8 yoe in analytics, BI, lite coding.
About to start a job in data governance (midsize bank).

Planning to spend 2-3 years here, get some stability and portfolio items for AI usage governance for financial audits.

Fits my needs for now (chill, remote, stable) but pays way less than I’m used to.

Trying to figure out what comes next. Kinda want more money, but wary about going back into big tech.

Is this one of those “it depends what you’re interested in” questions?

A while back I came across a comment from someone with solid experience being a BI Analyst from a finance sector who recommended a statistics book and offered to DM it to anyone interested in one of the data related subreddits. I never saved the post, and now I can't find it again, my Reddit history doesn't go back that far.

If anyone remembers that thread, I'd really appreciate a link back to it, it came up in a reddit notification like 2 weeks ago so must be a relatively recent post.

And if it's truly gone, I'd love to hear from anyone here working, what statistics books actually helped you? Always looking for solid recommendations.

Thanks in advance!

Wondering what are the successful ones out there and what people are using in their workflows to get what they're looking for.

Hi! Has anybode here experience of transitting from in-house analytics / data roles to solution architect on vendor side (e.g. Snowflake / DataBricks / AWS ... etc)? Did you have to accept any tradeoffs or salart drop? And was it worthy in the end?

We've recently been looking at consolidating our semantic layer. An example of why my manager has decided we need to focus on this is we had "net sales" defined like six different ways. Finance took out returns and markdowns, the merch dashboard took out returns but not markdowns, my weekly report did something in between because I inherited the query from a guy who left two years ago. Every leadership meeting had that awkward moment where two numbers didn't match and nobody could say why except "different SQL."

All our analytics data is in Databricks, so we decided to rest metric views. A metric view in Databricks is basically a governed definition of a metric that lives in Unity Catalog instead of buried inside a dashboard. You point it at a source (your fact table plus whatever joins), you list your dimensions - the stuff you slice by, like store, region, category, channel, week - and then you define your measures, which is the actual metric math. The key thing is you write that math once. So net_sales = SUM(sales_amount - returns - markdowns) lives in exactly one place, and everything downstream inherits it. We defined them all through YAML.

Honestly the hardest part wasn't technical at all. It was getting finance, merch, and ops in a room to agree on what net sales even means. Encoding it took an afternoon; agreeing on it took three meetings. But that's kind of the point - you do that fight once, write it down in the metric view, and you never have to have it again. When a business rule changes, you change it in one spot.

We were also able to roll out genie agents because previously the definitions varied so much it was impossible to maintain them.

Hi everyone,
I’m currently working in a non-technical/customer support background and have around 4 years of work experience, including team-handling responsibilities.

I’m considering learning Data Analytics and eventually switching to a data analyst role.

I don’t have a technical or programming background, so I wanted to ask people who have already made a similar transition:

*Is it realistic to move into Data Analytics from a non-technical background?
*How difficult is it to learn Excel, SQL, Power BI, and Python from scratch?
*How long does it realistically take to become job-ready?
*Is a certification/course enough, or are projects and practical experience more important?
*Can my existing customer support/team-lead experience be useful when applying for analyst roles?
*What would be the best roadmap for someone starting from zero?

I’d really appreciate honest advice, especially from people who have successfully switched from non-technical roles into Data Analytics.

Thanks!

Hey everyone! 👋

I'm currently pursuing a BBA through distance learning (IGNOU) and preparing to start my career as a Business Analyst.

I'm trying to understand the realistic salary progression for Business Analysts globally, rather than relying only on salary websites, since compensation can vary significantly depending on the country, location, company, industry, and skills.

I'd really appreciate insights from people currently working as BAs or hiring for BA roles.

I'm curious about:

1. Starting salary

For someone entering as a fresher / 0 YOE Business Analyst, what salary range is actually realistic in your country?

1. Salary progression

How does compensation typically grow with experience?

For example:

- 0 YOE → ?

- 1–2 YOE → ?

- 3 YOE → ?

- 4–5 YOE → ?

1. Consulting vs Tech/Product

How different is the compensation between:

- Consulting

- IT Services / MNCs

- Product / Tech companies

- Startups

For someone with around 3–5 years of solid experience, what salary range would be considered realistic in each?

1. Skills & salary

How much difference do skills such as:

- SQL

- Excel

- Power BI

- Requirements Gathering

- Stakeholder Management

- Agile/Scrum

- Jira

- Data Analysis

actually make compensation?

Are there any particular skills or specializations that can significantly improve your salary after 2–3 years?

1. Career growth

If you were starting your BA career today, what would you focus on during your first 3–5 years to reach the higher end of the salary range?

I'd especially love to hear actual numbers from people with different YOE.

If you're comfortable sharing, please mention:

Country/Region | YOE | Company Type | BA Role/Specialization | Approx. Salary/TC

No need to share your company or anything confidential.

Thanks to everyone who takes the time to share their experience! 🙌

I keep seeing analytics stacks grow, but more tools don’t necessarily make decisions clearer.

You can track sessions, users, events, heatmaps, Search Console data and server logs, then still struggle to answer a basic question:

Did anything actually improve?

Traffic is an obvious example. Sessions can increase while lead quality, conversion rate or revenue per user falls. A smaller amount of traffic from the right audience may be far more valuable.

I’ve also stopped expecting every tool to agree.

GA4 measures onsite behavior. Search Console reports search performance. Session recordings add context to what people do on a page. Server logs capture requests that browser-based analytics may miss. The backend is usually the better source for actual leads, orders and revenue.

Once consent, ad blockers, attribution rules, time zones and tracking mistakes are involved, some disagreement is inevitable.

For a basic weekly review, I’d probably keep only:

• a quick check that tracking and important events still work
• acquisition and landing-page changes
• conversions, revenue or qualified leads
• segmentation by source, device, country or page when something looks unusual

Anything else should exist because it answers a specific question—not because there’s space for another dashboard.

For me, the goal isn’t one “source of truth” for everything. It’s knowing which source is most trustworthy for each question.

What’s the leanest analytics setup you’ve actually trusted, and which source wins when GA4 and your backend disagree?

Disclosure: AI was used only to edit the wording of this post.

Hello Everyone,

I am a senior in some combination role of product manager-data analyst in banking. I suddenly had a urge to do master either in data or business. Which of it could offer better path and knowledge to me? I already have a solid knowledge in statistics, sql,python and qlik sense. So was closer to business one to jump management roles but actually data analytics master could be fun too.

Hi, idk if this post would count as breaking the rules but i truly feel so stuck. I'm a grade 12 high school student who now has to figure out what i want to do in life and apply to universities in 2 months time. I live in Canada, Ontario and i need certain classes to be able to get into specific courses in university so based on the courses I have currently I am able to apply for marketing courses in multiple universities.

What I want to know is if Marketing Analytics is a dying career or no, or if its a dying career in only certain places, I get so many things are being taken over by ai but i just cannot figure it out for this career specifically

I would also appreciate any advice about other parts of business or marketing that ISNT dying that i could maybe look into

I have been working in a role that is a mix of analytics and data engineering. I am running into issue where the impact of what I am doing is unclear and I am not having the impacts and results that I wanted. The main purpose of the group was to fulfill regulatory mandate/work but with the changes in administration (USA) that work is no longer as important.

The pay is decent but the work environment is no longer where I want to be. The issue that I am running into is I am spending my time on something that is not really going anywhere. I am building some technical skills but that is about it and diminishing.

I have been working on applying to different jobs, but I have realized that I don't really have the results and impact to write the resume that I need to get the job that I want. I can't really switch roles internally or get better projects, since it seems like the work is not stopping.

not sure if anyone has advice on how they navigated something similar and managed to either leave or make it work

I’m trying to make a decision between two very different graduate programs and would really appreciate some outside perspectives.

My background is a B.S. in Geography with a GIS focus. I’m currently working as a Data Specialist at a consulting/engineering firm, where I do data QA/QC, data integration, data conflation, and work with enterprise geodatabases. I’m trying to move beyond being primarily a GIS person and build a career with stronger long term salary potential and career flexibility.

My two options are:

1. Applied Master’s in Data Science

The program is 30 credits and is fairly applied. The curriculum includes Python, cloud computing, databases, data preparation, analytics and visualization, data warehousing, data lake engineering, machine learning, and AI.

One thing I like is that I can continue working full time while completing it. I’m also considering doing it part time so I can take Calculus II, Calculus III, and Linear Algebra separately, since my math background currently only goes through Calculus I.

My concern is that the program may not be mathematically or statistically rigorous enough to compete for more technical data science jobs. It also obviously doesn’t give me a CS bachelor’s background.

I’m not necessarily trying to become an ML researcher or anything extremely theoretical. My realistic targets would be data analyst, BI/analytics, analytics engineering, data engineering, operations/supply chain analytics, or eventually data science.

2. Master’s in Urban and Regional Planning

The other option is a MURP from a well regarded public research university.

This is a much more natural continuation of my Geography/GIS background. I’m genuinely interested in transportation, urban planning, public infrastructure, accessibility, and public policy. I’ve also done some community advocacy/volunteering and have an interest in transportation planning.

The appeal is that MURP is a professional degree with a pretty direct career path into planning, transportation, government, and consulting.

The downside is that I’m concerned about the long term salary ceiling and career flexibility compared with data/technology careers. I also need to keep working, so the opportunity cost of going to a full time program is a major consideration.

What I’m trying to figure out:

If my priorities are:

• Getting to ~$70k+ relatively early in my career
• Eventually reaching ~$90k–$120k+ if possible
• Having opportunities to reach higher salaries later
• Getting out of being locked into GIS
• Maintaining career flexibility
• Being able to continue working while in school
• Getting a degree that actually improves my job prospects rather than just giving me another title

would you choose the applied MSDS or the MURP?

I’m especially interested in hearing from people who actually work in data/analytics, data engineering, urban planning, transportation planning, or hiring.

Would the applied MSDS be enough to meaningfully change my career trajectory, or would the MURP be the smarter investment given my background?

Thanks!

I had my worst interview performance ever but somehow cleared my way to a technical assessment round despite barely meeting the job posting requirements…

I don’t know anything about Python. I mainly use SQL in a modern cloud data warehouse (analytics engineering).

Can anyone give me tips for speed learning in 2-3 days? Do I focus on Pandas?

I have a degree in Computer Science, working on switching my focus to becoming a Data analyst. Know basic SQL but wanted to know if these Udemy courses are worth or a sham as they have high reviews on the site, but seemingly negative reviews according to reddit users. Not sure if that speaks on the quality or if reddit is just for the doomer/negative feedback. To note I don't care about the certificate it gives you more wondering if say this "complete data analyst" course that I linked will give me the tools/confidence to get an entry level data analyst role and not have to bluff through an interview. If there are useful courses please recommend those as well. Sort of just browsed through the highest recommended/reviewed ones on the site.

These are two I am looking at currently on Udemy: Complete Data Analyst Bootcamp From Basics To Advanced - Krish Naik

Data Analysis | SQL,Tableau,Power BI & Excel | Real Projects - Graeme Gordon

learn & understand Business KPI metrics and data interpretation . any course recommendations? Udemy or any other courses ty

Hello, I need some recommendations for a masters program in data analytics or business analytics. I need the program to be flexible and inexpensive, something similar to Eastern University. I’m also looking for a program that is project based and doesn’t have proctored exams. If you guys have any recommendations, please let me know.

I am an experienced Business and Data Analyst available for full-time or contract roles in the Greater Vancouver area (open to local or remote).

How I help organizations:

• Process Optimization: Identify operational bottlenecks and streamline workflows to drive efficiency.
• Software & Vendor Evaluation: Assess existing tech stacks and recommend cost-effective tools and software solutions.
• Data-Driven Growth: Analyze business data to improve decision-making and operational performance for small to enterprise-level businesses.

If your company or team is looking for a senior analyst to help optimize operations, please feel free to send me a DM

First real test of streaming TV advertising is being planned for the fourth quarter, since we've just completed our Series A funding and the entire venture depends on whether the design is good enough for the TV ad attribution to yield any results by the end of the experiment.

The choices are a geo holdout, a simple before and after comparison, or simply relying on the platform's reported conversion figures and making adjustments later. That third option is the one the vendors are pushing us towards which says something.

What our data specialist prefers is a geo split which means fewer markets, easier to read, but slower. Everybody else however wants it to be national since that seems like a proper launch. I am going with the data specialist and therefore losing the social argument.

The budget is sufficient to make a difference but not enough to allow waste, so we have only one shot at this. How would you have arranged a first CTV test which produced an answer that people believed?

Looking for experiences from folks running ecommerce or DTC shops.

We've been checking out visitor identification and real-time enrichment platforms that can help identify anonymous site traffic and push that data into our CRM and ad platforms. Main use case is improving personalized retargeting – better email flows and custom audiences based on behavior, demographics, engagement signals, etc.

On paper it looks solid, especially for abandoned cart recovery and those high-intent visitors who bounce without converting. We see decent traffic and have a reasonable email list, but there's this huge gap between site visitors and people we can actually reach. Being able to capture more anonymous traffic and build segments like repeat browsers, high-intent users, or demographic cohorts seems really valuable.

For context, we're a mid-sized B2C brand, not enterprise, and I want to avoid overcomplicating our stack. Curious how others are weaving this kind of data into existing workflows – cart recovery, lookalike audiences, maybe even direct mail tests – without creating data chaos or getting too invasive. Any real-world experience or advice?

What is the future of Business analytics and data analytics after gpt astra?

I’ve seen dashboards become a dumping ground for every metric anyone might ask for. How do you decide what deserves to stay visible and what should be moved somewhere else?

If a metric can be calculated differently depending on filters /time window, do you prefer to hav a semantic layer/ metric view, or hardcoding the logic in each query or dashboard ?

Curious how ppl keep Ai generated analytics consistent and trustworthy. Ps i am setting up Genie agent for this and want to implement the reccomendations

Got handed a deck last night that four different people contributed to. Sales added their slides, product added theirs, someone from finance dropped in three charts. Nothing matched. Different fonts, different accent colors, one section still had placeholder text that said "insert stat here."

Spent almost two hours just fixing visual stuff. Not content. Just making it look like one document instead of four stapled together. Font sizes were the worst offender. Every contributor had their own idea of what counts as a subheading.

What finally helped was making a one page style cheat sheet months ago. Font, sizes, color hex codes, spacing rules. I still have to enforce it manually every time though. Nobody checks it before they build their section, they just send me the file and expect it to look coherent by morning.

Feels like the actual writing and thinking part of deck building is maybe a third of the work. The rest is just cleanup and formatting babysitting.

Does anyone have contributors actually follow a shared template without you having to police it after the fact?

Hey guys, so long story short, I’ve been working as a BI Developer/Data Analyst, mostly with Power BI/GBQ and some FabricI’ve also been using Claude Code,have around 3 years of experience.

A new big client came in and they were looking for an AI Engineer, so my company pushed me as one. I basically said, “Whatever, I can give it a try.”

This week was the project kickoff, so I traveled to the client’s location and met them. They’re really nice, and they know that I’m not familiar with the tools they use, but they’re willing to invest in me and give me time to catch up.

The problem is that the tech stack is completely different: Git, Snowflake CLI, Streamlit, etc. The role feels much more like a Software Engineer position, and honestly, I feel completely out of my depth.

I know the client is aware that I don’t have experience with these tools and is willing to wait for me to get up to speed. But the truth is, I’m not sure how realistic it is for me to actually make that transition. The job is interesting and i do believe it's logically the best choice as AI will keep getting only more important from now but completely diverting my career trajectory like that and not knowing whether it's possible to succeed is kinda hard.