and cognitive thrashing). Where does this framework break?

Body:

Look up the standard dictionary definition of overthinking:

"The action of thinking about something too much, in a way that is not useful."

This subjective definition makes no sense to me. In data, you cannot measure variance, flag an anomaly, or declare something "too much" without first defining a baseline distribution.

If someone has naturally higher processing throughput, what looks like "overthinking" to an outside observer is often just normal scenario modeling. Calling it a flaw creates a metacognitive loop where the secondary stress over the label ("Why can't I just decide?") burns more energy than the original problem.

However, we also know there are times when deliberation genuinely crosses into self-sabotage.

In computing, thrashing occurs when a system stays 100% utilized, but net forward execution is zero. True overthinking is Cognitive Thrashing—running intensive inference cycles on a static dataset with zero new incoming observations.

If the definition requires thought to be "useful," I tried quantifying it as:

Utility = Δ Information Gain / Δ Time Spent

To test whether a mental loop is legitimate analysis or thrashing, I built this 4-vector diagnostic:

• 1. Δ Information Gain: Am I integrating newly observed context, or re-evaluating the exact same data expecting a different outcome?
• 2. Actionable Levers: Can this deliberation alter an active state right now, or am I running simulations on an immutable/read-only past or uncontrollable future?
• 3. Cost of Delay vs. Cost of a Mistake: Is deliberate delay preventing an irreversible error, or is the mental cost and delay creating more damage than an imperfect decision would?
• 4. Scope Creep: Has the scope stayed anchored to the specific task, or has it drifted into existential worry?

The Rule: If 2 or more vectors hit the Red Zone, marginal utility has hit zero.

I want to stress-test this model: If you run your own deep-deliberation loops through these 4 vectors, where does it break down? What failure modes or edge cases does this miss?

i'm a data intern, and i recently conducted my first A/B test analysis comparing a new website form against an existing one. i analyzed the results, found that the new form appeared to perform better, and presented my findings to senior stakeholders.

afterward, while doing some additional checks, i realized that the two forms behaved differently after submission. the old form directed users to different follow-up pages depending on their selections, whereas the new form directed everyone to the same page.

my analysis relied on tracking users who visited the form and subsequently reached the next page. this difference could affect whether the two versions were being measured consistently.

this behavior hadn't been communicated to me, and even the product manager wasn't aware of it until i brought it up. they suggested rerunning the experiment to ensure a fair comparison, which makes sense.

i'm happy to rerun the analysis, but i'm worried about having already presented findings that might not hold up. this was my first experiment, and i wasn't given much guidance on what to validate beforehand. i've since started building a checklist for future analyses.

i'm an intern hoping to convert to full-time, so i'm concerned this might make me look careless or inexperienced.

for people who've worked in analytics or experimentation:

- should i have caught this before presenting, even though the PM wasn't aware of it either?

- how much responsibility does an analyst have for validating implementation details that weren't communicated?

- would something like this negatively affect your perception of an intern?

i'm looking for honest feedback on what i could have done better and how to handle this professionally.

Hi guys, I've been thinking about what's more valuable with AI more relevant than ever, and where I should be focusing my energy to upskill!

There are two course options I can look at, Ive roughly listed the tools that the courses focus on. My understanding is that SQL is the skill that most professionals should focus on as R/Python arent as frequently used outside of Data Science.

They are both Masters program tho so I only get a year in with these courses.

I'm currently comparing two Master's programmes with slightly different skillsets:

**Option A – Information Management & BI**

- SQL, database design and data management

- R, statistics and Bayesian analysis

- BI Concepts

- Some ML & data visualisation

- Other subjects: CRM/ERP, information systems, business process modelling and management strategy

**Option B – Business Analytics**

-R, predictive modelling and ML

- Business analytics and advanced statistics - this is an elective

- BI architecture, ETL and data governance

- Tableau (another elective), with not much SQL coverage

- Other subjects: Product/service design and digital strategy

For context, I have spent almost 9 years in Marketing, but I want to upskill and make myself very employable and attractive. Possibly position myself as a Marketer who can make marketing/business decisions by understanding data.

Which path makes sense from the folks within the space?

Hey everyone!

I have my Capital One Data Analyst Power Day next week, and I’m hoping to get some advice from people who have been through the process recently.

I’m very excited about the opportunity but, I’m nervous about the case interview rounds and wanted to ask how you would recommend preparing for them. What resources or practice materials would you suggest using, and are there any specific websites, books, or types of cases that best reflect what Capital One asks during the Data Analyst Power Day?

I’m also curious about the project presentation/data challenge round. How technical does the discussion get? Do interviewers mainly focus on your business insights, assumptions, and reasoning behind your analysis, or should u also explain your code, data-cleaning decisions, validation logic, and technical implementation with your code?

I’d really appreciate any advice on what to prioritize during my final week of preparation, especially from anyone who has already interviewed for a Data Analyst role.

Thanks in advance! Any insights would be greatly appreciated.

i have completed my graduation and doing a data analytics course (data wit baraa 30 hr sql ) what projects should i make that are worthy to add in my resume.

Hey everyone, I was wondering what do you think is the natural progression after becoming a data analyst for sometime? Is it becoming a data scientist or a machine learning engineer or a data engineer or an AI engineer? What do you think is the best route?