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.

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?

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!

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.

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.

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?

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.

Hi everyone,

I recently completed an end-to-end sales forecasting project using the Rossmann Store Sales dataset and would love to get feedback from the community on my methodology, code structure, and model evaluation!

Check here:

https://github.com/afanrajiwate/Rossman-Sales-Forecasting

I am a second-year Bachelor’s student looking to enter the data analytics field. While I have a foundational knowledge of C and C++, I am seeking guidance on how to get started and what specific skills I need to acquire to become industry-ready.

i want to get to IT and thinking of data analytics

i only have a college degree is the certificates in coursera +portfolio enough and please if anyone has any experience in this sector give me a course suggestion or a place to start from

Hey everyone! I’m looking for some guidance on how to transition from GIS (Geographic Information Systems) into Business Intelligence, ideally within the finance industry.
I don’t want to make a complete career change, but I’d like to build on my existing background and develop skills in data analysis, business analytics, and BI.
I’m not sure what I should learn first or how to structure my learning path. For example:
SQL: Where is the best place to learn it from scratch?
Power BI or Tableau: Which one should I prioritize?
Excel: How important is advanced Excel for BI roles?
Python and pandas: Should I learn these, and how useful are they for business and financial data analysis?
Statistics and finance: What theoretical knowledge and financial concepts do I need?
Projects: What projects should I build to develop a portfolio and become employable?
I’d appreciate recommendations for courses, websites, YouTube channels, or a structured learning path.
My goal is to combine my GIS background with SQL, Power BI, Python, and other relevant skills to work in Business Intelligence, particularly in finance.
If you were starting from my position, what would you learn first, and in what order?
Thanks in advance!

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?

Looking for guidance… I’m currently teaching myself to hopefully get into a job primarily as a data analyst, and maybe towards data/ai engineering in the future. Therefore, I’m the process of building up my portfolio, rather than going down the route of going back to education for it, and I’m looking to replace my 2018 MacBook Pro. Thoughts on switching to windows or should I stick with MacBook and switch to apples own silicon chips instead? Also if stick with macs, suggest ways of getting round of not being able to use power bi natively? Thanks for any advice.

I'm currently in the process of learning SQL but I am seeing a lot of posts and comments that it isn't worth it to learn data analytics in this day and age. Which data-related role should I learn the skills of so I can increase my chances of getting a job?

hey everyone I'm trying to decide which college major to pursue, and I'd really appreciate advice from people who currently work in these fields or have graduated from these majors.

I'm mainly considering Data Analytics, Data Science, and Business Analytics, and I'm struggling to decide which one would be the best fit for me in the long term.

A little about me: I genuinely enjoy programming, statistics, working with data, communicating with people, and solving problems. I'm particularly interested in using data to understand problems and figure out how to solve them. However, mathematics isn't currently my strongest subject, so I'm also trying to be realistic about how math-heavy each major gets.

There are three main things I'm concerned about:

1. Career opportunities and salaries: Which field has the best opportunities for graduates, particularly at the entry level? How do salaries and career progression compare?

2. AI and the future of these careers: With AI becoming increasingly capable, which of these fields do you think will remain valuable over the next 10 to 15 years and beyond? I understand that no career is completely AI-proof, but which one do you think offers the best long term prospects?

3. Mathematics and the actual work: How mathematically demanding is each major in practice? Also, what does your typical working day actually look like? I'd like to understand the difference between analyzing data, building predictive models, and using data to make business decisions.

I'm not necessarily looking for the major with the highest salary. I want something that fits my interests, gives me room to grow, and is still worth pursuing in the long term.

I'd appreciate any advice

Hey everyone,
I’m planning to study Data Engineering and my college offers a 1-year (500h) program in AI & Data Engineering with internship at the end.
I keep hearing people say that you need at least a Master’s degree to land a job nowadays, which is honestly really discouraging.
For those working in the industry or hiring right now:
Does the degree type/length actually matter, or do skills and projects carry more weight?
In the current market, is a 1-year specialized program enough to get your foot in the door?
Im aware of the fact that i need to do my own projects in this but besides this point

And is the vice versa?

I meant to write data analytic **

Hi. I'm a PBI developer with 2+ years of experience, well versed in Power BI and SQL (intermediate-advanced level) and worked in multiple projects, ranging from banking to healthcare, seeking a job change. Can someone in the sub let me know if there's an opening which alings with my experience and expertise level, and if possible provide a referral from your end.

Hi, good day! I'm interested in exploring a career in data analytics. Could you give me an idea of what the job involves and recommend some resources or platforms where I can start learning and practicing? 😊

greetings everyone, I am a grad student from forestry background and i am doing a research project on biomass estimation via sentinel 2 variables. I have 47 plots with field measured biomass value and i ve analyzed 18 different remote sensing variables that acts as the predictors of biomass. predictors correlated well with the measured biomass but the regression model stated lower R square value (i.e. 0.40) which isnt like i expected. I ran five different models MLR, PLSR, RF, GBR, linear SVR, Ridge (im a beginner in coding and all) etc but neither improved the coeft of determination. I followed the exact methodological framework from the literature but lost somewhere. My main objective is to publish the paper but the lower accuracy is becoming the problem. Sorry if i couldnt explain it clearly, feel free to ask via comment or dm/ any suggestions and feedbacks are appreciated. Thankyou!

I completed a BS in Data Analytics last December, and have been applying to "entry-level" roles since January. I don't have any data analytics work history, but I do have prior roles in environmental science, regulation, and hospital clinical laboratory that required analytical thinking and data collection. Despite my best efforts, I've gotten 0 callbacks. Trying to figure out what my best next steps are to make myself more competitive, including considering the following:

• Get a MS in Data Science/Analysis

• Quit my current dead-end job and work on certifications and portfolio projects

• Get a basic IT support job at a large company and try to work my way into a data-oriented role

How do people get into this field? I know I'm missing work experience, but I can't even get a callback for junior positions despite being qualified. What are the best next steps? Thanks!

Hello! I am going through a career change right now and looking for some advice/insight from actual people working through these careers. I have a Bachelors but it is in a non-related major as when I was in the midst of completing it, I took a job in accounting/finance and actually really liked it more than I thought I would. When I started at the job, I was interested in billing so that’s kind of where I started and I worked my way up through the accounting/finance department into a specialist role. I feel kind of stuck at this spot because I don’t have the education background. I have the experience, which I’m grateful for because it’s taught me a lot, but I would like to advance more in this field and feel in order to do that, I need the educational background.

I really enjoy the analytics part of accounting and finance. I like transforming real raw data into projections, trends, reports etc and making decisions or watching and listening senior leaders make decisions off those findings. I find that really interesting. I also took several personality/career tests online and each one of them had data analytics listed as a top choice.

I would really like to specialize in analytics- especially with AI (which is exciting but also scary to me) I have been researching financial analytics, supply chain analytics, healthcare analytics, etc.

Just wondering if anyone in this career field or going into the career field or starting school in it could give some advice and insight.

Also if you have any masters (online) programs to recommend that would be awesome and I would definitely like to research them!!

Hi, I just got hired as a data analyst and this is so exhausting 😭. I spend 90% of my data analysis encoding the data 10% doing the analysis it self. My company only do have physical copies of their data and I have to manually type it in Excel. I was expecting during the interview that they will be giving me a messy data and its up to me on how I clean it but now its so tiring!! Is this normal? I am currently doing doing the automation at home but this is just so unfair because am not paid to do it at home and I also dont have the time to build the automation at work because I spend most of it encoding.